MAP: Multimodal Automated Phenotyping
Electronic health records (EHR) linked with biorepositories are
a powerful platform for translational studies. A major bottleneck exists
in the ability to phenotype patients accurately and efficiently.
Towards that end, we developed an automated high-throughput
phenotyping method integrating International
Classification of Diseases (ICD) codes and narrative data extracted
using natural language processing (NLP). Specifically, our proposed method,
called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent
mixture models on aggregated ICD and NLP counts along with healthcare
utilization. The MAP algorithm yields a predicted probability of phenotype
for each patient and a threshold for classifying subjects with phenotype
yes/no (See Katherine P. Liao, et al. (2019) <doi:10.1101/587436>.).
| Version: |
0.1.3 |
| Depends: |
R (≥ 3.4.0), flexmix (≥ 2.3-14), Matrix (≥ 1.2-10) |
| Suggests: |
knitr, rmarkdown |
| Published: |
2019-04-01 |
| Author: |
Jiehuan Sun [aut, cre], Katherine P. Liao[aut], Sheng Yu [aut], Tianxi Cai [aut] |
| Maintainer: |
Jiehuan Sun <jiehuan.sun at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
MAP results |
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