A process to securely store and manage patient healthcare information. The patient provides some information that is available to selected healthcare providers thereby avoiding repeating the same information to each treating healthcare provider. Healthcare providers may also store information about the patient on the system to make that information available to other treating healthcare providers. The information is owned by the patient and is portable with the patient.

A process to securely store and manage patient healthcare information. The patient provides some information that is available to selected healthcare providers thereby avoiding repeating the same information to each treating healthcare provider. Healthcare providers may also store information about the patient on the system to make that information available to other treating healthcare providers. The information is owned by the patient and is portable with the patient.

Methods, systems and apparatus, including computer programs encoded on computer storage medium, for training a neurosymbolic data imputation system on training data inputs in a domain to impute missing data in a data input from the data domain. In one aspect a method includes, for each training data input, adding random noise to missing fields of the training data input;

generating an embedding data input for the training data input using concept embeddings from the domain; processing the noisy data input and the embedding data input through a correlation network to obtain correlation data; applying attention to the noisy training data input and the correlation data to generate a combined data input; processing, by an autoencoder, the combined data input to obtain a decoded data output; computing a difference between the decoded data output and the training data input; and updating parameters of the data imputation system using the difference.

Disclosed are techniques for building a dynamic dictionary and using the dictionary to remove phrases or words appearing in and out of context in a document. The techniques include, for example, receiving electronic health record (EHR) data, determining, using natural language processing (NLP), an instance of a personal health information (PHI) phrase in the EHR data based on a NLP system confidence metric being above a threshold, determining another instance of the PHI phrase in the EHR data that does not have the same context as the first context, removing the instances of the PHI phrase from the EHR data to produce cleaned EHR data, and taking an action based on the cleaned EHR data. The confidence metric can indicate likelihood that the PHI phrase is a PHI phrase and the metric can be based at least in part on a first context of the PHI phrase.

Disclosed are techniques for building a dynamic dictionary and using the dictionary to remove phrases or words appearing in and out of context in a document. The techniques include, for example, receiving electronic health record (EHR) data, determining, using natural language processing (NLP), an instance of a personal health information (PHI) phrase in the EHR data based on a NLP system confidence metric being above a threshold, determining another instance of the PHI phrase in the EHR data that does not have the same context as the first context, removing the instances of the PHI phrase from the EHR data to produce cleaned EHR data, and taking an action based on the cleaned EHR data. The confidence metric can indicate likelihood that the PHI phrase is a PHI phrase and the metric can be based at least in part on a first context of the PHI phrase.

A method for generating and presenting a patient readmission risk using a readmission risk analysis system, comprising: (i) receiving information about the patient, wherein the information comprises a plurality of readmission prediction features; (ii) extracting the plurality of readmission prediction features from the received information; (iii) analyzing the readmission prediction features to determine whether each of a predetermined list of readmission prediction features are present; (iv) replacing one or more identified missing readmission prediction features with a null value to generate a complete set of readmission prediction features for the patient; (v) analyzing the complete set of readmission prediction features for the patient to generate a readmission risk score; (vi) determining, using a populated lookup table of the readmission risk analysis system, an AUC score; and (vii) displaying the generated readmission risk score and the determined AUC score.

A method for generating and presenting a patient readmission risk using a readmission risk analysis system, comprising: (i) receiving information about the patient, wherein the information comprises a plurality of readmission prediction features; (ii) extracting the plurality of readmission prediction features from the received information; (iii) analyzing the readmission prediction features to determine whether each of a predetermined list of readmission prediction features are present; (iv) replacing one or more identified missing readmission prediction features with a null value to generate a complete set of readmission prediction features for the patient; (v) analyzing the complete set of readmission prediction features for the patient to generate a readmission risk score; (vi) determining, using a populated lookup table of the readmission risk analysis system, an AUC score; and (vii) displaying the generated readmission risk score and the determined AUC score.

A system for decision support includes a path unit for determining a determined path through a decision tree that leads to a determined recommendation node including a determined recommendation. The decision tree comprises condition nodes and recommendation nodes, wherein a condition node comprises a condition associated with a particular branch of the decision tree. A recommendation node comprises a recommendation associated with the one or more conditions of the one or more condition nodes on a path towards the recommendation node. The path unit is arranged for taking into account the conditions of the condition nodes along the path by applying the conditions to a set of parameters. The system comprises an explanation unit for generating an explanation of a reason for the determined recommendation based on at least one of the condition nodes on the path that leads to the recommendation node.

A system for decision support includes a path unit for determining a determined path through a decision tree that leads to a determined recommendation node including a determined recommendation. The decision tree comprises condition nodes and recommendation nodes, wherein a condition node comprises a condition associated with a particular branch of the decision tree. A recommendation node comprises a recommendation associated with the one or more conditions of the one or more condition nodes on a path towards the recommendation node. The path unit is arranged for taking into account the conditions of the condition nodes along the path by applying the conditions to a set of parameters. The system comprises an explanation unit for generating an explanation of a reason for the determined recommendation based on at least one of the condition nodes on the path that leads to the recommendation node.

A medical information processing system of an embodiment includes a processing circuit. The processing circuit acquires examination data showing medical examination results with respect to a medical treatment subject and reply data showing reply results of a medical examination by interview with respect to the medical treatment subject. The processing circuit estimates information on medical treatment of the medical treatment subject by inputting the examination data of the medical treatment subject to a first model and estimates information on medical treatment of the medical treatment subject by inputting the examination data and the reply data of the medical treatment subject to a second model. The processing circuit outputs a first estimation result of the first model and a second estimation result of the second model via an output unit.