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AI For Preventive Health Care: Four potential new solutions!
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Four winning teams were chosen, displaying innovativeness and above all, impact. The teams were awarded with 2500€ each.
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Prevention of Diabetic Foot Syndrome
Team FAIDEN from Leiden University presented the idea of a user-friendly, AI-driven software that can be used by a diabetic patient at home. It allows the patient to monitor and detect early signs of severe foot problems. Diabetic Foot Syndrome can have severe consequences if not detected early, and is a major cause for lower limb amputation in Europe and worldwide.
Emotional Awareness for Better Mental Health
Unfortunately, many have learned to bottle up, ignore, or push emotions away, especially emotions we have learned are “bad”. Emotional avoidance is a cause for distress, and can lead to severe mental health conditions. An app being developed by team Soil from Healthy Mind Tech in Denmark enables long-term self assessment and monitoring of the patient’s own emotional style, assisting also medical health professionals more precisely compared to conventional medical questionnaires.
AI Assisted Decision-Making Tool Kit for Nursing Homes
In elderly care, a decrease in funcational ability can lead to hospitalisation, with a drastic impact on the quality of life of the patient. Even small changes in eating, sleeping, weight and body mass, or everyday chores might predict early decreases in functional ability, but they are difficult to detect. The team from Attendo in Finland proposed an AI algorithm that could be used as a “gut feeling” to create guidelines and an individual prognosis in changes in functional ability.
Helsinki Health Study Score for Forecasting Healthy Aging in Midlife Adults
Early detection of risky health-related behaviors already in midlife adults could be a potential intervention to prevent health decline later in life. Huge amounts of patient-related information is collected, but it’s poorly used in preventive health care.
An efficient artificial intelligence approach is needed to solve these complex statistics and find latent time-series from individuals and prognose distinct developmental patterns in a novel way, to identify new risk groups. The Helsinki Health Study Team from University of Helsinki proposed using broad survey data in AI to build a scoring system at the individual level.
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Timely and topical ideas, of which we hope to hear more of on the DT4Regions project’s series DT Stories!
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