Dr. Andrew Hertler, Chief Medical Officer at New Century Health, and other experts describe the potential of big data and predictive analytics to improve clinical risk stratification for cancer patients. To read the full report from ASCO, please click here.
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Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This paper 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.
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