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Posted by Eric Horvitz and Munmun De Choudhury
At Microsoft Research, we’ve been exploring the use of data analysis and machine learning to gain insights about health and well-being—and to enhance the quality of health care. Our efforts in this area include research on using data stored in electronic health records to construct predictive models that can provide physicians with advance warning about patient outcomes.We’ve worked with colleagues to develop systems that can predict the likelihood that a patient will contract an infection while in the hospital or that a patient being discharged will be readmitted to the hospital within a short time. Some of these models have been deployed and are in use at hospitals throughout the world, providing demonstrated value to patients and physicians.Beyond examining data from medical health records about hospitalized patients, we have been interested in the prospects of developing new methods that can transform anonymized data about the search and communications activities of people into a large-scale sensor network for public health. As an example of directions and opportunities in this realm, we recently showed how we can detect previously unknown drug interactions via analysis of anonymized web-search logs. We identified useful signals via analysis of tens of millions of queries sent to search engines by millions of users who had consented to share their search activities with Microsoft for research purposes.