Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework [0.03%]
基于诊断编码的患者嵌入在医疗保健预测任务中的应用：Pat2Vec机器学习框架
Edgar Steiger,Lars Eric Kroll
Edgar Steiger
Background: In health care, diagnosis codes in claims data and electronic health records (EHRs) play an important role in data-driven decision making. Any analysis that uses a patient's diagnosis codes to predict future o...
Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach [0.03%]
利用修正德尔菲法制定伦理和公平原则、条件及参与工具以推进人工智能和机器学习的健康公平性和研究人员多样性
Rachele Hendricks-Sturrup,Malaika Simmons,Shilo Anders et al.
Rachele Hendricks-Sturrup et al.
Background: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohu...
Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study [0.03%]
深度学习变换模型构建综合实时创伤监测系统：发展与验证研究
Gabrielle Chenais,Cédric Gil-Jardiné,Hélène Touchais et al.
Gabrielle Chenais et al.
Background: Public health surveillance relies on the collection of data, often in near-real time. Recent advances in natural language processing make it possible to envisage an automated system for extracting information ...
Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review [0.03%]
利用机器学习预测行为改变支持系统遵从性：系统综述
Akon Obu Ekpezu,Isaac Wiafe,Harri Oinas-Kukkonen
Akon Obu Ekpezu
Background: There is a dearth of knowledge on reliable adherence prediction measures in behavior change support systems (BCSSs). Existing reviews have predominately focused on self-reporting measures of adherence. These m...
Review
JMIR AI. 2023 Nov 22:2:e46779. DOI:10.2196/46779 2023
Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study [0.03%]
基于孪生神经网络的临床自然语言处理少样本学习算法研发与验证研究
David Oniani,Premkumar Chandrasekar,Sonish Sivarajkumar et al.
David Oniani et al.
Background: Natural language processing (NLP) has become an emerging technology in health care that leverages a large amount of free-text data in electronic health records to improve patient care, support clinical decisio...
Physicians' and Machine Learning Researchers' Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study [0.03%]
临床机器学习工具早期开发中的伦理问题：医师和机器学习研究者的视角——定性访谈研究
Jane Paik Kim,Katie Ryan,Max Kasun et al.
Jane Paik Kim et al.
Background: Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a rang...
Determinants of Intravenous Infusion Longevity and Infusion Failure via a Nonlinear Model Analysis of Smart Pump Event Logs: Retrospective Study [0.03%]
智能输液泵事件日志的非线性模型分析确定输液持续时间和输液失败的因素：回顾性研究
Arash Kia,James Waterson,Norma Bargary et al.
Arash Kia et al.
Background: Infusion failure may have severe consequences for patients receiving critical, short-half-life infusions. Continued interruptions to infusions can lead to subtherapeutic therapy. ...
Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study [0.03%]
采用识别问题蕴含系统识别医药行业监管文件中的相似问题的评估研究
Nidhi Saraswat,Chuqin Li,Min Jiang
Nidhi Saraswat
Background: The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of ...
Identifying Frailty in Older Adults Receiving Home Care Assessment Using Machine Learning: Longitudinal Observational Study on the Role of Classifier, Feature Selection, and Sample Size [0.03%]
基于机器学习的居家护理评估中老年人衰弱识别：关于分类器、特征选择和样本量作用的纵向观察研究
Cheng Pan,Hao Luo,Gary Cheung et al.
Cheng Pan et al.
Background: Machine learning techniques are starting to be used in various health care data sets to identify frail persons who may benefit from interventions. However, evidence about the performance of machine learning te...
Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach [0.03%]
基于上下文的语言方法检测X（以前的Twitter）中的短暂抑郁情绪
Ali Akbar Jamali,Corinne Berger,Raymond J Spiteri
Ali Akbar Jamali
Background: Depression and momentary depressive feelings are major public health concerns imposing a substantial burden on both individuals and society. Early detection of momentary depressive feelings is highly beneficia...