AUTOMATED DETECTION AND RECOGNITION OF ADVERSE EVENTS

Abstract

A system, a method and a computer program product for automated detection and recognition of adverse events for monitoring the state of health of an individual

Claims

1. A method for automatically detecting, capturing and processing adverse events as part of a clinical study, a noninterventional study or a therapy, the method comprising: capturing, using one or more sensors, measurement values of one or more physiological parameters in a person who is being subjected to a medical treatment as part of the study or the therapy; analyzing the measurement values and identifying deviations from defined target values in the measurement values within an observation period; ascertaining further personal data and/or environmental data; classifying the observation period on the basis of the deviations from defined target values and the personal data and/or environmental data into one of: class A: the deviations from defined target values are not a result of the medical treatment, class B: the deviations from defined target values are a result of the medical treatment, class C: a clear statement about the cause of the deviations from defined target values cannot be made; and transmitting a message about the presence of an adverse event to a computer system for capturing adverse events in response to classifying the observation period into class A or class B, or transmitting measurement values to an expert for further clarification in response to classifying the observation period into class C.

2. The method of claim 1, wherein personal data and/or environmental data which were ascertained by one or more further sensors and/or read from one or more databases are used for the classification.

3. The method claim 1, wherein the one or more sensors are configured to capture measurement values of one or more of the following physiological parameters: body weight, body temperature, heart rate, heart rhythm, blood pressure, skin conductance, tremor (frequency), electrolyte/protein concentration or composition in body fluids, activity of specific brain areas, electrical activities of cardiac muscle fibers, central venous pressure, arterial oxygen saturation, respiratory rate.

4. The method of claim 1, wherein one or more of the following personal data and/or environmental data are used for the classification: weight of the person, age of the person, sex of the person, time and/or date at which the potentially adverse event occurred, activity level, body temperature, time and quantity of an ingested drug, time and quantity of ingested foodstuffs, blood pressure values, heart rate, respiratory rate, time and severity of a fall, location data of the person, speed of the person, fatigue level of the person, stress level of the person, pain level of the person, blood sugar level of the person, bilirubin level of the person, weather data at the time of the potentially adverse event.

5. The method of claim 1, wherein the medical treatment involves administering a drug to the person and the classification serves to identify or rule out the drug as the cause of an observed adverse event.

6. The method of claim 1, wherein the computer system for capturing adverse events includes a case report form in which the adverse events occurring as part of the study or the therapy and the causal links thereof with the medical treatment are to be documented.

7. The method of claim 1, wherein the one or more physiological parameters comprises a heart rate of the person who is being subjected to medical treatment, wherein the method further comprises ascertaining measurement values relating to an activity of the person within the observation period, and wherein class A includes that the deviations from defined target values are a result of the activity of the person.

8. A system for automatically detecting, capturing and processing adverse events as part of a clinical study, a noninterventional study or a therapy, comprising: one or more sensors configured to automatically capture measurement values of one or more physiological parameters in a person who is being subjected to a medical treatment; and one or more processors configured to: analyze the measurement values, identify deviations from defined target values in the measurement values, analyze the measurement values having deviations from defined target values and further personal data and/or environmental data, and perform a classification into one of: class A: the deviations from defined target values are not a result of the medical treatment, class B: the deviations from defined target values are a result of the medical treatment, class C: a clear statement about the cause of the deviations from defined target values cannot be made; and wherein in accordance with classification into class A or class B, the one or more processors are configured to transmit a message about the presence of an adverse event to a computer system for capturing adverse events, and in accordance with classification into class C, the one or more processors are configured to to transmit measurement values to an expert for further clarification.

9. The system of claim 8, comprising a sensor device, said sensor device comprising the one or more sensors for the automatic capturing of measurement values of one or more physiological parameters in the person, wherein the sensor device is a mobile, wearable sensor configured to be permanently carried by the person over a monitoring period of at least one day and preferably of at least one week.

10. The system of claim 8, comprising a sensor device, said sensor device comprising the one or more sensors for the automatic capturing of measurement values of one or more of the following physiological parameters: body weight, body temperature, heart rate, heart rhythm, blood pressure, skin conductance, tremor (frequency), electrolyte/protein concentration or composition in body fluids, activity of specific brain areas, electrical activities of cardiac muscle fibers, central venous pressure, arterial oxygen saturation, respiratory rate.

