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  "Title": "Automatic Phenotyping of Electronic Health Record at Visit\nResolution",
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  "Authors@R": "c(person(given = \"Thomas\",\nfamily = \"Ferte\",\nrole = c(\"aut\", \"cre\"),\nemail = \"thomas.ferte@u-bordeaux.fr\"),\nperson(given = \"Boris P.\",\nfamily = \"Hejblum\",\nrole = \"aut\",\nemail = \"boris.hejblum@u-bordeaux.fr\"))",
  "Description": "Using Electronic Health Record (EHR) is difficult because\nmost of the time the true characteristic of the patient is not\navailable. Instead we can retrieve the International\nClassification of Disease code related to the disease of\ninterest or we can count the occurrence of the Unified Medical\nLanguage System. None of them is the true phenotype which needs\nchart review to identify. However chart review is time\nconsuming and costly. 'PheVis' is an algorithm which is\nphenotyping (i.e identify a characteristic) at the visit level\nin an unsupervised fashion. It can be used for chronic or acute\ndiseases. An example of how to use 'PheVis' is available in the\nvignette. Basically there are two functions that are to be\nused: `train_phevis()` which trains the algorithm and\n`test_phevis()` which get the predicted probabilities. The\ndetailed method is described in Ferté et al. (2021)\n<doi:10.1016/j.jbi.2021.103746>.",
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