COVID-19 in differential diagnosis of online symptom assessments

• URL: http://arxiv.org/abs/2008.03323v3
• Date: Mon, 30 Nov 2020 22:13:39 GMT
• Title: COVID-19 in differential diagnosis of online symptom assessments
• Authors: Anitha Kannan, Richard Chen, Vignesh Venkataraman, Geoffrey J. Tso, Xavier Amatriain
• Abstract summary: COVID-19 pandemic has magnified an already existing trend of people looking for healthcare solutions online. Traditional symptom checkers are based on manually curated expert systems that are inflexible and hard to modify. In this paper we present an approach that combines the strengths of traditional AI expert systems and novel deep learning models.
• Score: 3.322985051936811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The COVID-19 pandemic has magnified an already existing trend of people looking for healthcare solutions online. One class of solutions are symptom checkers, which have become very popular in the context of COVID-19. Traditional symptom checkers, however, are based on manually curated expert systems that are inflexible and hard to modify, especially in a quickly changing situation like the one we are facing today. That is why all COVID-19 existing solutions are manual symptom checkers that can only estimate the probability of this disease and cannot contemplate alternative hypothesis or come up with a differential diagnosis. While machine learning offers an alternative, the lack of reliable data does not make it easy to apply to COVID-19 either. In this paper we present an approach that combines the strengths of traditional AI expert systems and novel deep learning models. In doing so we can leverage prior knowledge as well as any amount of existing data to quickly derive models that best adapt to the current state of the world and latest scientific knowledge. We use the approach to train a COVID-19 aware differential diagnosis model that can be used for medical decision support both for doctors or patients. We show that our approach is able to accurately model new incoming data about COVID-19 while still preserving accuracy on conditions that had been modeled in the past. While our approach shows evident and clear advantages for an extreme situation like the one we are currently facing, we also show that its flexibility generalizes beyond this concrete, but very important, example.

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