Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study
| Autor: | Jianying Hu, Michal Ozery-Flato, Liat Ein-Dor, Martin S. Kohn, Hani Neuvirth, Ranit Aharonov, Naama Parush-Shear-Yashuv |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.medical_specialty
Information Systems and Management Population Machine learning computer.software_genre 01 natural sciences Machine Learning 03 medical and health sciences 0302 clinical medicine Diabetes mellitus 0103 physical sciences medicine Electronic Health Records Humans Hypoglycemic Agents Medical physics 030212 general & internal medicine education Statistical hypothesis testing 010302 applied physics education.field_of_study Computational model business.industry Flagging medicine.disease Response to treatment Computer Science Applications Diabetes Mellitus Type 2 Hba1c test Cohort Artificial intelligence business computer Information Systems |
| Zdroj: | Big Data. 4:148-159 |
| ISSN: | 2167-647X 2167-6461 |
| DOI: | 10.1089/big.2016.0017 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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