We applied a hybrid Natural Language Processing (NLP) and machine learning (ML) approach (NLP-ML) to assessment of health related quality of life (HRQOL). The approach uses text patterns extracted from HRQOL inventories and electronic medical records (EMR) as predictive features for training ML classifiers. On a cohort of 200 patients, our approach agreed with patient self-report (EQ5D) and manual audit of the EMR 65-74% of the time. In an independent cohort of 285 patients, we found no association of HRQOL (by EQ5D or NLP-ML) with quality measures of metabolic control (HbA1c, Blood Pressure, Lipids). In addition; while there was no association between patient self-report of HRQOL and cost of care, abnormalities in Usual Activities and Anxiety/Depression assessed by NLP-ML were 40-70% more likely to be associated with greater health care costs. Our method represents an efficient and scalable surrogate measure of HRQOL to predict healthcare spending in ambulatory diabetes patients.
|Original language||English (US)|
|Number of pages||9|
|Journal||AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium|
|State||Published - 2011|