Multimodal machine learning in precision health: A scoping review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare
DQ Wang, LY Feng, JG Ye, JG Zou… - MedComm–Future …, 2023 - Wiley Online Library
Large‐scale artificial intelligence (AI) models such as ChatGPT have the potential to
improve performance on many benchmarks and real‐world tasks. However, it is difficult to …
improve performance on many benchmarks and real‐world tasks. However, it is difficult to …
Reviewing multimodal machine learning and its use in cardiovascular diseases detection
Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI)
that have already demonstrated their effectiveness in a variety of domains, including …
that have already demonstrated their effectiveness in a variety of domains, including …
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
A Khodadadi, N Ghanbari Bousejin, S Molaei… - Sensors, 2023 - mdpi.com
An electronic health record (EHR) is a vital high-dimensional part of medical concepts.
Discovering implicit correlations in the information of this data set and the research and …
Discovering implicit correlations in the information of this data set and the research and …
Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system
Postoperative patients are at risk of life-threatening complications such as hemodynamic
decompensation or arrhythmia. Automated detection of patients with such risks via a real …
decompensation or arrhythmia. Automated detection of patients with such risks via a real …
Dynamic prediction of patient outcomes in the intensive care unit: a scoping review of the state-of-the-art
Introduction Intensive care units (ICUs) are high-pressure, complex, technology-intensive
medical environments where patient physiological data are generated continuously. Due to …
medical environments where patient physiological data are generated continuously. Due to …
Prediction of postoperative deterioration in cardiac surgery patients using electronic health record and physiologic waveform data
MR Mathis, MC Engoren, AM Williams… - …, 2022 - pubs.asahq.org
Background Postoperative hemodynamic deterioration among cardiac surgical patients can
indicate or lead to adverse outcomes. Whereas prediction models for such events using …
indicate or lead to adverse outcomes. Whereas prediction models for such events using …
[HTML][HTML] Prediction of oral food challenge outcomes via ensemble learning
J Zhang, D Lee, K Jungles, D Shaltis, K Najarian… - Informatics in Medicine …, 2023 - Elsevier
Abstract Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy
due to the limitations of existing clinical testing. However, some patients are hesitant to …
due to the limitations of existing clinical testing. However, some patients are hesitant to …
Continuous sepsis trajectory prediction using tensor-reduced physiological signals
Abstract The quick Sequential Organ Failure Assessment (qSOFA) system identifies an
individual's risk to progress to poor sepsis-related outcomes using minimal variables. We …
individual's risk to progress to poor sepsis-related outcomes using minimal variables. We …
Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals
Early identification of coronary artery disease (CAD) can facilitate timely clinical intervention
and save lives. This study aims to develop a machine learning framework that uses tensor …
and save lives. This study aims to develop a machine learning framework that uses tensor …