Name
Abstract | ||
|---|---|---|
•We compared ILP to propositional machine learning approaches for EHR phenotyping.•Training subject selection for machine learning was automated using ICD-9 codes.•ILP out-performed propositional machine learning approaches in AUROC.•Relational learning using ILP offers a viable approach to EHR-driven phenotyping. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.jbi.2014.07.007 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Machine learning,Electronic health record,Inductive logic programming,Phenotyping,Relational machine learning | Data mining,Diagnosis code,Receiver operating characteristic,Computer science,Statistical relational learning,Implementation,Phenome,C4.5 algorithm,Artificial intelligence,Natural language processing,Inductive logic programming,Machine learning,Sign test | Journal |
Volume | Issue | ISSN |
52 | C | 1532-0464 |
Citations | PageRank | References |
13 | 0.69 | 12 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peggy Peissig | 1 | 189 | 23.83 |
| Vítor Santos Costa | 2 | 880 | 74.70 |
| David Page | 3 | 15 | 1.10 |
| Carla Rottscheit | 4 | 13 | 0.69 |
| Richard L Berg | 5 | 38 | 2.60 |
| Eneida A Mendonça | 6 | 238 | 27.16 |
| David Page | 7 | 533 | 61.12 |