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| http://dx.doi.org/10.1136/bmjgh-2021-006289 | DOI Listing |
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Developing a Machine Learning-Based Automated Patient Engagement Estimator for Telehealth: Algorithm Development and Validation Study.
JMIR Form Res
January 2025
Department of Computer Science, Purdue University, West Lafayett, IN, United States.
Background: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited.
View Article and Find Full Text PDFKey Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.
Curr Cardiol Rep
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Purpose Of Review: Artificial Intelligence (AI) technology will significantly alter critical care cardiology, from our understanding of diseases to the way in which we communicate with patients and colleagues. We summarize the potential applications of AI in the cardiac intensive care unit (CICU) by reviewing current evidence, future developments and possible challenges.
Recent Findings: Machine Learning (ML) methods have been leveraged to improve interpretation and discover novel uses for diagnostic tests such as the ECG and echocardiograms.
Characterization and Targeting of the Main Drivers of Elective Imaging Utilization: A Cross-sectional Study Applying Conjoint Analysis.
J Am Coll Radiol
January 2025
University of Chicago, Department of Radiology, Chicago, IL.
Objective: To characterize patient and referring physician preferences when selecting where to have elective imaging performed METHODS: In this Institutional Research Board approved survey-based study, online surveys were completed by 393 patients who had recently had a non-contrast MRI of the shoulder, lumbar spine, or knee within the past 2 years and 168 physicians who had ordered at least 12 such MRIs in the past year. The survey data included explicit questions about preferences as well as a set of conjoint choice screens. Conjoint analysis utilized a logistic model estimated using maximum likelihood estimation.
View Article and Find Full Text PDFscGO: interpretable deep neural network for cell status annotation and disease diagnosis.
Brief Bioinform
November 2024
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, No. 800 Dong Chuan Road, Shanghai 200240, China.
Machine learning has emerged as a transformative tool for elucidating cellular heterogeneity in single-cell RNA sequencing. However, a significant challenge lies in the "black box" nature of deep learning models, which obscures the decision-making process and limits interpretability in cell status annotation. In this study, we introduced scGO, a Gene Ontology (GO)-inspired deep learning framework designed to provide interpretable cell status annotation for scRNA-seq data.
View Article and Find Full Text PDFAnomaly detection in multidimensional time series for water injection pump operations based on LSTMA-AE and mechanism constraints.
Sci Rep
January 2025
College of computer science and technology, China University of Petroleum (East China), No.66 Changjiang West Road, Huangdao, Qingdao, 266580, Shandong, China.
Addressing the issues of inadequate information exchange among subsequences in the operational time series of water injection pumps, leading to low accuracy and high false alarm rates in anomaly detection, this paper proposes a multidimensional time series anomaly detection method for water injection pump operations, leveraging Long Short-Term Memory Autoencoder augmented with Attention Mechanism (LSTMA-AE) and mechanistic constraints. The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). The Encoder captures temporal dependencies and features within the input sequences, mapping the input data into a higher-dimensional space.
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