Intelligent wearable systems are at the forefront of precision medicine and play a crucial role in enhancing human-machine interaction. Traditional devices often encounter limitations due to their dependence on empirical material design and basic signal processing techniques. To overcome these issues, we introduce the concept of Human-Symbiotic Health Intelligence (HSHI), which is a framework that integrates multi-modal sensor networks with edge-cloud collaborative computing and a hybrid approach to data and knowledge modeling. HSHI is designed to adapt dynamically to both inter-individual and intra-individual variability, transitioning health management from passive monitoring to an active collaborative evolution. The framework incorporates AI-driven optimization of materials and micro-structures, provides robust interpretation of multi-modal signals, and utilizes a dual mechanism that merges population-level insights with personalized adaptations. Moreover, the integration of closed-loop optimization through reinforcement learning and digital twins facilitates customized interventions and feedback. In general, HSHI represents a significant shift in healthcare, moving towards a model that emphasizes prevention, adaptability, and a harmonious relationship between technology and health management.

With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements a smart elderly care service model to address issues such as data diversity, health status complexity, long-term dependence and data loss, sudden changes in behavior, and data privacy in the prediction of health behaviors of the elderly. The model achieves accurate prediction and dynamic management of health behaviors of the elderly through modules such as multimodal data fusion, data loss processing, nonlinear prediction, emergency detection, and privacy protection. In the experimental design, based on multi-source data sets and market research results, the model demonstrates excellent performance in health behavior prediction, emergency detection, and personalized services. The experimental results show that the model can effectively improve the accuracy and robustness of health behavior prediction and meet the actual application needs in the field of smart elderly care. In the future, with the integration of more data and further optimization of technology, the model will provide more powerful technical support for smart elderly care services.

We present a novel approach to personalized sleep health management using few-shot Chain-of-Thought (CoT) distillation, enabling small-scale language models (> 2B parameters) to rival the performance of large language models (LLMs) in specialized health domains. Our method simultaneously distills problem-solving strategies, long-tail expert knowledge, and personalized recommendation capabilities from larger models into more efficient, compact models. Unlike existing systems, our approach offers three key functionalities: generating personalized sleep health recommendations, supporting user-specific follow-up inquiries, and providing responses to domain-specific knowledge questions. We focus on sleep health due to its measurability via wearable devices and its impact on overall well-being. Our experimental setup, involving GPT-4o for data synthesis, Qwen-max for instruction set creation, and Qwen2.5 1.5B for model distillation, demonstrates significant improvements over baseline small-scale models in penalization, reasoning, and knowledge application. Experiments using 100 simulated sleep reports and 1,000 domain-specific questions shows our model achieves comparable performance to larger models while maintaining efficiency for real-world deployment. This research not only advances AI-driven health management but also provides a novel approach to leveraging LLM capabilities in resource-constrained environments, potentially enhancing the accessibility of personalized healthcare solutions.

Artificial intelligence (AI) in healthcare has significantly advanced intelligent medical treatment. However, traditional intelligent healthcare is limited by static data and unified standards, preventing full integration with individual situations and other challenges. Hence, a more professional and detailed intelligent healthcare method is needed for development. To this end, we propose an innovative framework named Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management methods, our approach has three main advantages. First, our method integrates health reports into a large model to provide detailed task information. Second, professional medical expertise is used to adjust the weighted scores of health characteristics. Third, we use a semi-automated feature extraction framework to enhance the analytical power of language models and incorporate expert insights to improve the accuracy of disease prediction. We have conducted disease prediction experiments on a large number of health reports to assess the effectiveness of Health-LLM. The results of the experiments indicate that the proposed method surpasses traditional methods and has the potential to revolutionize disease prediction and personalized health management. The code is available at https://github.com/jmyissb/HealthLLM.

At a TED Talk back in 2010, game designer and author Jane McGonigal argued that video games would help change the world for the better. While she may not have been referring to health and wellness specifically, recent developments in reinforcement learning (RL) for health care have rapidly turned parts of McGonigal's vision into reality. RL is simply a narrower subset of ML -- "the cherry on the cake" of artificial intelligence (AI), according to Facebook VP and Chief AI Scientist Yann LeCun. The main difference is that instead of merely inspecting data, RL agents learn by interacting with their environments and earning rewards or penalties based on their actions. The agent interacts with this environment, which can change either through the agent's actions or on its own.

