Stephanie N Stapleton, Michael Cassara, Benjamin Roth, Christina Matulis, Clare Desmond, Ambrose H Wong, Annemarie Cardell, Tiffany Moadel, Charles Lei, Brendan W Munzer, Hillary Moss, Nur Ain Nadir
The MIDAS touch: Frameworks for procedural model innovation and validation
AEM education and training
era extensive data collection growing need large scale data analysis tools handle many variables one modeling framework article present recent applications bayesian network modeling pathology informatics bayesian networks bns probabilistic graphical models represent domain knowledge allow investigators process knowledge following sound rules probability theory bns built based expert opinion well learned accumulating data sets bn modeling recognized suitable approach knowledge representation reasoning uncertainty last two decades bn successfully applied many studies medical prognosis diagnosis based data expert knowledge constructed several bn models assess patient risk subsequent specific histopathologic diagnoses related prognosis gynecological cytopathology breast pathology models include pittsburgh cervical cancer screening model assessing risk histopathologic diagnoses cervical precancer cervical cancer modeling significance benignappearing endometrial cells pap tests diagnostic modeling determine whether adenocarcinoma tissue specimens endometrial endocervical origin models assess risk recurrence invasive breast carcinoma ductal carcinoma situ bayesian network models used powerful flexible risk assessment tools large clinical datasets quantitatively identify variables greatest significance predicting specific histopathologic diagnoses related prognosis resulting bn models able provide individualized quantitative risk assessments prognostication specific abnormal findings commonly reported gynecological cytopathology breast pathology