Name
Abstract | ||
|---|---|---|
In the era of EHRs, it is possible to examine the outcomes of decisions made by doctors during clinical practice to identify patterns of care---generating evidence based on the collective practice of experts. We will discuss methods that use unstructured patient data to monitor for adverse drug events, profile specific drugs, identify off-label drug usage, uncover 'natural experiments' and generate practice-based evidence for difficult-to-test clinical hypotheses. We will describe how to detect associations among drugs and their adverse events several years before an alert is issued as well as compute the true rate of drug-drug interactions. We will present approaches to identify novel off-label uses of drugs using the patient feature matrix along with prior knowledge about drugs, diseases, and known usage. We will review a natural experiment--where a subset of congestive heart failure patients who were prescribed Cilostazol despite its black box warning--and profile its safety. We will discuss the testing of a clinical hypothesis about an association between allergic conditions and chronic uveitis in patients with juvenile idiopathic arthritis. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2623330.2630822 | KDD |
Keywords | Field | DocType |
health,practice-based evidence,unstructured ehr,learning health system,clinical data warehouse | Data mining,Arthritis,Computer science,Clinical Practice,Adverse effect,Cilostazol,Feature matrix,Chronic uveitis,Drug,Evidence-based practice | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nigam Shah | 1 | 212 | 20.11 |