Name
Title | ||
|---|---|---|
Temporal Phenotyping of Medically Complex Children via PARAFAC2 Tensor Factorization. |
Abstract | ||
|---|---|---|
•Raw electronic health records are often too complex to provide an intuitive understanding of patient phenotypes and their evolution.•To avoid the time-consuming chart review, we propose an unsupervised computational framework that extracts phenotypes and their temporal trends without precise phenotype labels.•We study a medically-complex children’s cohort and identified four phenotypes which are validated by a clinical expert and significant survival variations among different phenotypes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jbi.2019.103125 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Temporal phenotyping,Computational phenotyping,Tensor analysis | Health care,Data source,Information retrieval,Computer science,Clinical trial,Artificial intelligence,Chart,Tensor factorization,Clinical decision support system,Machine learning | Journal |
Volume | ISSN | Citations |
93 | 1532-0464 | 3 |
PageRank | References | Authors |
0.37 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioakeim Perros | 1 | 62 | 5.13 |
| Evangelos Papalexakis | 2 | 878 | 59.71 |
| Richard Vuduc | 3 | 1343 | 100.74 |
| Elizabeth Searles | 4 | 78 | 3.75 |
| Jimeng Sun | 5 | 4729 | 240.91 |