With rapid developments in medical sensing and imaging, we now live in an era of data explosion in which large amounts of data are readily available and accessible in the clinical environment. The fast-growing biomedical and healthcare data provide unprecedented opportunities for data-driven scientific knowledge discovery and clinical decision support. However, it is widely acknowledged that biomedical and healthcare data contain high dimensions, large volumes, missing values, imbalanced classes, heterogeneous sources, multi-modalities, and measurement noises. These inherent features pose significant barriers to the translation of data into meaningful clinical actions. Thus, there arises an urgent and compelling need for the development of innovative machine learning, simulation, and optimization algorithms to cope with the unique challenges associated with biomedical and healthcare data, facilitating the transformation of data into actionable insights for smart health.
Our Research Topic aims at catalyzing synergies among biomedical informatics, machine learning, computer simulation, operations research, systems engineering, and other related fields with three specific goals: (1) develop cutting-edge data-driven models to accelerate scientific knowledge discovery in biomedicine from medical and healthcare data collected from laboratory systems, imaging systems, and medical and sensing devices; (2) develop advanced simulation and calibration algorithms to build personalized digital twins by effectively assimilating patient-specific medical data with population-level computer models, facilitating precision medical planning; (3) develop innovative optimization algorithms for optimal medical decision making in the face of uncertainty factors, conflicting objectives, and complex trade-offs. This Research Topic will offer a timely collection of information to benefit the researchers and practitioners working in the broad fields of biomedical informatics, healthcare data analytics, medical image processing, and health-related artificial intelligence. By harnessing the potential of machine learning, computational simulation, and mathematical optimization techniques, healthcare professionals can effectively analyze and interpret the vast amount of biomedical data available to them, which will ultimately lead to more accurate disease diagnosis, personalized treatment plans, and improved patient outcomes.
We welcome submissions of original research and review papers on important new methods, applications, and insights at the intersection of machine learning, simulation, and optimization for smart health. The topics of interest include, but are not limited to:
• Developing and applying cutting-edge machine learning and optimization techniques to tackle real-world healthcare problems.
• Addressing long-lasting challenges in healthcare data analytics such as missing values, imbalanced datasets, ambiguous labeling, uncertainty quantification, and more.
• Developing effective methods to handle challenges in modeling electric-health-record (EHR) data: longitudinal records collected at irregularly-spaced visits, sparsely distributed records over time, heterogeneous medical variables, unstructured clinical notes, etc.
• Designing new data-fusion methods to integrate multiple data sources and modalities for effective biomarker extraction and decision support.
• Re-visiting traditional machine learning topics (e.g., clustering, classification, and regression) to solve newly emerging biomedical informatics problems.
• Developing effective simulation and calibration methods to build human digital twins, enabling personalized healthcare management and services.
• Developing new methods to optimize the operations and management of healthcare systems including patient flow, staff scheduling, and resource allocation.
• Designing scalable optimization algorithms for optimal decision-making based on heterogeneous and high-dimensional biomedical data.
With rapid developments in medical sensing and imaging, we now live in an era of data explosion in which large amounts of data are readily available and accessible in the clinical environment. The fast-growing biomedical and healthcare data provide unprecedented opportunities for data-driven scientific knowledge discovery and clinical decision support. However, it is widely acknowledged that biomedical and healthcare data contain high dimensions, large volumes, missing values, imbalanced classes, heterogeneous sources, multi-modalities, and measurement noises. These inherent features pose significant barriers to the translation of data into meaningful clinical actions. Thus, there arises an urgent and compelling need for the development of innovative machine learning, simulation, and optimization algorithms to cope with the unique challenges associated with biomedical and healthcare data, facilitating the transformation of data into actionable insights for smart health.
Our Research Topic aims at catalyzing synergies among biomedical informatics, machine learning, computer simulation, operations research, systems engineering, and other related fields with three specific goals: (1) develop cutting-edge data-driven models to accelerate scientific knowledge discovery in biomedicine from medical and healthcare data collected from laboratory systems, imaging systems, and medical and sensing devices; (2) develop advanced simulation and calibration algorithms to build personalized digital twins by effectively assimilating patient-specific medical data with population-level computer models, facilitating precision medical planning; (3) develop innovative optimization algorithms for optimal medical decision making in the face of uncertainty factors, conflicting objectives, and complex trade-offs. This Research Topic will offer a timely collection of information to benefit the researchers and practitioners working in the broad fields of biomedical informatics, healthcare data analytics, medical image processing, and health-related artificial intelligence. By harnessing the potential of machine learning, computational simulation, and mathematical optimization techniques, healthcare professionals can effectively analyze and interpret the vast amount of biomedical data available to them, which will ultimately lead to more accurate disease diagnosis, personalized treatment plans, and improved patient outcomes.
We welcome submissions of original research and review papers on important new methods, applications, and insights at the intersection of machine learning, simulation, and optimization for smart health. The topics of interest include, but are not limited to:
• Developing and applying cutting-edge machine learning and optimization techniques to tackle real-world healthcare problems.
• Addressing long-lasting challenges in healthcare data analytics such as missing values, imbalanced datasets, ambiguous labeling, uncertainty quantification, and more.
• Developing effective methods to handle challenges in modeling electric-health-record (EHR) data: longitudinal records collected at irregularly-spaced visits, sparsely distributed records over time, heterogeneous medical variables, unstructured clinical notes, etc.
• Designing new data-fusion methods to integrate multiple data sources and modalities for effective biomarker extraction and decision support.
• Re-visiting traditional machine learning topics (e.g., clustering, classification, and regression) to solve newly emerging biomedical informatics problems.
• Developing effective simulation and calibration methods to build human digital twins, enabling personalized healthcare management and services.
• Developing new methods to optimize the operations and management of healthcare systems including patient flow, staff scheduling, and resource allocation.
• Designing scalable optimization algorithms for optimal decision-making based on heterogeneous and high-dimensional biomedical data.