11. The system of claim 8, wherein the one or more processors are configured to use one or more of the following personal data and/or environmental data for the classification: weight of the person, age of the person, sex of the person, time and/or date at which the potentially adverse event occurred, activity level, body temperature, time and quantity of an ingested drug, time and quantity of ingested foodstuffs, blood pressure values, heart rate, respiratory rate, time and severity of a fall, location data of the person, speed of the person, fatigue level of the person, stress level of the person, pain level of the person, blood sugar level of the person, bilirubin level of the person, and weather data at the time of the potentially adverse event.

12. The system of claim 11, wherein the system is configured to read personal data and/or environmental data from one or more databases and use the personal data and/or environmental data for the classification.

13. The system of claim 8, wherein a self-learning system, preferably an artificial neural network, forms the basis of the classification.

14. The system of claim 8, comprising a first sensor for monitoring a heart rate of the person, a second sensor for measuring an activity level of the person, an electronic case report form for documenting adverse events occurring as part of the study or therapy and causal links of the adverse events with the medical treatment, wherein the one or more processors of the system are further configured to: analyze measurement values relating to heart rate and identify deviations from defined target values in the measurement values relating to heart rate, analyze measurement values relating to the activity-level of the person, and transmit a message about the presence of an adverse event to the electronic case report form in response to classification into class A or class B, and wherein class A includes that the deviations from defined target values are a result of the activity of the person.

15. A non-transitory computer readable program comprising instructions that, when executed by the one or more processors, cause the one or more processors to: receive measurement values of one or more physiological parameters of a person who is being subjected to a medical treatment from a sensor, analyze the measurement values and identify deviations from defined target values in the measurement values within an observation period, classify the observation period on the basis of the deviations from defined target values and personal data and/or environmental data into one of: class A: the deviations from defined target values are not a result of the medical treatment, class B: the deviations from defined target values are a result of the medical treatment, class C: a clear statement about the cause of the deviations from defined target values cannot be made, and transmit a message about the presence of an adverse event to a computer system for capturing adverse events in response to classification into classes A or B, or transmit measurement values to an expert for further clarification in response to classification into class C.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0131] Exemplary embodiments will be described in the following with reference to the following drawings:

[0132] FIG. 1 shows schematically an example of the system according to some embodiments of the invention.

[0133] FIG. 2 shows schematically an example of the system according to some embodiments of the invention.

[0134] FIG. 3 shows schematically an example of the system according to some embodiments of the invention.

[0135] FIG. 4 shows schematically an example of the system according to some embodiments of the invention.

[0136] FIG. 5 shows schematically an example of the system according to some embodiments of the invention.

[0137] FIG. 6 shows schematically an example of the system according to some embodiments of the invention.

[0138] The same reference signs in FIGS. 1, 2, 3, 4, 5 and 6 represent the same components.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0139] The system according to some embodiments of the invention can have different configurations. In the exemplary embodiment shown in FIG. 1, the system comprises two separate appliances, a sensor device (10) and an evaluation device (20). The sensor device (10) comprises two sensors (12a, 12b) for capturing measurement values of physiological parameters of a person, a transmission unit (13) which can transfer measurement values to the evaluation device (20), and a control unit (11) for controlling the capturing of measurement values and the transmission of measurement values. The evaluation device (20) comprises a receiving unit (24) for receiving measurement values from the sensor unit (10), a monitoring unit (25) for analyzing the received measurement values and for identifying signs of the presence of an adverse event. The evaluation unit (20) furthermore comprises a classification unit (26) for classifying events, a transmission unit (23) for transmitting data, and a control unit (21) for controlling the receiving of measurement values via the receiving unit (24), for controlling the transmission of data by means of the transmission unit and for controlling the components of the evaluation unit (20) and the data flows and signal flows between these components. In the present example, the sensor device (10) captures measurement values of at least two physiological parameters by means of the two sensors (12a, 12b) and transmits measurement values to the evaluation device (20) via a wireless connection (e.g., via a Bluetooth connection). The evaluation device (20) receives measurement values and the monitoring unit (25) analyzes the received measurement values. If the monitoring unit (25) identifies deviations from defined target values in the measurement values, the measurement values and also further personal data and/or environmental data are supplied to the classification unit (26). The classification unit (26) carries out a classification. The control unit (21) initiates various actions depending on the result of the classification: in the case of a class A or a class B, the control unit (21) prompts the transmission of a message about the presence of an adverse event to a computer system for capturing adverse events by means of the transmission unit (23); in the case of a class C, the control unit (21) prompts the transmission of the measurement values to an expert for further clarification by means of the transmission unit (23).

