DIFFERENTIAL MEDICAL DIAGNOSIS APPARATUS ADAPTED IN ORDER TO DETERMINE AN OPTIMAL SEQUENCE OF DIAGNOSTIC TESTS FOR IDENTIFYING A PATHOLOGY BY ADOPTING DIAGNOSTIC APPROPRIATENESS CRITERIA

Abstract

A differential diagnosis apparatus is described, which is adapted for medical applications in order to determine an optimal sequence of diagnostic tests for identifying a pathology by adopting diagnostic appropriateness criteria, comprising: a first updatable database containing patients' data; a second relational database containing identification data of pathologies, symptoms, clinical signs, identification data of diagnostic tests, and data relating to the appropriateness parameters of said diagnostic tests for defining a list of diagnostic hypotheses (pathologies); means adapted to determine said optimal sequence of diagnostic tests for identifying a pathology, said means comprising an inferential computation engine, which determine for each diagnostic hypothesis (pathology), based on data contained in said first and second databases, said optimal sequence of diagnostic tests with associated indices of appropriateness and probability that a patient is suffering from that pathology.

Claims

1. Differential diagnosis apparatus adapted for medical applications in order to determine an optimal sequence of diagnostic tests for identifying a pathology by adopting diagnostic appropriateness criteria, comprising a system of processors, in turn comprising: a first updatable database containing patients' data; a second updatable relational database containing identification data of pathologies, symptoms, clinical signs, identification data of diagnostic tests, and data relating to the appropriateness parameters of said diagnostic tests for defining a list of diagnostic hypotheses (pathologies); means adapted to determine said optimal sequence of diagnostic tests for identifying a pathology, said means comprising an inferential computation engine, which determine for each diagnostic hypothesis (pathology), based on data contained in said first and second databases, said optimal sequence of diagnostic tests with associated indices of appropriateness and probability that a patient is suffering from that pathology.

2. Diagnosis apparatus according to claim 1, wherein said means adapted to determine said optimal sequence of diagnostic tests are adapted to, through said inferential computation engine: determine, starting from said data contained in said first and second databases, a pre-test probability index of the possible diagnostic hypotheses (pathologies); determine, starting from said data contained in said first and second databases and from said pre-test probability index, a post-test probability index of the possible diagnostic hypotheses (pathologies) conditional upon the execution of said optimal sequence of diagnostic tests; associate an appropriateness index with each test of said optimal sequence; associate an appropriateness index with said optimal sequence of diagnostic tests.

3. Diagnosis apparatus according to claim 2, wherein said means adapted to determine said optimal sequence of diagnostic tests are adapted to determine said pre-test probability Pre(M|{DP}) and said post-test probability Post(M|{DP}U{T}) on the basis of said inferential computation of respective pre-test and post-test probabilities of finding said pathology M conditional upon, respectively, the set of the patient's identification data contained in said first database {DP} and the association of said patient's identification data {DP} with the set of said tests {T} of said optimal sequence of diagnostic tests.

4. Diagnosis apparatus according to claim 3, wherein said means adapted to determine said optimal sequence of diagnostic tests comprise means adapted to determine said pre-test probability Pre(M|{DP}) in a manner such that: with each symptom a likelihood ratio LR is associated, which is given by:
LR=P(S|M)/P(S|˜M) i.e. by the conditional probability P(S|M) of finding the symptom (S) in a patient suffering from a pathology (M) divided by the conditional probability P(S|˜M) of finding the same symptom in a patient not suffering from said pathology (˜M); computing a global likelihood ratio LRg given by the product of the likelihood ratios LR of each pathology; computing said relative pre-test probability Pre(M|{DP}) in terms of odds first:
odds_pre_test=LRg*odds_prevalenza where odds_prevalenza=P(M)/(1−P(M)), where P(M) is the prevalence of the pathology M. from which said pre-test probability Pre(M|{DP}) is given by:
Pre(M|{DP})=odds/(1+odds).

5. Diagnosis apparatus according to claim 4, wherein said means adapted to determine said optimal sequence of diagnostic tests comprise means adapted to determine said post-test probability Post(M|{DP}U{T}) in a manner such that:
odds_post_test=LR*odds_pre_test from which said post-test probability Post(M|{DP}U{T}) is given by:
Post(M|{DP}U{T})=odds_post_test/(1+odds_post_test).

6. Diagnosis apparatus according to claim 3, wherein said means adapted to determine said optimal sequence of diagnostic tests comprise means adapted to determine said pre-test probability Pre(M|{DP}) for a succession of symptoms in an iterative or recursive manner, by using the following relation:
P.sub.i=(LR*P.sub.i-1)/(LR*P.sub.i-1+/K.sub.i-1) where K.sub.i-1=(1−P.sub.i-1) P.sub.i is the pre-test probability of the first i symptoms; where i ranges from 1 to all n symptoms, and P.sub.0 is the prevalence of the pathology; then determine said pre-test probability Pre(M|{DP})=P.sub.n

7. Diagnosis apparatus according to claim 1, wherein said means adapted to determine said optimal sequence of diagnostic tests comprise means adapted to: determine said appropriateness index IA of a single diagnostic test defined as: IA=To/(C*Te*R), where Te is the test access wait time index; C is the test cost index; R is the index of the maximum between the intrinsic risk and the relative risk of the test; To is the test tolerability index; determine said global appropriateness index of a sequence of diagnostic tests IA.sub.G=To.sub.G/(C.sub.G*Te.sub.G*R.sub.G), where To.sub.G is the minimum among all tolerability indices of all tests in the sequence, C.sub.G is the sum of the single costs of each test in the sequence, Te.sub.G is the maximum among all wait times, R.sub.G is the maximum among all risk indices of the sequence.

8. Diagnosis apparatus according to claim 1, comprising software modules implemented in storage devices readable and executable by a computer equipped with input/output devices, implemented in (physical or virtual) computation clusters, and remotely executable and implemented as Apps for mobile devices (tablets, smartphones, notebook PCs).

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Further objects and advantages of the present invention will become apparent from the following detailed description of a preferred embodiment (and variants) thereof referring to the annexed drawings, which are only supplied by way of non-limiting example, wherein:

[0066] FIG. 1 shows a functional block diagram of the medical diagnosis apparatus according to the present invention;

[0067] FIG. 2 is a diagram showing the functional interrelation among blocks of the database system of the apparatus;

[0068] FIG. 3 is a diagram showing the interaction among the interactive menus of the apparatus.

