The accuracy vs. coverage trade-off in patient-facing diagnosis models

Kannan, Anitha, Fries, Jason Alan, Kramer, Eric, Chen, Jen Jen, Shah, Nigam, Amatriain, Xavier

arXiv.org Machine Learning 

In these online tools, patients input their initial symptoms and then proceed to answer a series of questions that the system deems relevant to those symptoms. The output of these online tools is a differential diagnosis (ranked list of diseases) that helps educate patients on possible relevant health conditions. Online symptom checkers are powered by underlying diagnosis models or engines similar to those used for advising physicians in "clinical decision support tools"; the main difference in this scenario being that the resulting differential diagnosis is not directly shared with the patient, but rather used by a physician for professional evaluation. Diagnosis models must have high accuracy while covering a large space of symptoms and diseases to be useful to patients and physicians. Accuracy is critically important, as incorrect diagnoses can give patients unnecessary cause for concern.

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