Clinical Recommender System: Predicting Medical Specialty Diagnostic Choices with Neural Network Ensembles

The growing demand for key healthcare resources such as clinical This system can consolidate specialty consultation needs and open expertise and facilities has motivated the emergence of artificial greater access to effective care for more patients. A key scientific intelligence (AI) based decision support systems. We address the barrier to realizing this vision is the lack of clinically acceptable problem of predicting clinical workups for specialty referrals. As tools powered by robust methods for collating clinical knowledge, an alternative for manually-created clinical checklists, we propose with continuous improvement through clinical experience, crowdsourcing, a data-driven model that recommends the necessary set of diagnostic and machine learning. Existing tools include electronic procedures based on the patients' most recent clinical record consults that allow clinicians to email specialists for advice, but extracted from the Electronic Health Record (EHR). This has the their scale remains constrained by the availability of human clinical potential to enable health systems expand timely access to initial experts. Electronic order checklists (order sets) are in turn limited medical specialty diagnostic workups for patients. The proposed by the effort to maintain and adapt content to individual patient approach is based on an ensemble of feed-forward neural networks contexts [10].