[0140] FIG. 2 shows an example of the system according to some embodiments of the invention. The exemplary system shown in FIG. 2 comprises two separate devices (appliances): a sensor device (10) and an evaluation device (20). The evaluation device (20) does not comprise a monitoring unit. Instead, a monitoring unit (15) is part of the sensor device (10). Measurement values which can be captured by one of the two sensors (12a or 12b) or by both sensors (12a and 12b) can be analyzed by the monitoring unit (15) for signs of the presence of an adverse event. If such a sign is identified, the control unit (11) prompts a transmission of the measurement values by means of the transmission unit (13) to the receiving unit (24) of the evaluation device (20). The control unit (21) of the evaluation device (20) prompts a closer analysis of the transmitted measurement values and a classification of the event by means of the classification unit (26), possibly with use of further data.

[0141] FIG. 3 shows an example of the system according to some embodiments of the invention. The exemplary system shown in FIG. 3 comprises three separate devices (appliances): a first sensor device (10), a second sensor device (10) and an evaluation device (20). Each of the sensor devices (10, 10) comprises a sensor (12, 12), a control unit (11, 11) and a transmission unit (13, 13). Both sensor devices (10, 10) can be configured such that they measure values of one or more physiological parameters and transmit the measurement values to the evaluation unit (20). The evaluation unit (20) receives measurement values and forwards them to a combined monitoring and classification unit (27) for further analysis. The combined monitoring and classification unit (27) can be configured such that it detects signs of the presence of an adverse event in measurement values and classifies the event underlying the measurement values, possibly with use of further data.

[0142] FIG. 4 shows an example of the system according to some embodiments of the invention. The exemplary system shown in FIG. 4 comprises a single device (appliance): a combined sensor and evaluation device (30). The combined sensor and evaluation device (30) comprises two sensors (32a, 32b), a control unit (31), a monitoring unit (35) and a classification unit (36). The combined sensor and evaluation device (30) can be connected to one or more further computer systems (depicted by a cloud 50) via a network (depicted by the dashed line). In the case of a classification into class A or class B, the control unit (31) prompts the transmission of a message about the presence of an adverse event to a computer system via the network. In the case of a classification into class C, the control unit (31) prompts the transmission of measurement values having signs of the presence of an adverse event to an expert for further clarification via the network.

[0143] FIG. 5 shows an example of the system according to some embodiments of the invention. The exemplary system shown in FIG. 5 comprises two separate devices (appliances): a sensor device (10) and an evaluation device (20). The evaluation device (20) can be connected to a database (60) via a network (depicted by the dashed line). The classification by means of the combined monitoring and classification unit (27) can be done using further data relating to the person, who is monitored by means of the sensor (12), the further data being obtained from the database (60).

[0144] FIG. 6 shows an example of the system according to some embodiments of the invention. The exemplary system shown in FIG. 6 comprises two separate devices (appliances): a sensor device (10) and an evaluation device (20). The sensor device (10) comprises a control unit (11), a sensor for capturing measurement values, a monitoring unit (15) for identifying signs of an adverse event in the measurement values, and a transmission unit (13). The control unit can be configured such that it transmits measurement values to the evaluation device (20) via the transmission unit (13) and via a network (50) if the monitoring unit (15) has identified a sign of the presence of an adverse event in the measurement values. The evaluation device comprises a receiving unit (24), a control unit (21), a classification unit (26) and a transmission unit (24). The classification unit can use further data, which it retrieves from a database (60) via the network (50), for the classification.

[0145] For all the embodiments shown here that comprise more than one sensor, it is conceivable that only the measurement values of one of the sensors are examined for a sign of the presence of an adverse event. The measurement values of whichever is the other sensor can, for example, be used as personal data and/or environmental data for the classification.