DETAILED DESCRIPTION OF SOME EMBODIMENTS OF THE INVENTION

[0069] The following will describe one example of embodiment of the apparatus according to the invention, with reference to the flow chart of FIG. 1.

[0070] At the centre of said diagram there is the block for processing the appropriate diagnostic path ePDA, which, given the system configuration cPDA, produces the output information oPDA. These blocks realize a vertical information flow PDA, which makes also use of the connection to the system database DB, divided into two further blocks: the patient data database DP and the knowledge base database BDC. The data coming from the latter are entered into the vertical information flow PDA through the inferential engine block MI. The two sections (BDC and DP) that make up the database DB may also be physically located on distinct hardware systems. The database DP may consist, for example, of the physician's private patients archive, interfaced to the diagnosis support system.

[0071] As aforesaid, the set of diagnostic tests that the physician will finally prescribe is called diagnostic path. A diagnostic test or path can be defined as appropriate for formulating a diagnosis in accordance with a general principle of cost/benefit ratio minimization, based on the determination of different appropriateness parameters, by associating a numeric value with each one of them.

[0072] Some non-limiting examples of appropriateness parameters are as follows:

[0073] The first one is undoubtedly the Effectiveness (E) of a test in confirming or excluding a specific pathology. The effectiveness of a test indicates how much the knowledge for a pathology progresses or regresses on the basis of the outcome, evaluated in terms of variation of the probability of a diagnostic hypothesis before (pre-test probability) and after (post-test probability) the execution of the test.

[0074] In order to determine if the execution of a test is certainly appropriate, it is also necessary to evaluate its economical cost. The Cost (C) parameter refers to the cost incurred by the National Sanitary System, as defined by the National Health Care Range of Fees.

[0075] Also the time (Te) required for accessing the test is a parameter useful for defining the appropriateness of a test, in that the test will clearly have to be executed as quickly as possible depending on the severity of the suspected disease. The wait time data are those defined by the National Waiting-List Plan.

[0076] Other aspects that characterize test appropriateness are patient safety and tranquillity.

[0077] The Risk (R) parameter takes into account the Intrinsic risk of the procedure itself and the Relative risk dependent on the patient's pathological condition.

[0078] Patient tolerability is important to ensure that the prescription will be followed and the path will actually be completed. The Tolerability (To) parameter is defined in a subjective way by means of, for example, a questionnaire to be filled by the patient.

[0079] All appropriateness parameters can be suitably associated with numerical values.

[0080] The Database

[0081] The information contained in the database DB, in the form of record tables interconnected by a network of relations is essentially of two kinds, as generally indicated in FIG. 1, and as will be described in detail below with reference to FIG. 2.

[0082] 1. BDC; an articulated set of preloaded data structures constituting the “knowledge base”, on which all logical choices and processing steps of the application are based;

[0083] 2. DP; specific data managed by physician users, pertaining to signs, symptoms and anamneses, entered during medical examinations (Patient data). DP also contains the data relating to the processing results, in terms of diagnostic hypotheses and tests to be carried out (i.e. the data produced by the vertical processing flow of the appropriate diagnostic path PDA).

[0084] Knowledge Base BDC of the Application.

[0085] The knowledge base is structured as a set of variously interconnected tables with preloaded and updateable contents, as shown in FIG. 2.

[0086] The Disease table plays a central role in the “knowledge base” of the application: it identifies the diseases known to the application as univocal combinations of categories and sub-categories according to the ICD10 classification. In fact, the tables connected thereto, i.e. CtgMltTmt_ICD10 (ICD10 disease and traumatism categories) and SubctgMltTmt_ICD10 (ICD10 disease and traumatism sub-categories), contain the corresponding lists of the tenth revision of the international classification of diseases and related health problems as proposed by OMS (ICD10).

[0087] The Prevalence table, which shows the probability value for each disease, normalized by sex, ethnic group and age range of the patient, is connected to the Disease table.

[0088] The Symptom, Sign and Test tables contain, respectively, the lists of all observable symptoms and signs and the list of all executable medical tests (instrumental tests). These tables allow the application to interpret the information entered by the physician during the examinations, allowing the selection among the various options available.

[0089] The Test table also indicates, in addition to the test name and description, whether it is a dichotomous test (positive/negative—range: Dichotomous) or a continuous-range test (in which case it also states the unit of measure in use—range: Unit).

[0090] This is of fundamental importance for the computation algorithm, in that the chosen approach is to logically treat and reduce each continuous-range test to a series of “virtual” dichotomous tests by dividing the variability range of the possible results into an adequate number of sub-ranges (according to this approach, every value resulting from the execution of the test will fall within one of the possible sub-ranges, thus causing the corresponding dichotomous sub-test to be positive).

[0091] This table also comprises the numerical values of some appropriateness parameters associated with the test: cost, wait time, and intrinsic risk.

[0092] The Disease_Symptom table expresses the many-to-many relationship existing between the Symptom table and the Disease table (each disease may have multiple symptoms—or none—and each symptom may be common to multiple diseases), and includes, as attributes, the probability of the presence of the symptom in subjects suffering from the corresponding disease and the probability of the absence of the symptom in subjects not suffering from the corresponding disease.

[0093] Like the previous one, the Disease_Sign table expresses the many-to-many relationship between the Sign table and the Disease table.

[0094] Likewise, the Disease_Test table expresses the many-to-many relationship existing between the Test table (instrumental tests) and the Disease table (each disease may be found by one or more combined or alternative tests—or none—and each test may be useful for finding multiple diseases). In this case, the attributes are the sensitivity and specificity of the test with respect to a specific disease.

[0095] The RiskFactor contains a list of risk factors of various types that may predispose to or cause diseases.

[0096] A many-to-many relationships binds the risk factors to the Disease table, said relationship being implemented in the Disease_RiskFactor table.

[0097] The Profession and EthnicGroup tables contain a list of, respectively, known professions and ethnic groups which are relevant from a medical viewpoint. Both are used (via 1-to-many relationships) by the Patient table. The EthnicGroup table is also related to the Prevalence table.

[0098] Tables and Structures for the Specific Patient Data DP.

[0099] The specific patient data include a set of variously interconnected tables with preloaded and updateable contents, as shown in FIG. 3.

[0100] The application is of course designed for use by different physicians; therefore, the Physician table contains some personal information, and all the specific data pertaining to each patient, entered over time, will be stored into the database and related to the physician's identifier.

[0101] The Patient table contains basic information about each patient.

