Issue |
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
|
|
---|---|---|
Article Number | 03001 | |
Number of page(s) | 17 | |
Section | Deep Learning | |
DOI | https://doi.org/10.1051/itmconf/20235603001 | |
Published online | 09 August 2023 |
Comprehensive Review of Deep learning Techniques in Electronic Medical Records
1 Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India
2 Assistant Professor, Department of Computer Science and Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore, India
3 Assistant Professor, Department of Computer Science and Engineering, Karpagam University, Coimbatore, India
4 Associate Professor, School of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India
Email – banupriya12317@gmail.com
Email – revathymkit@gmail.com
Email – raashma.mahaboobkahedu.edu.in
Email – meenagovind1@yahoo.in
A digital collection of patient’s health care data like diagnosis history of patient, treatment details, medical prescriptions are stored electronically. This electronic patient health records (EPHR) model provides huge volume of real time data and used for clinical research. Natural Language processing (NLP) automatically retrieve the patient’s information based on decision support system. NLP performs traditional techniques of machine learning, deep learning algorithms and focussing on word embeddings, classification and prediction, extraction, knowledge graphs, phenotyping, etc. By using NLP technique, extract the information from clinical data and analysis it provides valuable patient medical information. NLP based on clinical systems are evaluated on document level annotations which contains document of patient report, health status of patient, document section types contain past medical history of patient, summary of discharge statement, etc. similarly the semantic properties contain severity of disease in the aspects of positivity, negativity. These documents are developed and implemented on word level or sentence level. In this survey article, we summarize the recent NLP techniques which are used in EPHR applications. This survey paper focuses on prediction, classification, extraction, embedding, phenotyping, multilingually etc techniques.
Key words: Electronic Health Record / Medical Information / NLP / EPHR / document level / deep learning / machine learning
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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