Online healthcare communities provide users with various healthcare interventions to promote healthy behavior and improve adherence. When faced with too many intervention choices, however, individuals may find it difficult to decide which option to take, especially when they lack the experience or knowledge to evaluate different options. The choice overload issue may negatively affect users' engagement in health management. In this study, we take a design-science perspective to propose a recommendation framework that helps users to select healthcare interventions. Taking into account that users' health behaviors can be highly dynamic and diverse, we propose a multi-armed bandit (MAB)-driven recommendation framework, which enables us to adaptively learn users' preference variations while promoting recommendation diversity in the meantime. To better adapt an MAB to the healthcare context, we synthesize two innovative model components based on prominent health theories. The first component is a deep-learning-based feature engineering procedure, which is designed to learn crucial recommendation contexts in regard to users' sequential health histories, health-management experiences, preferences, and intrinsic attributes of healthcare interventions. The second component is a diversity constraint, which structurally diversifies recommendations in different dimensions to provide users with well-rounded support. We apply our approach to an online weight management context and evaluate it rigorously through a series of experiments. Our results demonstrate that each of the design components is effective and that our recommendation design outperforms a wide range of state-of-the-art recommendation systems. Our study contributes to the research on the application of business intelligence and has implications for multiple stakeholders, including online healthcare platforms, policymakers, and users.

Easy integration with Epic, eCW, Athena or any EHR Bi-direction interface with Epic or any EHR Interface various HL7 messages Dashboard capability to view messages. HiPaaS Go Live with ML/AI Model for leading Cancer Research Hospital to reduce in-hospital mortality rate. HiPaaS used for Population Health Management by leading Medical Group. HiPaaS collects following events from various sources – 1. CCDA information about Patient History 2. Client required a scalable and highly available solution for connectivity to multiple EHRs and payers, with interface for patient management and document services (OCR). HiPaaS is used by leading Cancer Research firm to collect genomics data which is then used for Prediction scoring.

Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estimation is exceptionally beneficial to Predictive Maintenance, and Prognostics and Health Management (PHM). Data driven approaches which leverage the power of algorithms for RUL estimation using sensor and operational time series data are gaining popularity. Existing algorithms, such as linear regression, Convolutional Neural Network (CNN), Hidden Markov Models (HMMs), and Long Short-Term Memory (LSTM), have their own limitations for the RUL estimation task. In this work, we propose a novel Functional Data Analysis (FDA) method called functional Multilayer Perceptron (functional MLP) for RUL estimation. Functional MLP treats time series data from multiple equipment as a sample of random continuous processes over time. FDA explicitly incorporates both the correlations within the same equipment and the random variations across different equipment's sensor time series into the model. FDA also has the benefit of allowing the relationship between RUL and sensor variables to vary over time. We implement functional MLP on the benchmark NASA C-MAPSS data and evaluate the performance using two popularly-used metrics. Results show the superiority of our algorithm over all the other state-of-the-art methods.

Trishan Panch, MD, MPH, is co-founder and chief medical officer for digital health management provider Wellframe (www.wellframe.com). He is an MIT lecturer for Health Sciences and Technology and teaches Masters and PhD students at the Harvard School of Public Health, Harvard Medical School, and MIT. Dr Panch is also on the advisory board of Boston Children's Hospital. He has set up and run primary care organizations in the US, UK, India, and Sri Lanka, providing comprehensive adult, pediatric, obstetric, and mental health care for complex populations in a mixture of urban and rural settings. I vividly remember the scene in the back office of my practice: clinical notes to the left of me, unsigned prescriptions to the right, and I was stuck in the middle looking at new quality metrics for our patient population.

In spite of noteworthy headway in innovation in various fields, health management and authoritative frameworks leave a ton of opportunity to get better. At present, in the greater part of the healthcare enterprises, the health record of a patient is put away manually which makes it harder to keep up such colossal measure of data. It is so difficult to keep up this healthcare information precisely. As a matter of first importance, all these information changes constantly, doctors are always moving all through systems, they are continually adopting new insurance coverage, they're changing office areas and changing their affiliations with facilities and clinics and the patient is analyzed at different health associations. So, the information keeps on changing except if doctors are great about reaching their systems each time one of those information fields changes which will drop out of synchronizing rapidly.