Research Topic

Towards Personalized and Connected Health: Deep Learning for Integrating Heterogeneous Healthcare Data Sources

About this Research Topic

Information about our individual health can now be gleaned from a variety of data sources – genomic profiles, electronic medical records, smart watches, social media, to name a few. Each data source can potentially inform us about our health at different levels of granularity, ranging from behavioral to physiological to genetic levels. The future of healthcare relies on integrating the available information to build effective descriptive, predictive and prescriptive models. However, the collected data is highly heterogeneous differing in a number of characteristics such as data types, scale, formats and noise levels. Machine Learning, in particular deep learning, has witnessed tremendous progress in the last decade. This Research Topic aims to provide a collection of novel models, algorithms and systems that utilize deep learning to learn from such heterogenous data sources to build effective healthcare applications.

Topics of interest include, but are not restricted to, the following:

Deep Learning models and algorithms, with healthcare applications, for:
o Data fusion
o Pattern Recognition
o Natural Language Processing
o Vision and Image Processing
o Processing Electronic Medical Records
o Time Series Analysis
o Clustering
o Combined data-driven and knowledge-based modeling
o Learning from sparse/imbalanced data
o Predictive and Prescriptive Analytics

Deep Learning models and algorithms for:
o Management of Diet and Exercise Behavior
o Mental Health Management
o Patient Risk Stratification
o Phenotyping
o Predicting Unforeseen Clinical Complications
o Prevention and Mangement of Chronic Diseases
o Diagnostics, Triage and Treatment planning
o Personalized Medicine
o Clinical Decision Support and Public Health


Keywords: data fusion, multimodal learning, deep learning, integrated care, IoT


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Information about our individual health can now be gleaned from a variety of data sources – genomic profiles, electronic medical records, smart watches, social media, to name a few. Each data source can potentially inform us about our health at different levels of granularity, ranging from behavioral to physiological to genetic levels. The future of healthcare relies on integrating the available information to build effective descriptive, predictive and prescriptive models. However, the collected data is highly heterogeneous differing in a number of characteristics such as data types, scale, formats and noise levels. Machine Learning, in particular deep learning, has witnessed tremendous progress in the last decade. This Research Topic aims to provide a collection of novel models, algorithms and systems that utilize deep learning to learn from such heterogenous data sources to build effective healthcare applications.

Topics of interest include, but are not restricted to, the following:

Deep Learning models and algorithms, with healthcare applications, for:
o Data fusion
o Pattern Recognition
o Natural Language Processing
o Vision and Image Processing
o Processing Electronic Medical Records
o Time Series Analysis
o Clustering
o Combined data-driven and knowledge-based modeling
o Learning from sparse/imbalanced data
o Predictive and Prescriptive Analytics

Deep Learning models and algorithms for:
o Management of Diet and Exercise Behavior
o Mental Health Management
o Patient Risk Stratification
o Phenotyping
o Predicting Unforeseen Clinical Complications
o Prevention and Mangement of Chronic Diseases
o Diagnostics, Triage and Treatment planning
o Personalized Medicine
o Clinical Decision Support and Public Health


Keywords: data fusion, multimodal learning, deep learning, integrated care, IoT


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

26 June 2020 Abstract
24 August 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

26 June 2020 Abstract
24 August 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..