Predicting changes in systolic blood pressure using longitudinal patient records.
Where this comes from
- Record sourced from PubMed, PMID 26210360.
- Also identified by DOI 10.1016/j.jbi.2015.06.024 and PMC identifier 4990201.
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Abstract
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. 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. 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.
Medical subject headings
- Blood Pressure
- Blood Pressure Determination
- Data Interpretation, Statistical
- Data Mining
- Diagnosis, Computer-Assisted
- Electronic Health Records