Description Usage Arguments Value

Surrogate-guided ensemble Latent Dirichlet Allocation

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`X` |
nPatients x nFeatures matrix of EHR feature counts |

`ICD` |
nPatients x nPhenotypes matrix of main ICD surrogate counts |

`NLP` |
nPatients x nPhenotypes matrix of main NLP surrogate counts |

`HU` |
nPatients-dimensional vector containing the healthcare utilization feature |

`filter` |
nPatients x nPhenotypes binary matrix indicating filter-positives |

`prior` |
'PheNorm', 'MAP', or nPatients x nPhenotypes matrix of prior probabilities (defaults to PheNorm) |

`weight` |
'beta', 'uniform', or nPhenotypes x nFeatures matrix of feature weights (defaults to beta) |

`nEmpty` |
Number of 'empty' topics to include in LDA step (defaults to 10) |

`alpha` |
LDA Dirichlet hyperparameter for patient-topic distribution (defaults to 100) |

`beta` |
LDA Dirichlet hyperparameter for topic-feature distribution (defaults to 100) |

`burnin` |
number of burnin Gibbs iterations (defaults to 50) |

`ITER` |
number of subsequent iterations for inference (defaults to 150) |

`phi` |
(optional) nPhenotypes x nFeatures pre-trained topic-feature distribution matrix |

`nCores` |
(optional) Number of parallel cores to use only if phi is provided (defaults to 1) |

`labeled` |
(optional) nPatients x nPhenotypes matrix of a priori labels (set missing entries to NA) |

`verbose` |
(optional) indicating whether to output verbose progress updates |

scores nPatients x nPhenotypes matrix of weighted patient-phenotype assignment counts from LDA step

probs nPatients x nPhenotypes matrix of patient-phenotype posterior probabilities

ensemble Mean of sureLDA posterior and PheNorm/MAP prior

prior nPatients x nPhenotypes matrix of PheNorm/MAP phenotype probability estimates

phi nPhenotypes x nFeatures topic distribution matrix from LDA step

weights nPhenotypes x nFeatures matrix of topic-feature weights

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