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Digital Phenotyping Potential Clinical Tool in Myasthenia Gravis

The investigation evaluated if symptom signatures of myasthenia gravis exacerbations could be evaluated using real-world data gathered via a smartphone-based research platform.

Passive Health Tracking Can Predict Changes in MS Patients | Apps, Fitness Tracker Collect Data to Predict Disease Outcomes

Passive Health Tracking Can Predict Changes in MS Patients | Apps, Fitness Tracker Collect Data to Predict Disease Outcomes
multiplesclerosisnewstoday.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from multiplesclerosisnewstoday.com Daily Mail and Mail on Sunday newspapers.

Thomas Insel on Science, Zip Code, and Future-Proofing Psychotherapy

Technical University of Denmark: Digital biomarkers identify bipolar disorder

Digitalisation plays a part in everything we do in our daily lives. Mountains of data collected from all our devices are a key ingredient in the digitalisation process. Health apps on our mobile phones, for example, record our unique behavioural and

The problem of algorithmic opacity, or What the heck is the algorithm doing?

Read later Summary: Opacity in AI used to be an academic problem - now it s everyone s problem. In this piece, I define the issues at stake, and how they tie into the ongoing discussion on AI ethics. (frankie s - shutterstock) Opacity in AI is a formal, academic description of what is more commonly referred to as, What the heck is the algorithm doing? It s a problem that is at the root of many ethical issues with AI. It appears as a robust classification and ranking mechanisms, such as search engines, credit card fraud detection, market segmentation, spam filters, all used in insurance or loan qualification advertising or credit. These mechanisms of classification are calculated on computational algorithms, most often machine learning algorithms.

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