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

Nhat Le; Matthew Wiley; Antonio Loza; Vagelis Hristidis; Robert El-Kareh

doi:10.2196/16008

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

JMIR Med Inform. 2020 Jul 17;8(7):e16008. doi: 10.2196/16008.

Authors

Nhat Le^#¹, Matthew Wiley¹, Antonio Loza², Vagelis Hristidis¹, Robert El-Kareh³

Affiliations

¹ Department of Computer Science & Engineering, University of California, Riverside, Riverside, CA, United States.
² School of Medicine, University of California, Riverside, Riverside, CA, United States.
³ Department of Medicine, University of California, San Diego, San Diego, CA, United States.

^# Contributed equally.

PMID: 32706678
PMCID: PMC7395257
DOI: 10.2196/16008

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.

Keywords: consumer health information; decision support techniques; electronic health record.

©Nhat Le, Matthew Wiley, Antonio Loza, Vagelis Hristidis, Robert El-Kareh. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 17.07.2020.