Name
Abstract | ||
|---|---|---|
Medical research is experiencing a paradigm shift from “one-size-fits-all” strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. We propose a statistical method to estimate the optimal individualized treatment rules (ITRs) that are tailored according to subject-specific features using electronic health records (EHR) data. Our approach merges statistical modeling and medical domain knowledge with machine learning algorithms to assist personalized medical decision making using EHR. We transform the estimation of optimal ITR into a classification problem and account for the non-experimental features of the EHR data and confounding by clinical indication. We create a broad range of feature variables that reflect both patient health status and healthcare data collection process. Using EHR data collected at Columbia University clinical data warehouse, we construct a decision tree for choosing the best second line therapy for treating type 2 diabetes patients. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICHI.2016.13 | 2016 IEEE International Conference on Healthcare Informatics (ICHI) |
Keywords | Field | DocType |
type 2 diabetes patient treatment,second line therapy,decision tree,Columbia University clinical data warehouse,healthcare data collection process,patient health status,feature variables,clinical indication,classification problem,optimal ITR estimation,personalized medical decision making,machine learning algorithms,medical domain knowledge,statistical modeling,EHR,subject-specific features,optimal individualized treatment rule estimation,statistical method,precision medicine approach,one-size-fits-all strategy,medical research,electronic health record data,optimal individualized treatment rule learning | Health care,Data warehouse,Data mining,Decision tree,Data collection,Precision medicine,Domain knowledge,Information retrieval,Statistical model,Medical record,Medicine | Conference |
Volume | ISBN | Citations |
2016 | 978-1-5090-6118-1 | 1 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanjia Wang | 1 | 1 | 0.71 |
| Peng Wu | 2 | 1 | 0.71 |
| Ying Liu | 3 | 2 | 0.75 |
| Chunhua Weng | 4 | 547 | 75.69 |
| Donglin Zeng | 5 | 1 | 2.73 |