Machine learning and deep learning tools for the automated capture of cancer surveillance data.

The National Cancer Institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep learning models to automate the capture of information from unstructured clinical text for inclusion in cancer registries. Applications include extraction of key data elements from pathology reports, determination of whether a pathology or radiology report is related to cancer, extraction of relevant biomarker information, and identification of recurrence. With the growing complexity of cancer diagnosis and treatment, capturing essential information with purely manual methods is increasingly difficult. These new methods for applying advanced computational capabilities to automate data extraction represent an opportunity to close critical information gaps and create a nimble, flexible platform on which new information sources, such as genomics, can be added. This will ultimately provide a deeper understanding of the drivers of cancer and outcomes in the population and increase the timeliness of reporting. These advances will enable better understanding of how real-world patients are treated and the outcomes associated with those treatments in the context of our complex medical and social environment.

Toward real-time reporting of cancer incidence: methodology, pilot study, and SEER Program implementation.

BACKGROUND: A lag time between cancer case diagnosis and incidence reporting impedes the ability to monitor the impact of recent events on cancer incidence. Currently, the data submission standard is 22 months after a diagnosis year ends, and the reporting standard is 27.5 months after a diagnosis year ends. This paper presents the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program's efforts to minimize the lag and achieve "real-time" reporting, operationalized as submission within 2 months from the end of a diagnosis year. METHODS: Technology for rapidly creating a consolidated tumor case (CTC) from electronic pathology (e-path) reports is described. Statistical methods are extended to adjust for biases in incidence rates due to reporting delays for the most recent diagnosis years. RESULTS: A registry pilot study demonstrated that real-time submissions can approximate rates obtained from 22-month submissions after adjusting for reporting delays. A plan to be implemented across the SEER Program rapidly ascertains unstructured e-path reports and uses machine learning algorithms to translate the reports into the core data items that comprise a CTC for incidence reporting. Across the program, cases were submitted 2 months after the end of the calendar year. Registries with the most promising baseline values and a willingness to modify registry operations have joined a program to become certified as real-time reporting. CONCLUSION: Advances in electronic reporting, natural language processing, registry operations, and statistical methodology, energized by the SEER Program's mobilization and coordination of these efforts, will make real-time reporting an achievable goal.