An online platform for COVID-19 diagnostic screening using a machine learning algorithm

Abstract

OBJECTIVE: COVID-19 has brought emerging public health emergency and new challenges. It configures a complex panorama that has been requiring a set of coordinated actions and has innovation as one of its pillars. In particular, the use of digital tools plays an important role. In this context, this study presents a screening algorithm that uses a machine learning model to assess the probability of a diagnosis of COVID-19 based on clinical data. METHODS: This algorithm was made available for free on an online platform. The project was developed in three phases. First, an machine learning risk model was developed. Second, a system was developed that would allow the user to enter patient data. Finally, this platform was used in teleconsultations carried out during the pandemic period. RESULTS: The number of accesses during the period was 4,722. A total of 126 assistances were carried out from March 23, 2020, to June 16, 2020, and 107 satisfaction survey returns were received. The response rate to the questionnaires was 84.92%, and the ratings obtained regarding the satisfaction level were higher than 4.8 (on a 0-5 scale). The Net Promoter Score was 94.4. CONCLUSION: To the best of our knowledge, this is the first online application of its kind that presents a probabilistic assessment of COVID-19 using machine learning models exclusively based on the symptoms and clinical characteristics of users. The level of satisfaction was high. The integration of machine learning tools in telemedicine practice has great potential.

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