[0102] The Patient table is connected, via a 1-to-many relationship, to the Episode table Each record in said table identifies an episode in the patient's clinical history, which begins from the first examination, during which the physician collects signs and symptoms connected to a certain disorder, and ends with the diagnosis, possibly after a number of instrumental tests. Should the patient subsequently return to the doctor's for another disorder (different from or similar to the previous one), the physician will open, by using the application, another clinical episode that will imply the creation of a new record in the Episode table, connected to the same patient.

[0103] The Episode table is related to the Episode_Sign and Episode_Symptom tables: during the examination, the physician will enter the symptoms reported by the patient. The two Episode_Sign and Episode_Symptom tables express, by populating them, two many-to-many relationships between Episode and Sign and between Episode and Symptom.

[0104] Something similar occurs in the Anamnesis table. The Anamnesis table is connected to the Patient table. The patient's case history data are entered only once and may possibly be updated over time. Also the Anamnesis table expresses a many-to-many relationships between the Patient table and the RiskFactor table. The anamnesis data are collected by listing all risk factors to which the patient is exposed.

[0105] After all said information has been entered, the application is ready for the first processing step that will compute the first diagnostic hypotheses on a probabilistic base (pre-test probability) through the use of the algorithm implemented in the inferential engine MI.

[0106] The resulting list of pre-test diagnostic hypotheses is then stored into the Diagnosis table.

[0107] Then the post-test probabilities are computed by means of the inferential engine MI, which resorts to a series of queries that cross-reference the list of pre-test diagnostic hypotheses in Diagnosis with the Disease_Test and knowledge base Test tables, obtaining for each one of the hypothesized diseases all the associated instrumental clinical tests and the respective sensitivity and specificity values and parameters for the calculation of the appropriateness level of each test.

[0108] It is thus determined which tests, among those just determined, are most useful for gaining information for the final diagnosis.

[0109] These results are stored into the Test_Diagnosis table, wherein a series of tests are associated with each diagnostic hypothesis, with their respective values of the intrinsic appropriateness parameters (cost, wait time, intrinsic risk) and post-test probabilities.

[0110] The values of additional appropriateness parameters may then be entered into the same table, such as relative risk and tolerability of the test, which will be used in the optimization step, wherein a test appropriateness index will be computed in order to give the physician some more information useful for the final choice of the tests to be prescribed.

[0111] These same records may also subsequently receive the values of the actual results of the prescribed diagnostic tests actually carried out, for future use.

[0112] Inferential Engine MI.

[0113] This is the module that implements the algorithm for computing, starting from the patient data DP and, as previously described, from the set of preloaded data structures that make up the “knowledge base” BDC, the probability values for each diagnostic hypothesis. The module also proposes, for each hypothesis, one or more diagnostic tests, and computes how much a particular test sequence will affect the likelihood of the different hypotheses, associating with each path a respective appropriateness index.

[0114] The inferential procedure may be based, for example, on any one of the algorithms cited in the scientific literature (the most common ones being those based on fuzzy logic relationships between symptoms and diseases, artificial neural networks, Bayesian networks, and support vector machines). The following detailed description will present, as one of the possible algorithms that may be implemented, an inferential engine based on a Bayesian approach known as NAIVE BAYES. More in general, the choice of the algorithm will not lead to different final diagnoses, but may affect the diagnostic path in terms of how quickly the final diagnosis will be arrived at. In other words, all algorithms, also because of the interactivity of the procedure, will lead to the same diagnosis, though they may require different times.

[0115] The algorithm selection will determine, case by case, the necessity of modifying the DB to include therein the data required for the execution of that specific algorithm.

[0116] At any rate, the DB, as described above, still contains the information necessary for implementing a NAIVE BAYES type algorithm.

[0117] Let us now consider, by way of example, the general case of a Bayesian network. Mathematically speaking, a Bayesian network is a directed acyclic graph wherein the nodes represent the variables, the arcs represent the relationships of statistical dependence between the variables and the local probability distribution of the leaf nodes with respect to the values of the parent nodes.

[0118] In general, every pathology, sign, symptom, anamnesis datum, and test is, without distinction, a statistical variable, and hence a node of our Bayesian network. With each directed arc that binds a generic node A to a node B, a conditional probability P(B|A) must be associated; the knowledge of the probability values of all arcs in the network allows the formal computation of the probability for any one node, conditional upon the probability associated with any other set of data nodes. We will thus be formally able to compute the probabilities Pre(M|{DP}) and Post(M|{DP}U{T}), i.e. the pre-test and post-test probabilities of finding the pathology M, respectively conditional upon the patient dataset {DP} and the union of {DP} and the test set {T}.

[0119] To do so, we will have to add to the previously described DB the information about the network structure (a many-to-many table) and the value of the probability functions associated with each arc.

[0120] Therefore, as aforementioned, the choice of the algorithm implies additions to the database structure. In this respect, we will mention herein another type of implementation of the inferential engine MI.

[0121] Reference was made previously to, among others, those algorithms known as support vector machines; these represent a particular case of the so-called machine learning techniques. By way of example, such techniques can be implemented as follows: the probability of a pathology is represented as a non-linear function of the patient data (signs, symptoms, tests, etc.). When the latter are known, said probability can be easily computed. The difficulty lies in knowing the values of those parameters and coefficients of that function which are not known a priori. They may be predetermined by using automatic learning procedures typical of the machine learning techniques. Once determined, the parameter and coefficient values have to be entered into a supplementary many-to-many table of the database. What has been described by way of example in the last paragraph is applicable to any algorithm. In general, all preloaded data structures making up the “knowledge base”, necessary for the MI to estimate the probability of a pathology, can be preliminarily obtained: [0122] 1. from epidemiological databanks, guidelines and scientific literature. [0123] 2. from clinical databases (e.g. electronic medical handbook). [0124] 3. from the history of appropriate diagnostic path processing steps carried out by the [0125] MI and stored in the DB.

[0126] If the data sources described at 2 and 3 above are used, well-known supervised machine learning techniques will be employed in order to “compute” those parameters which, once “preloaded” into the knowledge base, will allow the MI to process the PDA for each patient. It is also conceivable to use the same procedures for periodically updating and consolidating the knowledge base BDC.

[0127] As far as the vertical information flow PDA of FIG. 1 is concerned, it includes three successive functional steps (the user may nevertheless return to a previous step to review and change the previous settings):

[0128] 1. Appropriate diagnostic path configuration (cPDA block in FIG. 1);

[0129] 2. Pathology selection for differential diagnosis (ePDA block in FIG. 1);

[0130] 3. Diagnostic test selection (oPDA block in FIG. 1).

