Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology.
Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology.
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DOI：
10.1200/edbk_238891
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Bekelman, Justin E
中科院分区：
文献类型：
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作者：
Parikh, Ravi B;Gdowski, Andrew;Bekelman, Justin E
大数据和预测分析在改善风险分层方面具有巨大的潜力，特别是在肿瘤学等数据丰富的领域。本文回顾了已发表的关于应用预测分析改善肿瘤学风险分层的用例和挑战的文献。我们将肿瘤学预测分析的循证用例分为三个不同的领域：（1）人口健康管理，（2）放射组学和（3）病理学。然后，我们强调了预测分析在临床决策支持和基因组风险分层中的前景。最后，我们描述了大数据在肿瘤学未来应用中的挑战，即（1）获取全面数据和终点的困难，（2）缺乏预测工具的前瞻性验证，以及（3）观察数据集自动化偏倚的风险。如果能够克服这些挑战，临床风险分层的计算技术将在短期内改善癌症患者的临床风险分层。
Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology. We then highlight promising future use cases of predictive analytics in clinical decision support and genomic risk stratification. We conclude by describing challenges in the future applications of big data in oncology, namely (1) difficulties in acquisition of comprehensive data and endpoints, (2) the lack of prospective validation of predictive tools, and (3) the risk of automating bias in observational datasets. If such challenges can be overcome, computational techniques for clinical risk stratification will in short order improve clinical risk stratification for patients with cancer.