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cff-version: 1.2.0
message: 'To cite package "MAP" in publications use:'
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license: GPL-3.0-only
title: 'MAP: Multimodal Automated Phenotyping'
version: 0.1.4
doi: 10.32614/CRAN.package.MAP
abstract: 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) <https://doi.org/10.1093/jamia/ocz066>.).
authors:
- family-names: Sun
  given-names: Jiehuan
  email: jiehuan.sun@gmail.com
- family-names: Liao
  given-names: Katherine
- family-names: Yu
  given-names: Sheng
- family-names: Cai
  given-names: Tianxi
repository: https://celehs.r-universe.dev
repository-code: https://github.com/celehs/MAP
commit: d5d942880afe952042f3e8fa0b21cc01ce289fa7
url: https://celehs.github.io/MAP
contact:
- family-names: Sun
  given-names: Jiehuan
  email: jiehuan.sun@gmail.com