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Predicting Patient’s Diagnoses and Diagnostic Categories from Clinical-Events in EHR Data

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)

Abstract

In this paper we develop and study machine learning based models based on latent semantic indexing capable of automatically assigning diagnoses and diagnostic categories to patients based on structured clinical data in their Electronic Health record (EHR). These models can be either used for automatic coding of patient’s diagnoses from structured EHR data at the time of discharge, or for supporting dynamic diagnosis and summarization of the patient condition. We study the performance of our diagnostic models on MIMIC-III EHR data.

Keywords

Lower dimensional representation Singular value decomposition Electronic health records Machine learning ICD-9 diagnosis 
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Notes

Acknowledgement

This work was supported by NIH grant R01GM088224. The content of the paper is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of PittsburghPittsburghUSA

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