1. Evidence-based pathology in its second decade: toward probabilistic cognitive computing.
- Author
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Marchevsky AM, Walts AE, and Wick MR
- Subjects
- Area Under Curve, Carcinoid Tumor chemistry, Carcinoid Tumor mortality, Cell Proliferation, Diagnosis, Computer-Assisted history, Evidence-Based Medicine history, History, 21st Century, Humans, Immunohistochemistry, Kaplan-Meier Estimate, Ki-67 Antigen analysis, Lung Neoplasms chemistry, Lung Neoplasms mortality, Models, Statistical, Pathology history, Predictive Value of Tests, Prognosis, Proportional Hazards Models, ROC Curve, Reproducibility of Results, Software, Time Factors, Carcinoid Tumor pathology, Data Mining history, Diagnosis, Computer-Assisted methods, Evidence-Based Medicine methods, Lung Neoplasms pathology, Pathology methods, Probability Learning
- Abstract
Evidence-based pathology advocates using a combination of best available data ("evidence") from the literature and personal experience for the diagnosis, estimation of prognosis, and assessment of other variables that impact individual patient care. Evidence-based pathology relies on systematic reviews of the literature, evaluation of the quality of evidence as categorized by evidence levels and statistical tools such as meta-analyses, estimates of probabilities and odds, and others. However, it is well known that previously "statistically significant" information usually does not accurately forecast the future for individual patients. There is great interest in "cognitive computing" in which "data mining" is combined with "predictive analytics" designed to forecast future events and estimate the strength of those predictions. This study demonstrates the use of IBM Watson Analytics software to evaluate and predict the prognosis of 101 patients with typical and atypical pulmonary carcinoid tumors in which Ki-67 indices have been determined. The results obtained with this system are compared with those previously reported using "routine" statistical software and the help of a professional statistician. IBM Watson Analytics interactively provides statistical results that are comparable to those obtained with routine statistical tools but much more rapidly, with considerably less effort and with interactive graphics that are intuitively easy to apply. It also enables analysis of natural language variables and yields detailed survival predictions for patient subgroups selected by the user. Potential applications of this tool and basic concepts of cognitive computing are discussed., (Copyright © 2016 Elsevier Inc. All rights reserved.)
- Published
- 2017
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