Bayesian Logical Neural Networks for Human-Centered Applications in Medicine

Medicine is characterized by its inherent ambiguity, i.e., the difficulty to identify and obtain exact outcomes from available data. Regarding this problem, electronic Health Records (EHRs) aim to avoid imprecisions in the data recording, for instance by its recording in an automatic way or by the integration of data that is both readable by humans and machines. However, the inherent biology and physiological processes introduce a constant epistemic uncertainty, which has a deep implication in the way the condition of the patients is estimated. For instance, for some patients, it is not possible to speak about an exact diagnosis, but about the "suspicion" of a disease, which reveals that the medical practice is often ambiguous. In this work, we report a novel modeling methodology combining explainable models, defined on Logic Neural Networks (LONNs), and Bayesian Networks (BN) that deliver ambiguous outcomes, for instance, medical procedures (Therapy Keys (TK)), depending on the uncertainty of observed data. If epistemic uncertainty is generated from the underlying physiology, the model delivers exact or ambiguous results depending on the individual parameters of each patient. Thus, our model does not aim to assist the customer by providing exact results but is a user-centered solution that informs the customer when a given recommendation, in this case, a therapy, is uncertain and must be carefully evaluated by the customer, implying that the final customer must be a professional who will not fully rely on automatic recommendations. This novel methodology has been tested on a database for patients with heart insufficiency.