[0131] Appropriate Diagnostic Path Configuration (cPDA Block in FIG. 1).

[0132] During the step of Appropriate diagnostic path configuration it is possible to display, select and possibly modify the system variables, i.e. the parameters that will be used in the next steps for processing and generating the diagnostic paths. Such parameters also include those necessary for computing the appropriateness indices. During the next steps it will still be possible to return to this step in order to reconfigure, whether partially or totally, the whole apparatus.

[0133] Pathology Selection for Differential Diagnosis (ePDA Block in FIG. 1).

[0134] In this step, the apparatus connects to the patient database DP to acquire, in addition to the patient's personal data, also the anamnesis data necessary for the next processing steps.

[0135] With the patient thus selected, a list of signs and symptoms observed and/or reported during the medical examination is associated.

[0136] This information allows generating a list of most likely pathologies (LPP), processed by the inferential engine MI. Said list is joined by a list of rare diseases (LMR), which is processed by entering also pathologies not included in LPP but present in the database DB, for which, however, one of the detected signs/symptoms is highly specific (or has high sensitivity).

[0137] The total list can then be interactively reviewed and updated by the physician user.

[0138] In particular, in order to verify that all relevant symptoms have been considered for the pathologies of interest, one or more pathologies can be selected among those included in the list for further investigating the correlated symptoms and verifying the presence of additional typical symptoms not yet detected/reported.

[0139] Then a list of pathologies for which diagnostic paths are to be estimated is interactively processed; from the list of probable pathologies LPP, those pathologies are selected which have a post-signs-symptoms probability higher than a predefined and modifiable threshold (in cPDA). To LPP and to the list of rare diseases, one can add the list of manually added pathologies LPA; the total list LPS is thus given by LPS=LPP+LMR+LPA.

[0140] Therefore, at this stage it is possible to modify and expand LPP: [0141] manually: any pathology can be added at any time. [0142] by changing the threshold for the selection of the most likely pathologies. [0143] by directly editing the list: any pathology can be selected/unselected from the final list Once LPS has been consolidated, the next step can be carried out.

[0144] Diagnostic test selection (oPDA block in FIG. 1).

[0145] A set of tests (obtained by interrogating the DATABASE) corresponds to each pathology of the selected list LPS. The union of all these sets for all pathologies constitutes a TAMS (tests associated with selected diseases) set.

[0146] In general, for selecting the diagnostic tests to be executed, different modes or combinations thereof can be used:

[0147] 1. Semi-automatic interactive selection of tests with high diagnostic effect. From the TAMS set of all relevant tests, only those having a sensitivity and/or specificity higher than an effectiveness threshold, the value of which can be changed, are selected. Then the post-test probability is computed for each pathology, assuming that every single diagnostic test will be executed. This identifies those tests which, if carried out, will cause the knowledge to progress or regress. The tests with a high diagnostic effect will be those which exceed a post-test probability threshold, also modifiable by the physician.

[0148] 2. Manual test selection. For each pathology, the physician can display all the tests associated therewith and select one or more diagnostic tests at will. Thus, also a test not exceeding the effectiveness threshold can be selected. For each selected test, the system computes the post-test probability and the appropriateness.

[0149] 3. If necessary, the physician may also select any test from the DB, regardless of its relevance. The tests selected with this option will be entered into the TAMS set.

[0150] If more than one diagnostic test is chosen, the apparatus will also compute the post-test probability of each diagnostic hypothesis, assuming that all tests will be carried out, and the appropriateness of the entire diagnostic path (i.e. the global appropriateness of all selected tests), in addition to displaying the post-test probability and the appropriateness of each test.

[0151] The above-mentioned procedure associates with each test its respective appropriateness index IA, which, by way of example, can be defined as:

IA=To/(C*Te*R)

[0152] where Te is the test access wait time index; C is the test cost index; R is the index of the maximum between the intrinsic risk and the relative risk of the test; To is the test tolerability index.

[0153] A global appropriateness index (IA.sub.G) is also determined for all the selected diagnostic tests, which is also computed by using the above formula, i.e.:

IA.sub.G=To.sub.G/(C.sub.G*Te.sub.G*R.sub.G)

[0154] where: To.sub.G is the minimum among all tolerability indices of all tests in the sequence, C.sub.G is the sum of the single costs of each test in the sequence, Te.sub.G is the maximum among all wait times, R.sub.G is the maximum among all risk indices of the sequence.

[0155] In summary, the output consists of the following data, preferably shown on a display (in the oPDA block in FIG. 1) for each diagnostic hypothesis: [0156] the pre-test probability, [0157] the list of all tests sorted by appropriateness, with their respective post-test probability and appropriateness index, [0158] the global post-test probability and appropriateness index of the entire diagnostic path.

[0159] Different diagnostic paths can be constructed, even by adding and removing individual tests from the selection, and then the post-test probabilities of the pathologies and the appropriateness indices of the paths can be re-calculated.

[0160] It is therefore possible to define an alternative path (by selecting a different set of tests): the apparatus will keep in memory the previous path with its appropriateness values, so that different paths can be compared.

DETAILED DESCRIPTION OF AN EXAMPLE OF EMBODIMENT

[0161] The present invention is preferably implemented through software modules stored on storage devices readable and executable by a computer (e.g. server, desktop, workstation, notebook, tablet, smartphone, etc.) equipped with mass storage and input/output devices (see diagram of FIG. 1).

[0162] For example, the software modules are implemented on (physical or virtual) computation clusters, and can be executed remotely and implemented as Apps for mobile devices (tablets, smartphones, notebook PCs).

[0163] The implementation of the hardware is within the grasp of a man skilled in the art; therefore, no further details are necessary.

[0164] Such software modules will use the information stored in the knowledge base BDC and in the patient data archive DP; both databases have been described above in the section entitled “THE DATABASE”.

[0165] All software modules and the whole database are developed by resorting to a Ruby on Rails framework used as a RAD environment and equipped with a series of opensource (gems) additional modules made available by the community of developers that supports this environment. In most cases, in this kind of implementation solution all non-volatile data and information necessary for the operation of the application are managed by using a relational database. In this case, a MySQL database server is used, connected to the application via a local socket. The man skilled in the art will be able to implement the software part by relying on his basic knowledge and on the present description.

