Results 31 to 40 of about 32,727 (270)

Artificial intelligence in ophthalmic electronic medical records, health records, and registries

Kerala Journal of Ophthalmology
The integration of electronic health records (EHRs) and electronic medical records (EMRs) is fundamentally transforming ophthalmology by generating vast amounts of clinical data.
John Davis Akkara
doaj   +1 more source

COG and subsystem comparison of strain-specific CDSs.

, 2014
The numbers indicate the relative abundance of COG and subsystem assigned genes.*, Coverage means the proportion of COG and subsystem assigned CDSs in strain specific CDSs.COG and subsystem comparison of strain-specific CDSs.
Munusamy Madhaiyan (628457), Jihyun F. Kim (13106), Yi Lee (628458), Tong-Min Sa (628459), Haeyoung Jeong (13091), Min-Jung Kwak (628456), Tae Kwang Oh (13105)   +6 more
core   +1 more source

Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management

, 2021
The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management.
Mah Laka, Tracy Merlin, Adriana Milazzo
core   +1 more source

Photo‐Switchable Molecular Module Programming PET Biodegradation in Aquatic Environments

Advanced Functional Materials, EarlyView.
We address the plastic durability paradox by integrating an itaconic acid–derived pyrrolidone module into polymer backbones. Sunlight in water triggers Norrish Type I ring‐opening and hydrophilization, enabling rapid biodegradation into nontoxic minerals.
Mohammad Asif Ali, Jixin Zheng, Satoshi Wakai, Jie Liu, Hidetaka Nomaki, Misato Tsukatani, Noriyuki Isobe, Jingcheng Sang, Yuriko Aoki, Hideo Okamura, Daisuke Sasaki, Chiaki Ogino, Yuzo Yamazaki, Takashi Nishikaze, Kenji Takada, Maiko K. Okajima, Tatsuo Kaneko   +16 more
wiley   +1 more source

Physician Compliance With a Computerized Clinical Decision Support System for Anemia Management of Patients With End-stage Kidney Disease on Hemodialysis: Retrospective Electronic Health Record Observational Study

JMIR Formative Research, 2023
BackgroundPrevious studies on clinical decision support systems (CDSSs) for the management of renal anemia in patients with end-stage kidney disease undergoing hemodialysis have previously focused solely on the effects of the CDSS.
Ju-Yeh Yang, Kai-Hsiang Shu, Yu-Sen Peng, Shih-Ping Hsu, Yen-Ling Chiu, Mei-Fen Pai, Hon-Yen Wu, Wan-Chuan Tsai, Kuei-Ting Tung, Raymond N Kuo   +9 more
doaj   +1 more source

Expression analysis of sRNAs associated with CDSs.

, 2013
(A) Heat map showing results of cluster analysis of 312 filtered CDSs with differential expression of sRNAs. The filtering criteria were at least 100 normalized (reads per kilobase per million, RPKM) counts in one of the conditions and a difference of ...
Ehud Zelzion (390893), Dana C. Price (430045), Jeferson Gross (92841), Junyi Li (430046), Cheong Xin Chan (192301), Sana Wajid (430044), Debashish Bhattacharya (35009)   +6 more
core   +1 more source

OsCPK12‐OsATPD1 Module Regulates Photosynthetic Acclimation to Light Stress in Rice

Advanced Science, EarlyView.
OsCPK12 interacts and phosphorylates OsAtpD1 at Ser8 in its transit peptide. This phosphorylation promote its chloroplast translocation, thereby maintaining ATP synthase activity and proton gradient (ΔpH) homeostasis and regulating photosynthetic acclimation, photoprotection, and repair under light stress.
Beifang Wang, Aaron Chan, Yongyi Fan, Yanwu Wang, Pao Xue, Yingxin Zhang, Xiaodeng Zhan, Weixun Wu, Ping Yu, Daibo Chen, Junlin Fu, Yongbo Hong, Xihong Shen, Lianping Sun, Aike Zhu, Shihua Cheng, Qunen Liu, Liyong Cao   +17 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
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

MODELLING THE BID AND ASK PRICES OF ILLIQUID CDSs [PDF]

International Journal of Theoretical and Applied Finance, 2012
CDS (credit default swap) contracts that were initiated some time ago frequently have spreads and/or maturities that are not available on the current market of CDSs, and are thus illiquid. This article introduces an incomplete-market approach to valuing illiquid CDSs that, in contrast to the risk-neutral approach of current market practice, allows a ...
openaire   +3 more sources