Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study

Big Data. 2016 Sep;4(3):148-59. doi: 10.1089/big.2016.0017. Epub 2016 Aug 19.

Abstract

The availability of electronic health records creates fertile ground for developing computational models of various medical conditions. We present a new approach for detecting and analyzing patients with unexpected responses to treatment, building on machine learning and statistical methodology. Given a specific patient, we compute a statistical score for the deviation of the patient's response from responses observed in other patients having similar characteristics and medication regimens. These scores are used to define cohorts of patients showing deviant responses. Statistical tests are then applied to identify clinical features that correlate with these cohorts. We implement this methodology in a tool that is designed to assist researchers in the pharmaceutical field to uncover new features associated with reduced response to a treatment. It can also aid physicians by flagging patients who are not responding to treatment as expected and hence deserve more attention. The tool provides comprehensive visualizations of the analysis results and the supporting data, both at the cohort level and at the level of individual patients. We demonstrate the utility of our methodology and tool in a population of type II diabetic patients, treated with antidiabetic drugs, and monitored by the HbA1C test.

Keywords: association tests; contextual anomaly detection; electronic medical records; machine learning application; metric learning; patient similarity.

MeSH terms

  • Diabetes Mellitus, Type 2 / drug therapy*
  • Electronic Health Records
  • Humans
  • Hypoglycemic Agents / therapeutic use*
  • Machine Learning

Substances

  • Hypoglycemic Agents