[0166] With reference to FIG. 3, the output device will display a sequence of interactive menus, the most important ones being three: [0167] Home menu [0168] Pathology selection for differential diagnosis menu [0169] Diagnostic test selection menu

[0170] Said menus will call the following menus and the two main procedures: [0171] CPDA (Appropriate diagnostic path configuration) [0172] Patient data [0173] Sign and symptom selection [0174] Symptom list [0175] Selection of relevant symptoms [0176] Add rare disease [0177] SIT (Interactive test selection) [0178] SMT (Manual test selection)

[0179] Procedures: [0180] LP—calculates the sorted list of the most likely pathologies [0181] OPDA—processes the output of the appropriate diagnostic path

[0182] Description of the Operation of the Individual Menus and Procedures.

[0183] (in association with each menu option, the following will provide a description of the option and the list of the activities, in pseudo code, that will be executed when the option is activated).

[0184] Home Menu

[0185] In this menu it is possible to call the sub-menus in which one can configure the system (CPDA menu), identify the patient and his/her clinical data, select signs, symptoms and anamnesis data (Patient data menu). This information allows processing a list of most likely pathologies (LPP) and displaying it in this menu. The list of rare diseases (LMR) is also associated with this list. The total list can then be reviewed and updated by using the menu options listed below.

[0186] “Ignored symptoms by pathology” option: for verifying that all relevant symptoms have been considered for the pathologies of interest.

[0187] A pathology is selected from those included in the list for further investigating the related symptoms and looking for any additional typical symptoms that have not yet been detected/reported.

[0188] If there are any, new symptoms can then be entered that were not entered during the first investigation step. The list of the most likely pathologies will be updated accordingly.

[0189] “Ignored symptoms by importance” option: for refining the differential diagnosis among the most likely hypotheses.

[0190] For the first diagnostic hypotheses in the list, the number of which is set by the NuSePa variable (e.g. 5 by default), all symptoms not reported during the first investigation step are extracted. Only the first 10 symptoms are displayed (default value of the SiPr variable) with the highest absolute LR (likelihood ratio) (maximum LR of a symptom among the most likely pathologies).

[0191] If there are any, the new symptoms not entered during the first investigation step can then be entered. The list of the most likely pathologies will be updated accordingly.

[0192] “Significant symptoms already included” option: for verifying that a pathology that is not very likely (rare disease) has not been neglected although a highly relevant symptom was detected.

[0193] Among all symptoms reported during the first investigation step, the system will display the first 10 symptoms (default value of the SiPiuP variable) having the highest LR and the pathology for which such value is highest.

[0194] The low-probability pathology can then be entered into the list of diseases to be taken into account in the subsequent menus.

[0195] Home Menu (Procedure). [0196] available options: [0197] Appropriate diagnostic path configuration [0198] opens the CPDA menu [0199] Patient data [0200] opens the Patient data menu [0201] the displayed output is as follows: [0202] IF (“the patient data and symptoms have not been entered yet”) [0203] THEN PRINT (“enter patient data, signs and symptoms”) [0204] ELSE call the LP procedure [0205] displays the LPP+LMR list [0206] turns on supplementary options in the Home menu [0207] supplementary options: [0208] Ignored symptoms by pathology [0209] when a pathology is selected from the list (by clicking on it), [0210] opens the menu Symptom list [0211] Ignored symptoms by importance [0212] opens the Selection of relevant symptoms menu [0213] Significant symptoms already included [0214] opens the Add rare disease menu [0215] Next [0216] opens the [0217] Pathology selection for differential diagnosis menu

[0218] Pathology Selection for Differential Diagnosis Menu

[0219] In this menu, the list of pathologies for which diagnostic paths need to be estimated is processed.

[0220] The three main options are:

[0221] Automatic Selection:

[0222] the selected pathologies are those which have a post-signs-symptoms probability higher than a 40% threshold (default value of the SoSePa_inf variable), but any “rare diseases” possibly selected in the previous menu will be kept in the list.

[0223] Manual selection: the physician can add any pathology at any time.

[0224] Modify list: the physician can select/unselect any pathology in the final list

[0225] Pathology selection for differential diagnosis menu (procedure). [0226] available options: [0227] Automatic selection [0228] updates the LPP by considering only the pathologies having a probability value higher than or equal to that of the SoSePa_inf variable [0229] Manual selection [0230] the physician enters a pathology that he/she considered to be relevant, the latter is entered into the LPA list (pathologies manually added by the physician) along with the respective pre-test probability value. [0231] Modify list [0232] displays the entire list of selected pathologies LPS; [0233] the pathologies can be unselected/selected [0234] by clicking on them; the selected ones are displayed in bold. [0235] Configuration [0236] opens the CPDA menu [0237] back [0238] returns to the Home menu [0239] next [0240] opens the Diagnostic test selection menu; the output displays the list of selected pathologies (LPS), consisting of the sum of the LPP list (updated with the value of the SoSePa_inf variable)+LMR+the list of pathologies manually added by the physician (LPA), hence LPS=LPP+LMR+LPA

[0241] Diagnostic Test Selection

[0242] A set of tests (obtained by interrogating the DATABASE) corresponds to each pathology of the selected list LPS. The union of all these sets constitutes the TAMS (tests associated with selected diseases) set. For the preliminary construction of the TAMS set, not all pathologies of LPS are taken into account, since those tests are excluded which are associated with pathologies having a pre-test probability already higher than 70% (default value of the SoSePa_sup variable). This choice is due to the fact that the pathologies with high pre-test probability already provide a strong diagnostic indication without requiring other tests that would add less information than obtainable with tests more specific for the pathologies within the diagnostic uncertainty interval (defined by the SoSePa_sup and SoSePa_inf variables, having respective default values of 70% and 40%).

[0243] In general, in order to select the diagnostic tests to be executed, the physician can adopt different modes or combinations thereof:

[0244] Semi-Automatic Interactive Selection of Tests with High Diagnostic Effect:

[0245] From the TAMS set of all relevant tests, only those having an LR above an effectiveness threshold (SoEf), the value of which can be set by the physician in the PDA Configuration menu (by default SoEf=2), are further selected.

[0246] The post-test probability is computed for each pathology, assuming that every single diagnostic test will be executed. This identifies those tests which, if carried out, will cause the knowledge to progress or regress. The tests with high diagnostic effect will be those exceeding a post-test probability threshold (by default 70%, and anyway lower than or equal to SoSePa_sup).

[0247] Manual Test Selection

[0248] For each pathology, the physician can display all the tests associated therewith and sorted by likelihood ratio, and select at will one or more diagnostic tests. Thus, also a test not exceeding the effectiveness threshold can be selected. For each selected test, the system computes the post-test probability and the appropriateness.

