Dataset Information

Prediction of Medical Concepts in Electronic Health Records: Similar Patient Analysis.

ABSTRACT:

Background

Medicine 2.0-the adoption of Web 2.0 technologies such as social networks in health care-creates the need for apps that can find other patients with similar experiences and health conditions based on a patient's electronic health record (EHR). Concurrently, there is an increasing number of longitudinal EHR data sets with rich information, which are essential to fulfill this need.

Objective

This study aimed to evaluate the hypothesis that we can leverage similar EHRs to predict possible future medical concepts (eg, disorders) from a patient's EHR.

Methods

We represented patients' EHRs using time-based prefixes and suffixes, where each prefix or suffix is a set of medical concepts from a medical ontology. We compared the prefixes of other patients in the collection with the state of the current patient using various interpatient distance measures. The set of similar prefixes yields a set of suffixes, which we used to determine probable future concepts for the current patient's EHR.

Results

We evaluated our methods on the Multiparameter Intelligent Monitoring in Intensive Care II data set of patients, where we achieved precision up to 56.1% and recall up to 69.5%. For a limited set of clinically interesting concepts, specifically a set of procedures, we found that 86.9% (353/406) of the true-positives are clinically useful, that is, these procedures were actually performed later on the patient, and only 4.7% (19/406) of true-positives were completely irrelevant.

Conclusions

These initial results indicate that predicting patients' future medical concepts is feasible. Effectively predicting medical concepts can have several applications, such as managing resources in a hospital.

SUBMITTER: Le N

PROVIDER: S-EPMC7395257 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Prediction of Medical Concepts in Electronic Health Records: Similar Patient Analysis.

Le Nhat N Wiley Matthew M Loza Antonio A Hristidis Vagelis V El-Kareh Robert R

JMIR medical informatics 20200717 7

<h4>Background</h4>Medicine 2.0-the adoption of Web 2.0 technologies such as social networks in health care-creates the need for apps that can find other patients with similar experiences and health conditions based on a patient's electronic health record (EHR). Concurrently, there is an increasing number of longitudinal EHR data sets with rich information, which are essential to fulfill this need.<h4>Objective</h4>This study aimed to evaluate the hypothesis that we can leverage similar EHRs to ...[more]

PMID: 32706678

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Dataset Information

Prediction of Medical Concepts in Electronic Health Records: Similar Patient Analysis.

Background

Objective

Methods

Results

Conclusions

Publications

Prediction of Medical Concepts in Electronic Health Records: Similar Patient Analysis.

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