Results 221 to 230 of about 1,120,114 (285)

ParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems

Advanced Intelligent Systems, EarlyView.
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng, Wenfeng Fan, Feng Li, Jian Wu, Wei Quan   +4 more
wiley   +1 more source

Soft Active Electromyography Interface for Machine Learning‐Enabled Silent Speech Recognition

Advanced Intelligent Systems, EarlyView.
A soft, hand‐worn electromyography interface enables intent‐driven silent speech recognition without continuous facial attachment. The device integrates liquid‐metal interconnects, a transparent flexible circuit, and elastomer encapsulation with a fingertip electrode that contacts perioral muscles only on demand.
Yuta Kurotaki, Shusuke Yamakoshi, Reitaro Yoshida, Yutaka Isoda, Tamami Takano, Yuji Isano, Yusuke Miyake, Kentaro Kuribayashi, Hiroki Ota   +8 more
wiley   +1 more source

Co‐Design of Stretchable Fabric Sensors and Tiny Machine Learning for Human Interface Device‐Based Edge‐Intelligent Wearable Gloves

Advanced Intelligent Systems, EarlyView.
A flexible smart glove integrated with textile sensors enables real‐time hand gesture recognition for human–machine interaction. Using edge AI inference and Bluetooth communication, the system translates finger movements into human interface device commands to control endpoint devices efficiently.
Chi Cuong Vu, Tuan Nghia Nguyen, Minh‐Thanh Le   +2 more
wiley   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

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Electronic Health Records and Personal Health Records

Seminars in Oncology Nursing, 2011
To provide an overview of electronic personal health information technology.Peer reviewed research studies, review articles, and web resources.As technology develops and electronic health records become more common, patients and clinicians are working toward a safer, more personal form of health care delivery.Improving access and input to personal ...
Patricia C Dykes
exaly   +3 more sources

Personal Health Records

Information Security in Healthcare, 2020
Personal health records (PHR) are tools that empower the healthcare consumer/patient to participate in managing their family’s health. The National Health Information Infrastructure Workgroup of the National Committee on Vital and Health Statistics held six hearings on PHRs and PHR systems between 2002 and 2005.
Lory Wood
openaire   +2 more sources

Do personal health concerns and trust in healthcare providers mitigate privacy concerns? Effects on patients' intention to share personal health data on electronic health records.

Social Science & Medicine (1967), 2021
RATIONALE Health digitalization raises important challenges for personal health-data management. Particularly, the advantages underlying the implementation of Electronic Health Record (EHR) remain limited in many countries due to patients' privacy ...
E. Cherif, Nora Bezaz, M. Mzoughi
semanticscholar   +1 more source

Blockchain-Backed Searchable Proxy Signcryption for Cloud Personal Health Records

IEEE Transactions on Services Computing, 2023
Patient-centered data management and sharing of personal health records (PHRs) are difficult to be realized as data is controlled by doctors/hospitals. In addition, security and privacy, oppressive costs, search and tracing unreliability, and complicated
Suhui Liu, Liquan Chen, Ge Wu, Huaqun Wang, Hongtao Yu   +4 more
semanticscholar   +1 more source

Secure Decentralized Attribute-Based Sharing of Personal Health Records With Blockchain

IEEE Internet of Things Journal, 2022
Personal health records (PHRs) are located in a patient-centered electronic health system in which users can store and share medical information.
Leyou Zhang, Tianshuai Zhang, Qing Wu, Yi Mu, F. Rezaeibagha   +4 more
semanticscholar   +1 more source