{ "_id": "6a16c8a8acfb0bcc41d890ef", "Package": "MAP", "Type": "Package", "Title": "Multimodal Automated Phenotyping", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Thomas\", \"Charlon\", role = c(\"aut\", \"cre\"),\nemail = \"charlon@protonmail.com\",\ncomment = c(ORCID = \"0000-0001-7497-0470\")),\nperson(\"Chuan\", \"Hong\", role = \"aut\"),\nperson(\"Jiehuan\", \"Sun\", role = \"aut\"),\nperson(\"Katherine\", \"Liao\", role = \"aut\"),\nperson(\"Sheng\", \"Yu\", role = \"aut\"),\nperson(\"Tianxi\", \"Cai\", role = \"aut\"),\nperson(\"PARSE Health\", role = \"aut\", comment = \"\"),\nperson(\"CELEHS\", role = \"aut\", comment = \"\")\n)", "Description": "Electronic health records (EHR) linked with\nbiorepositories are a powerful platform for translational\nstudies. A major bottleneck exists in the ability to phenotype\npatients accurately and efficiently. Towards that end, we\ndeveloped an automated high-throughput phenotyping method\nintegrating International Classification of Diseases (ICD)\ncodes and narrative data extracted using natural language\nprocessing (NLP). Specifically, our proposed method, called MAP\n(Map Automated Phenotyping algorithm), fits an ensemble of\nlatent mixture models on aggregated ICD and NLP counts along\nwith healthcare utilization. The MAP algorithm yields a\npredicted probability of phenotype for each patient and a\nthreshold for classifying subjects with phenotype yes/no (See\nKatherine P. Liao, et al. (2019) .).", "URL": "https://github.com/celehs/MAP, https://celehs.github.io/MAP/", "BugReports": "https://github.com/celehs/MAP/issues", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "RoxygenNote": "7.3.2", "Repository": "https://celehs.r-universe.dev", "Date/Publication": "2025-01-08 21:27:10 UTC", "RemoteUrl": "https://github.com/celehs/map", "RemoteRef": "HEAD", "RemoteSha": "0361d0424cabadae38781f9f8f9d76be2e132d84", "Packaged": { "Date": "2026-05-27 10:31:18 UTC", "User": "root" }, "Author": "Thomas Charlon [aut, cre] (ORCID:\n),\nChuan Hong [aut],\nJiehuan Sun [aut],\nKatherine Liao [aut],\nSheng Yu [aut],\nTianxi Cai [aut],\nPARSE Health [aut] (),\nCELEHS [aut] ()", "Maintainer": "Thomas Charlon ", "MD5sum": "d2c78300085cf9dac04cd5162fe740a6", "_user": "celehs", "_type": "src", "_file": "MAP_1.0.0.tar.gz", "_fileid": "776c7148a098f2128d2473753a0ec55369d32845a879ed58cca1f14a257a776f", "_filesize": 166477, 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