Evidence-based pathology in its second decade: toward probabilistic cognitive computing.
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.
Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048. Electronic address: alberto.marchevsky@cshs.org.
Alberto M
AM
Marchevsky
Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048.
Ann E
AE
Walts
Department of Pathology, Virginia College of Medicine, Charlottesville, VA 22908-0214.
Mark R
MR
Wick
D019540:Area Under Curve; D002276:Carcinoid Tumor / Q000737:chemistry / Q000401:mortality / Q000473:pathology*; D049109:Cell Proliferation; D057225:Data Mining / Q000266:history; D003936:Diagnosis, Computer-Assisted / Q000266:history / Q000379:methods*; D019317:Evidence-Based Medicine / Q000266:history / Q000379:methods*; D049674:History, 21st Century; D006801:Humans; D007150:Immunohistochemistry; D053208:Kaplan-Meier Estimate; D019394:Ki-67 Antigen / Q000032:analysis; D008175:Lung Neoplasms / Q000737:chemistry / Q000401:mortality / Q000473:pathology*; D015233:Models, Statistical; D010336:Pathology / Q000266:history / Q000379:methods*; D011237:Predictive Value of Tests; D011337:Probability Learning; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors
D019394:Ki-67 Antigen
Cognitive computing; Evidence-based; IBM Watson; Pathology; Predictive analytics