[0249] If necessary, the physician may also select any test from the Diagnostic tests list, regardless of its relevance for the diagnostic hypotheses and its effectiveness for the differential analysis. The tests selected with this option will be added to the TAMS set.

[0250] If more than one diagnostic test are chosen, the system will also compute the post-test probability, assuming that all tests will be carried out, and the appropriateness of the entire diagnostic path, in addition to displaying the post-test probability and appropriateness of each test.

[0251] The physician can thus build different diagnostic paths at will by adding and removing individual tests from the selection and by recalculating the post-test probability of the pathologies and the appropriateness of the paths.

[0252] Diagnostic Test Selection Menu (Procedure). [0253] available options: [0254] Semi-automatic interactive selection of tests with high diagnostic effect [0255] opens the SIT menu [0256] Manual test selection [0257] when a pathology is selected by clicking on it, [0258] opens the SMT menu [0259] Configuration [0260] opens the CPDA menu [0261] Back [0262] opens the Pathology selection for differential diagnosis menu [0263] Save [0264] Saves the diagnostic path table (under a name) [0265] Load [0266] Loads a previously saved diagnostic path table [0267] Print [0268] Prints the diagnostic path table and displays the output of the OPDA (Appropriate diagnostic path output) procedure. In detail, for each pathology it displays the pre-test probability and then lists, sorted by appropriateness, all tests with their post-test probability and appropriateness, and, finally, the probability and appropriateness of the entire diagnostic path.

[0269] Patient Data Menu

[0270] From this menu one can connect to the patient database DP to acquire, in addition to the patient's personal data, also the anamnesis data necessary for the next processing steps.

[0271] In this menu, a list of signs and symptoms observed and/or reported during the medical examination is associated with the selected patient.

[0272] Signs and symptoms can be selected in many ways. The simplest way is to use a search engine within the symptom DB: the user writes the reported symptom or sign in a dialog box and, based on this, the search engine will provide a list of signs and symptoms similar to the one just entered, from which the user will be allowed to select the one that he/she considers as most appropriate to describe the patient's situation.

[0273] At the end of the selection, the user will have a list of signs and symptoms that, together with the patient's anamnesis information (already in the database), will allow returning to the Home menu and processing the list of the most likely pathologies.

[0274] Patient Data Menu (Procedure). [0275] available options: [0276] Patient selection [0277] displays the patient's personal information [0278] Sign and symptom selection [0279] signs and symptoms are entered by selecting them in the DB [0280] Back [0281] returns to the Home menu [0282] the output displays: [0283] the patient's personal information (once selected) [0284] the list of selected signs and symptoms observed in the patient

[0285] Symptom List Menu

[0286] This menu displays all signs and symptoms associated with a predefined pathology.

[0287] The number of signs and symptoms displayed can be at most equal to the NuMaxSint variable (20 by default); among these, those already observed by the physician (i.e. already selected) are highlighted in bold. It is thus possible to

[0288] verify that all relevant symptoms have been considered for the pathology of interest, and

[0289] verify the presence of any further typical symptoms not yet detected/reported.

[0290] If there are any, the physician can then enter those new symptoms that were not entered during the first investigation step. The list of the most likely pathologies will be updated accordingly upon returning to the Home menu.

[0291] Symptom List Menu (Procedure). [0292] available options: [0293] Configuration [0294] opens the CPDA menu [0295] Back [0296] returns to the Home menu [0297] the output displays the list of 20 (default value of the NuMaxSint variable) signs and symptoms for that pathology [0298] The selected signs and symptoms are displayed in bold. [0299] Others can be selected (or unselected) by clicking on them.

[0300] Selection of Relevant Symptoms Menu

[0301] This option allows detecting important signs and symptoms that were previously ignored, in order to refine the differential diagnosis among the most likely hypotheses.

[0302] From the first 5 (default value of the NuSePa variable) diagnostic hypotheses in the list, all symptoms not reported during the first investigation step are extracted. Only the first 5 symptoms are displayed (default value of SiPr) with the highest absolute LR (likelihood ratio) (maximum LR of a symptom among the most likely pathologies).

[0303] If there are any, the physician can then enter those new symptoms that were not entered during the first investigation step. The list of the most likely pathologies will be updated accordingly upon returning to the Home menu.

[0304] Selection of Relevant Symptoms Menu (Procedure). [0305] available options: [0306] Configuration [0307] opens the CPDA menu [0308] Back [0309] returns to the Home menu [0310] the output displays a list that may contain at most NuSePa*SiPr signs and symptoms. [0311] The selected signs and symptoms are displayed in bold. [0312] Others can be selected (or unselected) by clicking on them.

[0313] Add Rare Disease Menu

[0314] This option allows verifying that a pathology that is not very likely (rare disease) has not been neglected, although a highly relevant symptom was detected for that disease.

[0315] Among all symptoms reported during the first investigation step, the system will display the first 10 symptoms (default value of SiPiuP) having the highest LR and the pathology for which such value is highest.

[0316] If necessary, the physician can add low-probability pathologies to the list of rare diseases already considered, and then display them in the next menu.

[0317] Add Rare Disease Menu (Procedure). [0318] available options: [0319] Configuration [0320] opens the CPDA menu [0321] Back [0322] returns to the Home menu [0323] the output displays a list of SiPiuP (10 by default) signs and symptoms with which the pathology having the highest LR is associated (via interrogation of the DB); the already selected signs and symptoms and pathology are displayed in bold. Others can be selected (or unselected) by clicking on them.

[0324] Sit Menu

[0325] Menu for semi-automatic interactive selection of tests with high diagnostic effect. To each pathology in the selected list (LPS: list of selected pathologies), a specific set of diagnostic tests corresponds.

[0326] Of all these tests, only those having a pre-test probability lower than or equal to 70% (default value of the SoSePa_sup variable) are taken into account. The tests thus selected make up the so-called TAMS set (tests associated with selected diseases).

[0327] From the TAMS set containing all relevant tests, the system first selects those having, in absolute terms (i.e. for all selected pathology) an LR above an effectiveness threshold SoEf (2 by default), and then selects from the latter and displays those which, for at least one of the selected pathologies, exceed the post-test probability value of 70% (default value of the SoPPT variable, which is in any case smaller than or equal to SoSePa_sup).

[0328] The physician can then select/unselect the TAMS tests as considered appropriate.

