Literature DB >> 26210360

Predicting changes in systolic blood pressure using longitudinal patient records.

John Wes Solomon1, Rodney D Nielsen2.   

Abstract

OBJECTIVE: This paper introduces a model that predicts future changes in systolic blood pressure (SBP) based on structured and unstructured (text-based) information from longitudinal clinical records.
METHOD: For each patient, the clinical records are sorted in chronological order and SBP measurements are extracted from them. The model predicts future changes in SBP based on the preceding clinical notes. This is accomplished using least median squares regression on salient features found using a feature selection algorithm.
RESULTS: Using the prediction model, a correlation coefficient of 0.47 is achieved on unseen test data (p<.0001). This is in contrast to a baseline correlation coefficient of 0.39.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Blood pressure; Clinical; Informatics; NLP; Prediction; SBP

Mesh:

Year:  2015        PMID: 26210360      PMCID: PMC4990201          DOI: 10.1016/j.jbi.2015.06.024

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


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