Design, Implementation, and Evaluation of the Computer-aided Clinical Decision Support System based on Learning-to-Rank: Collaboration between physicians and machine learning in the differential diagnosis process

Abstract

Background We are researching, developing, and publishing the Clinical Decision Support System based on Learning-to-Rank. The main objectives are 1) Supporting differential diagnoses by internists and general practitioners and 2) Preventing diagnostic errors by physicians. The main features are that "A physician inputs a patient's symptoms, findings, and test results to the system, and the system outputs a ranking list of possible diseases." Method The software libraries for machine learning and artificial intelligence are TensorFlow and TensorFlow Ranking. The prediction algorithm is Learning-to-Rank., with a listwise approach. The ranking metric is Normalized Discounted Cumulative Gain (NDCG). The loss functions are Approximate NDCG (A-NDCG) and Gumbel Approximate NDCG (G-A-NDCG). We evaluated the machine learning performance on k-fold cross-validation. We evaluated the differential diagnosis performance with actual cases. Results The machine learning performance of our system was much higher than that of the conventional system. The machine learning performance using G-A-NDCG was slightly higher than that of A-NDCG. The differential diagnosis performance of our system was much higher than that of the conventional system. We have shown that the Clinical Decision Support System prevents physicians' diagnostic errors due to confirmation bias. Conclusions We have demonstrated that the Clinical Decision Support System is useful for supporting differential diagnoses and preventing diagnostic errors. We believe that Differential diagnosis by physicians and Learning-to-Rank by machine has a high affinity. We found that Information Retrieval and Clinical Decision Support Systems have much in common (Target data, Learning-to-Rank, etc.). We believe that Clinical Decision Support Systems have the potential to support 1) recall of rare diseases, 2) differential diagnoses for difficult-to-diagnose diseases, and 3) prevention of diagnostic errors. We also believe that our system has the potential to evolve into an Explainable Clinical Decision Support System.