[0329] SIT Menu (Procedure). [0330] available options: [0331] Back [0332] returns to the previous menu [0333] Configure appropriate diagnostic paths [0334] opens the CPDA menu [0335] the output displays the list of the diagnostic tests belonging to the TAMS set and having LR>SoEf and post-test probability >SoPPT. [0336] The tests can be selected/unselected by clicking on them. [0337] The selected tests are displayed in bold.

[0338] SMT Menu

[0339] The menu for manually selecting the tests for the pathology selected in the previous menu displays all relevant tests sorted by relative likelihood ratio, so that the physician can select one or more diagnostic tests at will. In some cases, even a test not exceeding the effectiveness threshold may be chosen and entered into the TAMS set.

[0340] SMT Menu (Procedure). [0341] available options: [0342] Back [0343] includes the selected tests into the TAMS set (if not already present) returns to the previous menu [0344] Configure appropriate diagnostic paths [0345] opens the CPDA menu [0346] the output displays the list of diagnostic tests. [0347] The tests can be selected/unselected by clicking on them. [0348] The selected tests are displayed in bold.

[0349] Cpda Menu

[0350] In the Appropriate diagnostic path configuration it is possible to display, and possibly modify, the values of the system variables.

[0351] CPDA Menu (Procedure). [0352] available options: [0353] NuMaxSint [0354] 20 by default; [0355] maximum number of signs and symptoms that can be selected per pathology [0356] it is used in the Symptom list menu [0357] NuPaLis [0358] 10 by default; number of pathologies in the list. [0359] maximum number of pathologies listed in the menu [0360] home from LP procedure [0361] NuSePa [0362] 5 by default; maximum number of selected pathologies. [0363] Number of most likely pathologies considered in the [0364] Selection of relevant symptoms menu [0365] SiPr [0366] 10 by default; probable symptoms. [0367] maximum number of most likely signs and symptoms ignored [0368] per pathology; [0369] it is used in the [0370] Selection of relevant symptoms menu [0371] SiPiuP [0372] 10 by default; most likely symptoms. [0373] Maximum number of pathologies for each one of which the [0374] selected signs and symptoms have the highest value of LR. [0375] It is used in the Add rare disease menu [0376] SoSePa_inf [0377] 40% by default; lower pathology selection threshold; threshold [0378] of the probability of a pathology, above which that pathology [0379] will be included in the LPP list of the pathologies automatically [0380] selected in the Pathology selection for differential diagnosis menu. [0381] This is the lower limit of the diagnostic uncertainty interval, given [0382] by the difference SoSePa_sup−SoSePa_inf. [0383] SoSePa_sup [0384] 70% by default; upper pathology selection threshold. Upper limit of [0385] the diagnostic uncertainty interval, given by the difference SoSePa_sup−SoSePa_inf. [0386] Only diagnostic tests associated with pathologies having pre-test [0387] probabilities within this interval will be taken into account in the [0388] Diagnostic test selection menu. [0389] SoEf [0390] 2 by default; effectiveness threshold [0391] value of the LR of a diagnostic test above which a diagnostic test [0392] can be selected by the physician in order to process a [0393] diagnostic path. It is used in the SIT menu [0394] SoPPT [0395] 70% by default; post-test probability threshold. [0396] Threshold beyond which a test can be included by the physician [0397] into the list of diagnostic paths. SoPPt must be <=SoSePa_sup. [0398] It is used in the SIT menu [0399] Te: test access wait time [0400] the use of this variable can be selected/unselected for computing [0401] the appropriateness [0402] C: test cost index [0403] the use of this variable can be selected/unselected for computing [0404] the appropriateness [0405] R: test risk index [0406] the use of this variable can be selected/unselected for computing [0407] the appropriateness [0408] To: test tolerability index [0409] the use of this variable can be selected/unselected for computing [0410] the appropriateness [0411] Show appropriateness values [0412] for selecting/unselecting the possibility of explicitly displaying the [0413] appropriateness values in the output of the [0414] Diagnostic test selection menu [0415] Back [0416] returns to the previous menu [0417] the output will display the updated value of each variable.

[0418] LP Procedure—Description

[0419] The LP procedure builds the list of most likely pathologies (LPP) by using a NAIVE BAYES model (e.g. as described in Wagholikar et al., op. cit.).

[0420] The procedure starts from the prevalence, expressed in terms of odds, of the pathology, i.e. from the epidemiological datum.

[0421] In statistics, the term “odds” refers to the ratio between the probability “p” of an event and the probability that such event will not occur (i.e. the probability (1−p) of the complementary event). Oddsprevalenza indicates the ratio between the probability that the patient is suffering from a pathology, based on demographic and age-related considerations (prevalence), and the probability that the patient is not suffering from that pathology (1−prevalence). The prevalence of the pathology M being defined as P(M), it follows that oddsprevalenza=P(M)/(1-P(M)).

[0422] A likelihood ratio LR is associated with each symptom, sign, executed test and anamnesis datum, which is given by:

LR=P(S|M)/P(S|˜M)

[0423] i.e. by the conditional probability of finding the symptom (S) in a diseased patient (M) divided by the conditional probability of finding the same symptom in a patient not suffering from that specific disease (˜M). The global LRg of a set of signs, symptoms, etc. is given by the product of the individual LR's.

[0424] It is now possible to estimate the probability that a patient with those symptoms, signs, etc. has the disease M prior to executing any further diagnostic tests (pre-tests); in terms of odds, this is written as:

odds_pre_test=LRg*odds_prevalenza

[0425] and this can, in general, be expressed again in terms of probability P, knowing that

P=odds/(1+odds).

[0426] The above-described formula for the calculation of odds_pre_test can be easily and quickly implemented and, as aforesaid, from the latter one can calculate the probability P_pre_test. As a slightly more complex alternative, one may directly compute the pre-test probability for a succession of n symptoms in an iterative or recursive manner. In this case, the following relation is used:

P.sub.i=(LR*P.sub.i-1)/(LR*P.sub.i-1+K.sub.i-1) where K.sub.i-1=(1−P.sub.i-1)

[0427] P.sub.i is the pre-test probability of the first i symptoms; where i ranges from 1 to all n symptoms, and P.sub.0 is the prevalence of the pathology. Of course, P_pre test=P.sub.n Although this implementation appears to be more complex, it allows the use of simple variants of the strictly Bayesian model. For example, different formulations of K.sub.i-1 can be adopted, such as the following two:

K.sub.i-1=(1−P.sub.0) or K.sub.i-1=(1−P.sub.i-1).sup.LR.

[0428] The basic idea of these latter two formulations is to reduce the pre-test probabilities obtained with the Bayes formula, which considers all system variables as independent of one another.

