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Dynamic Hierarchical Classification for Patient Risk-of-Readmission
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Readmission After Hospitalization for Congestive Heart Failure Among Medicare Beneficiaries
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Rehospitalizations among Patients in the Medicare Fee-for-Service Program
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Big data solutions for predicting risk-of-readmission for congestive heart failure patients
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Multistage pattern recognition of signals represented in wavelet bases with reject option
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Incremental Value of Clinical Data Beyond Claims Data in Predicting 30-Day Outcomes After Heart Failure Hospitalization
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Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
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Proportion of hospital readmissions deemed avoidable: a systematic review
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Text categorization with support vector machines
inferredcites →https://doi.org/10.48550/arxiv.1306.2094openalex-snapshot
Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach
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Data mining: concepts and techniques
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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