CDRH Seeks Public Comment: Digital Health Technologies for Detecting Prediabetes and Undiagnosed Type 2 Diabetes
This document provides responses to the FDA's request for public comments (Docket No FDA 2023 N 4853) on the role of digital health technologies (DHTs) in detecting prediabetes and undiagnosed type 2 diabetes. It explores current DHT applications in prevention, detection, treatment and reversal of prediabetes, highlighting AI chatbots, online forums, wearables and mobile apps. The methods employed by DHTs to capture health signals like glucose, diet, symptoms and community insights are outlined. Key subpopulations that could benefit most from remote screening tools include rural residents, minority groups, high-risk individuals and those with limited healthcare access. Capturable high-impact risk factors encompass glycemic variability, cardiovascular parameters, respiratory health, blood biomarkers and patient reported symptoms. An array of non-invasive monitoring tools are discussed, although further research into their accuracy for diverse groups is warranted. Extensive health datasets providing immense opportunities for AI and ML based risk modeling are presented. Promising techniques leveraging EHRs, imaging, wearables and surveys to enhance screening through AI and ML algorithms are showcased. Analysis of social media and streaming data further allows disease prediction across populations. Ongoing innovation focused on inclusivity and accessibility is highlighted as pivotal in unlocking DHTs potential for transforming prediabetes and diabetes prevention and care.
Code (0)
등록된 구현이 없습니다.
Tasks
Disease PredictionSimilar Papers 제목 키워드 기반
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody dominates antigen-binding specificity. There…
Bayesian OptimisationBenchmarkingSpecificityPredicting Different Types of Subtle Toxicity in Unhealthy Online Conversations
This paper investigates the use of machine learning models for the classification of unhealthy online conversations containing one or more forms of subtler abuse, such as hostility, sarcasm, and generalization. We levera…
Sentiment AnalysisDiscussion quality diffuses in the digital public square
Studies of online social influence have demonstrated that friends have important effects on many types of behavior in a wide variety of settings. However, we know much less about how influence works among relative strang…
Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
Social relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. {Violent interactions in this sphere are very frequent, and have serious effects…
Six Attributes of Unhealthy Conversation
We present a new dataset of approximately 44000 comments labeled by crowdworkers. Each comment is labelled as either 'healthy' or 'unhealthy', in addition to binary labels for the presence of six potentially 'unhealthy' …