Better Screening Using Big Data.
Venue: Journal of oncology practice

The American Cancer Society has estimated that 53,070 Americans will be diagnosed with pancreatic cancer in 2016, and amajority of these patients will die as a result of their disease. Most patients with pancreatic cancer present at a late stage because of the timing of the onset of symptoms; therapies effective at treating advanced disease are limited, and consequentially, the average survival remains quite poor. At present, there is no evidence based asymptomatic screening for pancreatic cancer. The utility of any tool to screen for a cancer is dependent on many factors: identification of patients at risk, which contributes to the pretest probability a cancer will occur; sensitivity and specificity of a screening test; timing and frequency of use of a screening tool; and ability to alter the outcome of disease or death when a disease is identified early. Receiver operating characteristic curves are often used to aggregate some of these factors in optimizing implementation of a screening tool. Pancreatic cancer screening posesmany challenges. First of all, it is a relatively rare cancer. Second, although some patients can be identified in high risk cohorts, most patients are not. There is no standard screening tool that has been shown to be effective. Lastly, because the cancer so frequently presents at a late stage, it is unclear if early diagnosis would prevent the outcome of death. These are all important considerations. Paparrizos et al used statistical modeling with time stamped search strings to identify patients with symptoms of pancreatic cancer as well as search strings associated with a diagnosis of pancreatic cancer to learn the association between the symptom search string and the diagnosis search string.Although thismethod is far from being ready for implementation, and there are operational challenges in how these data are used today, this modality of using publicly available data to predict disease and enhance disease management in population health is timely in our era of big data and offers tremendous potential. The use of publicly available data for statistical prediction can enhance our abilities in modern medicine with machine learning, by helping us understand the relationships between the determinants that can cause or predict an event and the outcome itself. Bymapping the relationships of events or their representatives and constructing models to understand the weights and interactions of the relationships, the uncertainty of an outcome can be reduced. There are examples throughout medicine of using statistical prediction to help us prognosticateanoutcome,plananinterventionas necessary, and sometimes even predict a response to that intervention. We have used traditional predictors in cancer as such tools, including stage, age, grade, and pathologic phenotype.Wehave alsoused genomic tools to this end, such as the 21 gene recurrence score and other prognostic tools in breast cancer. The use of nontraditional publically available data, as reported by Paparrizos et al, is whatmakes their work so innovative and exciting. We can learn to use what already exists. We do this today in modeling risks and planning interventions in other diseases, but much of this work is new. Hospital readmission can be predicted by using electronic health record and other dataonhigh risk individuals,andstrategies