Clinical Recommender System: Predicting Medical Specialty Diagnostic Choices with Neural Network Ensembles
This addresses the need for timely access to medical specialty diagnostic workups for patients, though it appears incremental as it builds on existing neural network methods.
The paper tackled the problem of predicting clinical workups for specialty referrals by proposing a data-driven model based on neural network ensembles, which achieved significantly higher accuracy compared to conventional clinical checklists.
The growing demand for key healthcare resources such as clinical expertise and facilities has motivated the emergence of artificial intelligence (AI) based decision support systems. We address the problem of predicting clinical workups for specialty referrals. As an alternative for manually-created clinical checklists, we propose a data-driven model that recommends the necessary set of diagnostic procedures based on the patients' most recent clinical record extracted from the Electronic Health Record (EHR). This has the potential to enable health systems expand timely access to initial medical specialty diagnostic workups for patients. The proposed approach is based on an ensemble of feed-forward neural networks and achieves significantly higher accuracy compared to the conventional clinical checklists.