Surrogate-guided ensemble Latent Dirichlet Allocation

sureLDA( X, ICD, NLP, HU, filter, prior = "PheNorm", weight = "beta", nEmpty = 20, alpha = 100, beta = 100, burnin = 50, ITER = 150, phi = NULL, nCores = 1, labeled = NULL, verbose = FALSE )

X | nPatients x nFeatures matrix of EHR feature counts |
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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