An efficacy driven approach for medication recommendation in type 2 diabetes treatment using data mining techniques.
We demonstrate how data mining techniques can help recommend effective medications when physicians need to control the glucose level of patients with type 2 diabetes. We first identify the factors that may affect physicians' medication decisions and then develop a patient-similarity based approach to automatically recommend medications for a patient with the specific condition so that his blood glucose level (measured by HbA1C value) can be well controlled. The approach is validated through experiments on real data sets and compared with the recommendations by following a clinical guideline.
IBM Research - China, Beijing, China.
Haifeng
H
Liu
Guotong
G
Xie
Jing
J
Mei
Weijia
W
Shen
Wen
W
Sun
Xiang
X
Li
D001786:Blood Glucose / Q000032:analysis; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000175:diagnosis / Q000188:drug therapy*; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records / Q000458:organization & administration*; D006801:Humans; D008509:Medication Systems / Q000458:organization & administration; D054539:Medication Therapy Management / Q000458:organization & administration*; D017063:Outcome Assessment, Health Care / Q000379:methods; D016896:Treatment Outcome
D001786:Blood Glucose