Healthcare analytics is an interdisciplinary domain aiming to assist physicians using computational techniques and digital patient data. Analyzing a vast amount of patient data is vital to infer the characteristics of a patient cohort. Pattern recognition offers essential tools for healthcare analytics tasks. In particular, machine learning and deep learning techniques have been successfully applied to various healthcare tasks, such as risk prediction, deciphering disease progression, and patient subtyping. Healthcare tasks pose numerous challenges for pattern recognition. The heterogeneous, high-dimensional, non-linear, temporal, and distributed nature of the patient data complicate the traditional techniques. Such challenges inspire the pattern recognition domain to develop novel techniques to solve specific challenges in healthcare.
The goal of this Research Topic is to showcase some of the latest and cutting-edge developments in pattern recognition for healthcare analytics. While the healthcare tasks can be modeled as an instance of the "learning from data" paradigm, patient data pose specific challenges that may not be solved by traditional pattern recognition methods. Researchers are invited to submit high-quality clinically interpretable studies addressing these challenges. Original ideas on predictive modeling, clustering, feature extraction, temporal analysis, data visualization, and interpretability for patient data in tabular, text, and image formats are encouraged. We also welcome interactive tools that facilitate the utilization of pattern recognition techniques by clinical researchers.
Original research articles and review submissions are welcomed. The scope of the Research Topic includes but is not limited to:
-Predictive modeling for heterogeneous patient data
-Disease progression modeling for temporal patient data
-Embedding learning for clinical notes
-Adversarial robustness in medical image classification
-Generative models for medical image classification
-Deep clustering techniques for patient subtyping
-Patient similarity learning for personalized medicine
-Knowledge graph embedding for healthcare
-Interpretable models for clinical decision support
Healthcare analytics is an interdisciplinary domain aiming to assist physicians using computational techniques and digital patient data. Analyzing a vast amount of patient data is vital to infer the characteristics of a patient cohort. Pattern recognition offers essential tools for healthcare analytics tasks. In particular, machine learning and deep learning techniques have been successfully applied to various healthcare tasks, such as risk prediction, deciphering disease progression, and patient subtyping. Healthcare tasks pose numerous challenges for pattern recognition. The heterogeneous, high-dimensional, non-linear, temporal, and distributed nature of the patient data complicate the traditional techniques. Such challenges inspire the pattern recognition domain to develop novel techniques to solve specific challenges in healthcare.
The goal of this Research Topic is to showcase some of the latest and cutting-edge developments in pattern recognition for healthcare analytics. While the healthcare tasks can be modeled as an instance of the "learning from data" paradigm, patient data pose specific challenges that may not be solved by traditional pattern recognition methods. Researchers are invited to submit high-quality clinically interpretable studies addressing these challenges. Original ideas on predictive modeling, clustering, feature extraction, temporal analysis, data visualization, and interpretability for patient data in tabular, text, and image formats are encouraged. We also welcome interactive tools that facilitate the utilization of pattern recognition techniques by clinical researchers.
Original research articles and review submissions are welcomed. The scope of the Research Topic includes but is not limited to:
-Predictive modeling for heterogeneous patient data
-Disease progression modeling for temporal patient data
-Embedding learning for clinical notes
-Adversarial robustness in medical image classification
-Generative models for medical image classification
-Deep clustering techniques for patient subtyping
-Patient similarity learning for personalized medicine
-Knowledge graph embedding for healthcare
-Interpretable models for clinical decision support