title: Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study.
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.
authors.affiliation: 1 IBM Research-Haifa, Haifa University Campus , Haifa, Israel .
authors.forename: Michal
authors.initials: M
authors.lastname: Ozery-Flato
authors.affiliation: 1 IBM Research-Haifa, Haifa University Campus , Haifa, Israel .
authors.forename: Liat
authors.initials: L
authors.lastname: Ein-Dor
authors.affiliation: 1 IBM Research-Haifa, Haifa University Campus , Haifa, Israel .
authors.forename: Naama
authors.initials: N
authors.lastname: Parush-Shear-Yashuv
authors.affiliation: 1 IBM Research-Haifa, Haifa University Campus , Haifa, Israel .
authors.forename: Ranit
authors.initials: R
authors.lastname: Aharonov
authors.affiliation: 1 IBM Research-Haifa, Haifa University Campus , Haifa, Israel .
authors.forename: Hani
authors.initials: H
authors.lastname: Neuvirth
authors.affiliation: 2 IBM Research-T.J. Watson , Yorktown Heights, New York.
authors.forename: Martin S
authors.initials: MS
authors.lastname: Kohn
authors.affiliation: 2 IBM Research-T.J. Watson , Yorktown Heights, New York.
authors.forename: Jianying
authors.initials: J
authors.lastname: Hu
mesh_terms: D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D057286:Electronic Health Records; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use*; D000069550:Machine Learning
chemical_list: D007004:Hypoglycemic Agents
keywords: association tests; contextual anomaly detection; electronic medical records; machine learning application; metric learning; patient similarity