Research Topic

Artificial Intelligence in Medical Applications using Unstructured Electronic Health Records

About this Research Topic

The application of Artificial Intelligence (AI) in the medical field holds great promise for improving patient health. AI has made its way into hospitals around the world to assist diagnosis, treatment, and prevention. The rapid adoption of AI has benefited a lot from the wide usage of electronic health records (EHRs) and biomedical data with high-speed, big data processing computers combined with the human know-how to crack complex health care conditions. However, the vast majority of information about patient care in the EHRs is recorded as unstructured text. Compared to structured data entry in EHRs, unstructured free-text EHR is a more conventional way in the health care environment to document impressions, clinical findings, assessments, and care plans. Mining information hidden in unstructured EHRs using AI could help physicians make a better clinical decision for patient care.

The goal of this Research Topic is to provide research methodologies and applications on applying artificial intelligence on unstructured EHR data. We encourage original work dealing with topics relevant to medical artificial intelligence, expert systems, data mining, machine learning, and medical image processing.

The topics of interest include but not limited to:

I. Artificial Intelligence Techniques on Unstructured EHRs
· Natural language processing in patient care and public health
· Text mining for clinical decision support
· Clinical information extraction
· Information retrieval
· Medical named entity recognition
· Relation extraction from EHRs
· Medical question answering
· Sentiment analysis

II. Deep Learning in Healthcare
· Applications of deep learning using free-text EHRs
· Deep representations, word embeddings for medical concepts
· Distributed deep models for unstructured EHR data
· Deep learning for real-time clinical trial

This article collection welcomes diverse article types, including Original Research, Review, Methods, Technology Report, Mini Review, Perspective, Code, Data Report, General Commentary, Opinion.


Keywords: #healthrecords, #EHR, #artificialintelligence, #AI, #deeplearning


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.

The application of Artificial Intelligence (AI) in the medical field holds great promise for improving patient health. AI has made its way into hospitals around the world to assist diagnosis, treatment, and prevention. The rapid adoption of AI has benefited a lot from the wide usage of electronic health records (EHRs) and biomedical data with high-speed, big data processing computers combined with the human know-how to crack complex health care conditions. However, the vast majority of information about patient care in the EHRs is recorded as unstructured text. Compared to structured data entry in EHRs, unstructured free-text EHR is a more conventional way in the health care environment to document impressions, clinical findings, assessments, and care plans. Mining information hidden in unstructured EHRs using AI could help physicians make a better clinical decision for patient care.

The goal of this Research Topic is to provide research methodologies and applications on applying artificial intelligence on unstructured EHR data. We encourage original work dealing with topics relevant to medical artificial intelligence, expert systems, data mining, machine learning, and medical image processing.

The topics of interest include but not limited to:

I. Artificial Intelligence Techniques on Unstructured EHRs
· Natural language processing in patient care and public health
· Text mining for clinical decision support
· Clinical information extraction
· Information retrieval
· Medical named entity recognition
· Relation extraction from EHRs
· Medical question answering
· Sentiment analysis

II. Deep Learning in Healthcare
· Applications of deep learning using free-text EHRs
· Deep representations, word embeddings for medical concepts
· Distributed deep models for unstructured EHR data
· Deep learning for real-time clinical trial

This article collection welcomes diverse article types, including Original Research, Review, Methods, Technology Report, Mini Review, Perspective, Code, Data Report, General Commentary, Opinion.


Keywords: #healthrecords, #EHR, #artificialintelligence, #AI, #deeplearning


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.

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Submission Deadlines

15 January 2019 Manuscript

Participating Journals

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

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Topic Editors

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Submission Deadlines

15 January 2019 Manuscript

Participating Journals

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

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