TY - JOUR
T1 - The role of the electronic medical record in the assessment of health related quality of life.
AU - Pakhomov, Serguei V.S.
AU - Shah, Nilay D.
AU - Van Houten, Holly K.
AU - Hanson, Penny L.
AU - Smith, Steven A.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
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M3 - Article
C2 - 22195169
AN - SCOPUS:84874209193
SN - 1559-4076
VL - 2011
SP - 1080
EP - 1088
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
ER -