Finding the needle by modeling the haystack: Pulmonary embolism in an emergency patient with cardiorespiratory manifestations

Abstract

Background:Diagnoses from non-specific symptoms lead to reason over a huge space of hypotheses. Bayesian Networks (BN) can systematically address the task by integrating existing causal medical knowledge and clinical, possibly incomplete, case observations. Although prior specification of a causal BN structure can increase the generality of diagnostic predictions, particular care should be paid to avoid structural inconsistencies with the data at hand. Methods:The causal structure of a BN covering 129 cardiopulmonary disorders and 235 manifestations was elicited by experts. Markov chain Monte Carlo estimation of quantitative parameters was performed on the basis of 282 cases admitted to an Emergency Department and subsequently hospitalized. BN structural features underwent a refinement process to improve parameters sensitivity to data and diagnostic precision, until changes were consistent with published evidence. Discrimination/calibration measures of diagnostic performance (based on 284 test cases) were used to assess the predictive improvement after refinement. Results:Although one third of 1,417 parameter estimates remained anchored to prior settings, the BN updated by data returned adequately calibrated diagnostic predictions on the first six most observed disorders in the test sample, with an M-index of 0.91 (95% CI 0.87, 0.95) on 20 alternative diagnoses. Conclusions:Our large-sized BN effectively combines existing causal medical knowledge with sparse information contained in clinical records. It addresses a broad-spectrum differential diagnosis and informs physicians about possibly neglected cardiopulmonary conditions. The calibration analysis oriented the refinement of initial causal assumptions without loss of generality in diagnostic predictions.