TY - GEN
T1 - Summarizing Behavioral Health Electronic Health Records Using a Natural Language Processing Pipeline
AU - Dacayan, Tristram
AU - Ojeda, Daniel
AU - Kwak, Daehan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Doctors and nurses have limited time between patients to analyze and review a patient's documents to provide a quality assessment. This problem is supplemented by the existence of Electronic Health Records (EHR), which are essentially digital files regarding the patient. However, the length and content of each document vary greatly, reducing the effectiveness. Therefore, this research aims to reduce the need for medical professionals to manually search for crucial information about the patient's health history. We intend to accomplish our objective using various natural language processing (NLP) techniques to break down digital documents into smaller subtasks, such as event extraction and abstractive summarization, to provide a concise summary. Our proposed system intends to streamline the process and nullify the issue of having to read lengthy documents to locate essential information, which can affect the overall efficiency and quality of the patient's care. In the future, we intend to migrate to a closed-domain event extraction model and implement a timeline for easier visualization.
AB - Doctors and nurses have limited time between patients to analyze and review a patient's documents to provide a quality assessment. This problem is supplemented by the existence of Electronic Health Records (EHR), which are essentially digital files regarding the patient. However, the length and content of each document vary greatly, reducing the effectiveness. Therefore, this research aims to reduce the need for medical professionals to manually search for crucial information about the patient's health history. We intend to accomplish our objective using various natural language processing (NLP) techniques to break down digital documents into smaller subtasks, such as event extraction and abstractive summarization, to provide a concise summary. Our proposed system intends to streamline the process and nullify the issue of having to read lengthy documents to locate essential information, which can affect the overall efficiency and quality of the patient's care. In the future, we intend to migrate to a closed-domain event extraction model and implement a timeline for easier visualization.
KW - Electronic health records (EHR)
KW - Event extraction
KW - Natural language processing (NLP)
KW - pipeline
KW - Summarization
UR - http://www.scopus.com/inward/record.url?scp=85172013811&partnerID=8YFLogxK
U2 - 10.1109/CSCI58124.2022.00292
DO - 10.1109/CSCI58124.2022.00292
M3 - Conference contribution
AN - SCOPUS:85172013811
T3 - Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
SP - 1635
EP - 1639
BT - Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
Y2 - 14 December 2022 through 16 December 2022
ER -