GoodLabs Studio Inc.

Published purpose

Technologies that can speedily and efficiently analyze large amounts of data in real-time are playing a critical role in helping healthcare professionals and governments predict the impact and spread of the Covid-19 virus over time. Enhanced data-sharing capabilities, particularly syndromic data between the population and health professionals, are also proving to be an indispensable tool for governments to confront the pandemic. Syndrome Anomaly Detection System (SADS) is an innovative platform that will help governments manage any health emergencies efficiently. SADS is an advanced real-time symptom collection and disease spreading analytics platform. SADS uses deep learning natural language processing (NLP) to capture a set of symptoms using conversations of patients from various sources including hospital triages, doctor’s offices, telehealth/pandemic hotlines, ambulances, online reporting systems, and social media. Other important information, such as age, gender, ethnicity, time, and location, are also anonymously collected from verbal and textual conversations. SADS then aggregates the syndromic and individual characteristics from these multiple sources and apply machine learning (ML) algorithms to classify and detect sudden increases in unusual syndromes in communities. SADS will immediately notify public health and government officials upon outbreak detection. Authorized parties can access both realtime and historical information in the analytics tools and further analysis of the data can be performed and shared with others. SADS will allow governments to manage disease outbreaks efficiently and in real-time. This platform will help governments save lives, reduce health care costs and hospitalizations.