More data almost always yields better results when it comes to the effectiveness of machine learning, and the healthcare sector is sitting on a data goldmine. As per the estimates by McKinsey, big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.
Smart Electronic Health Record
Document classification using support vector machines and optical character recognition are both essential ML-based technologies in helping advance the collection and digitization of electronic health information. Two big examples of innovations in this area are MATLAB’s ML handwriting recognition technologies and Google’s Cloud Vision API for optical character recognition. The MIT Clinical Machine Learning Group is spearheading the development of next-generation intelligent electronic health records, which will incorporate built-in ML/AI to help with things like diagnostics, clinical decisions, and personalized treatment suggestions.
Epidemic Outbreak Prediction
Based on data collected from satellites, historical information on the web, real-time social media updates, and other sources, ML and AI technologies are also being applied to monitoring and predicting epidemic outbreaks around the world. For example, malaria outbreaks have been predicted using support vector machines and artificial neural networks, taking into account data such as temperature, average monthly rainfall, the total number of positive cases, and other data points. Prediction of outbreak severity is a pressing problem especially in third-world countries, which often lack medical infrastructure, educational avenues, and access to treatments. An internet-based reporting program for monitoring emerging diseases, ProMED-mail provides outbreak reports in real-time. The organization HealthMap leverages ProMED reports and other mined media data, uses automated classification and visualization to help monitor and provides alerts for disease outbreaks in any country.