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Stratify, Don't Personalize: Balancing Implementation Complexity and Outcome Disparity in Medical Care
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Development and Assessment of a Model for Predicting Individualized Outcomes in Patients With Oropharyngeal Cancer
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Patterns of Treatment Failure and Postrecurrence Outcomes Among Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma After Chemoradiotherapy Using Modern Radiation Techniques
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Value of routine follow‐up for patients cured of laryngeal carcinoma
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Effect of routine follow‐up after treatment for laryngeal cancer on life expectancy and mortality
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Chemotherapy and immunotherapy for recurrent and metastatic head and neck cancer: a systematic review
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Constructing optimal binary decision trees is NP-complete
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A Retrospective Analysis of the Outcome of Patients with Recurrent and/or Metastatic Squamous Cell Carcinoma of the Head and Neck Refractory to a Platinum-based Chemotherapy
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On the undecidability of probabilistic planning and related stochastic optimization problems
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Development and validation of a staging system for HPV-related oropharyngeal cancer by the International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S): a multicentre cohort study
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Quantifying factors for the success of stratified medicine
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Multi-model Markov decision processes
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Approximation Algorithms for NP-Hard Problems
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What is personalized medicine: sharpening a vague term based on a systematic literature review
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Factors influencing the implementation of clinical guidelines for health care professionals: A systematic meta-review
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From one size fits all to a tailored approach: integrating precision medicine into medical education
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An Instrumental Variable Forest Approach for Detecting Heterogeneous Treatment Effects in Observational Studies
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The Complexity of Markov Decision Processes
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Personalized Healthcare Outcome Analysis of Cardiovascular Surgical Procedures
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Strong Optimal Classification Trees
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Is Separately Modeling Subpopulations Beneficial for Sequential Decision-Making?
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Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
inferredcites →https://doi.org/10.3389/fonc.2020.00688openalex-snapshot
Follow-Up of Head and Neck Cancer Survivors: Tipping the Balance of Intensity
inferredcites →https://doi.org/10.48550/arxiv.2110.10994openalex-snapshot
Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage
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