Patients are often encouraged to make use of wearable devices for remote collection and monitoring of health data. This adoption of wearables results in a significant increase in the volume of data collected and transmitted. The battery life of the devices is then quickly diminished due to the high processing requirements of the devices. Given the importance attached to medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data for network transmission may improve sensor battery life without compromising accuracy. There is a trade-off between efficiency and accuracy which can be controlled by adjusting the sampling and transmission rates. This paper demonstrates that machine learning can be used to analyse complex health data metrics such as the accuracy and efficiency of data transmission to overcome the trade-off problem. The study uses time series nonlinear autoregressive neural network algorithms to enhance both data metrics by taking fewer samples to transmit. The algorithms were tested with a standard heart rate dataset to compare their accuracy and efficiency. The result showed that the Levenbery-Marquardt algorithm was the best performer with an efficiency of 3.33 and accuracy of 79.17%, which is similar to other algorithms accuracy but demonstrates improved efficiency. This proves that machine learning can improve without sacrificing a metric over the other compared to the existing methods with high efficiency.
Massachusetts Institute of Technology（麻省理工学院）；Vector Institute（向量研究所）；University of Toronto（多伦多大学）；New York University（纽约大学）；New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）
The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses
The Fu Foundation School of Engineering and Applied Science, Columbia University（哥伦比亚大学傅 Foundation 工程与应用科学学院）；College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）
HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System
United Arab Emirates University（阿联酋大学）；The University of Melbourne（墨尔本大学）
Review of multimodal machine learning approaches in healthcare
Oxford Internet Institute, University of Oxford（牛津大学互联网研究所）；Institute for People-Centred AI, University of Surrey（萨里大学以人为本人工智能研究所）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London（伦敦大学学院惠康/英国工程与物理科学研究委员会介入与外科科学中心）
A Framework for Selection of Machine Learning Algorithms Based on Performance Metrices and Akaike Information Criteria in Healthcare, Telecommunication, and Marketing Sector
Selecting Interpretability Techniques for Healthcare Machine Learning models
University of Antioquia UdeA（安蒂奥基亚大学）；Universidad de Antioquia UdeA（安蒂奥基亚大学）；University of Antioquia（安蒂奥基亚大学）；Hospital Alma Máter de Antioquia（安蒂奥基亚母校医院）；Arcada University of Applied Sciences（阿卡达应用科学大学）
Secure and Robust Machine Learning for Healthcare: A Survey
University of the West England (UWE)（西英格兰大学（UWE））；Hamad Bin Khalifa University (HBKU)（哈马德·本·哈利法大学（HBKU））；Information Technology University (ITU)（信息技术大学（ITU））
MLHOps: Machine Learning for Healthcare Operations
Vector Institute for Artificial Intelligence（向量人工智能研究所）；Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；York University（约克大学）；Dalhousie University（达尔豪斯大学）
AI-based modular warning machine for risk identification in proximity healthcare
Department of Electrical, Computer and Biomedical Engineering, Università di Pavia, Pavia, Italy（电气、计算机与生物医学工程系，普瓦亚大学，普瓦亚，意大利）；Neuroscience and Human Genetics Department, Meyer Children's Hospital IRCCS, Firenze, Italy（神经科学与人类遗传学系，梅耶儿童医院IRCCS，佛罗伦萨，意大利）
Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction
Courant Institute of Mathematical Sciences（科朗数学科学研究所）；Center for Data Science（数据科学中心）；New York University（纽约大学）；New York University Langone Medical Center（纽约大学兰贡医学中心）
ISTHMUS: Secure, Scalable, Real-time and Robust Machine Learning Platform for Healthcare
Parkland Center for Clinical Innovation（帕克兰临床创新中心）；Dallas, TX 75247, USA（美国得克萨斯州达拉斯市75247（注：此条为地址，非机构，按要求不应提取，实际仅需保留Parkland Center for Clinical Innovation））
Automated Ensemble Multimodal Machine Learning for Healthcare
Department of Statistics, University of Oxford（统计系，牛津大学）；Division of Medical Image Computing, German Cancer Research Center (DKFZ)（医学影像计算部，德国癌症研究中心（DKFZ））；Medical Faculty Heidelberg, Heidelberg University（海德堡大学医学系）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（模式分析与学习小组，海德堡大学医院放射肿瘤学系）；National Center for Tumor Diseases (NCT) Heidelberg（海德堡国家肿瘤疾病中心（NCT））；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）
Accu-Help: A Machine Learning based Smart Healthcare Framework for Accurate Detection of Obsessive Compulsive Disorder
Gangadhar Meher University（甘加达尔·梅赫尔大学）；Konark Institute of Science and Technology（科纳克科学技术学院）；King George’s Medical University（乔治国王医科大学）；All India Institute of Medical Sciences, Bhubaneswar（全印医学科学学院布巴内斯瓦尔分院）；University of North Texas（北德克萨斯大学）
SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning
University of Melbourne（墨尔本大学）；United Arab Emirates University（阿联酋大学）；Emirates Center for Mobility Research（阿联酋交通研究中心）；National Water and Energy Center（国家水资源与能源中心）；UAE University（阿联酋大学）
Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research
Madrigal Pharmaceuticals Inc（马德里加尔制药公司）；Boston University School of Medicine（波士顿大学医学院）；AbbVie Inc.（艾伯维公司）；St. Elizabeth Healthcare（圣伊丽莎白医疗中心）；Thermo Fisher Scientific（赛默飞世尔科技）