TABLE-US-00001 LP procedure for (each pathology of the “Diseases” list from BDC) P0 = prevalence of the pathology ODDS = P0 /(1− P0) /* prevalence expressed in terms of odds */ LRg = 1 /*global likelihood ratio for all data associated with the patient*/ for (each symptom, sign, anamnesis datum, executed test) read the corresponding LR (likelihood ratio) LRg = LRg * LR ODDSpre = LRg * ODDS PpreTest=ODDSpre/(ODDSpre+1) /* PpreTest is the pre-test probability */ stores the LPP list of each pathology with the respective PpreTest end

[0429] OPDA Procedure—Description

[0430] The procedure processes the output of the appropriate diagnostic path by using the same NAIVE BAYES model and the same formulae as shown in the LP procedure.

[0431] Let LR be the likelihood ratio associated with a generic diagnostic test; the estimated probability (expressed in terms of odds) of the pathology M, if the test (post-test) has a positive outcome, will be given by:

odds_post_test=LR*odds_pre_test

[0432] and this can then be expressed again in terms of probability P, knowing that

P=odds/(1+odds).

[0433] This probability can be evaluated for every single test and for the full test set.

[0434] The procedure also associates with each test its respective appropriateness index IA, defined as:

IA=To/(C*Te*R)

[0435] where To is test tolerability index; C is the test cost index; Te is the test access wait time index; R is the index of the maximum between the intrinsic risk and the relative risk of the test.

[0436] The higher the appropriateness, the greater the value of the IA index.

[0437] The global appropriateness index (IA.sub.G) of all the selected diagnostic tests is computed as follows:

IA.sub.G=To.sub.G/(C.sub.G*Te.sub.G*R.sub.G)

[0438] where To.sub.G is the minimum among all tolerability indices; C.sub.G is the sum of the individual costs for each test; Te.sub.G is the maximum among all wait times; R.sub.G is the maximum among all risk indices.

[0439] The physician may decide to define an alternative path (by selecting a different set of tests); the system will keep in memory the previous path with the respective appropriateness indices, so that the physician can compare different paths.

[0440] For the computation of odds_post_test, the same considerations apply as those made while commenting on the LP procedure for the computation of the probability P_pre_test in regard to the statistical independence of the variables associated with signs, symptoms and tests. The above-described variants of the strictly Bayesian computation apply to this case as well.

TABLE-US-00002 OPDA procedure for (each pathology of the LPS list from BDC) P0 = PpreTest /* previously computed pre-test probability of the pathology */ ODDS = P0/(1− P0) LRg =1 /*global likelihood ratio for all tests*/ for (each test belonging to the TAMS set ) read the corresponding LR (likelihood ratio) LRg = LRg * LR read Te /* access wait time index of the test */ read C /* cost index of the test */ read Ri /* intrinsic risk index of the test */ read Rr /* relative risk index of the test */ determine R /* as the maximum between Ri and Rr of the test */ read To /* tolerability index of the test */ compute C.sub.G as the sum of all C indices determine Te.sub.G as the maximum of the individual Te indices determine R.sub.G as the maximum of the individual R indices determine To.sub.G as the minimum of the individual To indices Appropriateness = To / (C*Te*R) /* appropriateness index of the test */ IA.sub.G= To.sub.G / (C.sub.G*Te.sub.G* R.sub.G) /* global appropriateness of all tests */ K = LR * ODDS PpostTest=K/(1+ K) /* post-test probability for each test */ ODDSpostG = LRg * ODDS /* compute the global post-test probability */ PpostTestG = ODDSpostG / (1 + ODDSpostG) for (each pathology in the LPS list from BDC) saves and displays: the name of the pathology and its respective PpreTest saves and displays, in decreasing appropriateness order, each symptom with its respective PpostTest and (optionally) the appropriateness index the global post-test probability: PpostTestG the global appropriateness of all tests (if the option is on) end

Glossary

[0441] BDC: Base Di Conoscenza (Knowledge base)

[0442] C: cost of the diagnostic test (variable)

[0443] CPDA: Configurazione Percorso Diagnostico Appropriato (Appropriate diagnostic path configuration) (menu)

[0444] DB: database

[0445] DP: dati paziente (patient data)

[0446] LMR: lista delle Malattie Rare (List of rare diseases)

[0447] LP: procedure for the computation of the probabilities of the diagnostic hypotheses (procedure)

[0448] LPA: Lista Patologie Aggiunte (List of manually added pathologies)

[0449] LPP: Lista di Patologie più Probabili (List of most likely pathologies)

[0450] LPS: Lista delle Patologie Selezionate (List of selected pathologies)

[0451] LR: Likelihood ratio

[0452] NuMaxSint Maximum number of signs and symptoms that can be selected per pathology (variable)

[0453] NuPaLis Number of pathologies in the list (variable)

[0454] NuSePa: Maximum number of selected pathologies (variable)

[0455] OPDA: Output Percorso Diagnostico Appropriato (Appropriate diagnostic path output)

[0456] OPDA: procedure for processing the PDA output (procedure)

[0457] R: Risk associated with the test (variable)

[0458] Ri: Intrinsic risk of the test (variable)

[0459] Rr: Relative risk of the test (variable)

[0460] SiPiuP: Most likely symptoms (variable)

[0461] SiPr: Probable symptoms (variable)

[0462] SIT: Interactive diagnostic test selection (menu)

[0463] SMT: Manual diagnostic test selection (menu)

[0464] SoEf: Effectiveness threshold (variable)

[0465] SoPPT: Post-test probability threshold (variable)

[0466] SoSePa_inf: Lower pathology selection threshold (variable)

[0467] SoSePa_sup: Upper pathology selection threshold (variable)

[0468] TAMS: set of tests associated with the selected diseases

[0469] Te: diagnostic test access time (variable)

[0470] To: test tolerability index (variable)

[0471] The present invention can advantageously be implemented through a computer program, which comprises coding means for implementing one or more steps of the method when said program is executed by a computer. It is therefore understood that the protection scope extends to said computer program as well as to computer-readable means that comprise a recorded message, said computer-readable means comprising program coding means for implementing one or more steps of the method when said program is executed by a computer.

[0472] The above-described example of embodiment may be subject to variations without departing from the protection scope of the present invention, including all equivalent designs known to a man skilled in the art.

[0473] The elements and features shown in the various preferred embodiments may be combined together without however departing from the protection scope of the present invention.

[0474] From the above description, those skilled in the art will be able to produce the object of the invention without introducing any further construction details.