Probabilistic Machine Learning for Healthcare.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-06-01 DOI:10.1146/annurev-biodatasci-092820-033938
Irene Y Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath
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引用次数: 33

Abstract

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.

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医疗保健领域的概率机器学习。
机器学习可以用来理解医疗数据。概率机器学习模型有助于提供医疗保健中观察数据的完整图像。在这篇综述中,我们研究了概率机器学习如何推进医疗保健。我们考虑了预测模型构建管道中的挑战,其中概率模型可能是有益的,包括校准和丢失数据。除了预测模型,我们还研究了概率机器学习模型在表型、临床用例生成模型和强化学习中的效用。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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