Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning

2019 ◽  
pp. 357-378 ◽  
Author(s):  
Feifan Liu ◽  
Chunhua Weng ◽  
Hong Yu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Clinical Research ◽  
Language Processing ◽  
Health Records ◽  
Electronic Health

scholarly journals Using Natural Language Processing and Machine Learning to Identify Incident Stroke from Electronic Health Records (Preprint)

10.2196/22951 ◽  
2020 ◽  
Author(s):  
Yiqing Zhao ◽  
Sunyang Fu ◽  
Suzette Bielinski ◽  
Paul A Decker ◽  
Alanna M Chamberlain ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Health Records ◽  
Electronic Health

scholarly journals Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0211116 ◽  
Author(s):  
Nicholas J. Carson ◽  
Brian Mullin ◽  
Maria Jose Sanchez ◽  
Frederick Lu ◽  
Kelly Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Suicidal Behavior ◽  
Health Records ◽  
Electronic Health ◽  
Psychiatrically Hospitalized

Development of algorithm for classification smoking status from unstructured bilingual electronic health records based on natural language processing (Preprint)

2021 ◽  
Author(s):  
Ye Seul Bae ◽  
Kyung Hwan Kim ◽  
Han Kyul Kim ◽  
Sae Won Choi ◽  
Taehoon Ko ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Smoking Status ◽  
Svm Classifier ◽  
Keyword Extraction ◽  
Health Records ◽  
Clinical Notes ◽  
Electronic Health

BACKGROUND Smoking is a major risk factor and important variable for clinical research, but there are few studies regarding automatic obtainment of smoking classification from unstructured bilingual electronic health records (EHR). OBJECTIVE We aim to develop an algorithm to classify smoking status based on unstructured EHRs using natural language processing (NLP). METHODS With acronym replacement and Python package Soynlp, we normalize 4,711 bilingual clinical notes. Each EHR notes was classified into 4 categories: current smokers, past smokers, never smokers, and unknown. Subsequently, SPPMI (Shifted Positive Point Mutual Information) is used to vectorize words in the notes. By calculating cosine similarity between these word vectors, keywords denoting the same smoking status are identified. RESULTS Compared to other keyword extraction methods (word co-occurrence-, PMI-, and NPMI-based methods), our proposed approach improves keyword extraction precision by as much as 20.0%. These extracted keywords are used in classifying 4 smoking statuses from our bilingual clinical notes. Given an identical SVM classifier, the extracted keywords improve the F1 score by as much as 1.8% compared to those of the unigram and bigram Bag of Words. CONCLUSIONS Our study shows the potential of SPPMI in classifying smoking status from bilingual, unstructured EHRs. Our current findings show how smoking information can be easily acquired and used for clinical practice and research.

Abstract PO-050: Identifying de novo stage IV breast cancer (DNIV) cases in Electronic Health Records (EHR) using natural language processing

2021 ◽  
Author(s):  
Liwei Wang ◽  
Karthik Giridhar ◽  
Kimberly Corbin ◽  
Brenda Ernst ◽  
Sadia Choudhery ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
De Novo ◽  
Stage Iv ◽  
Health Records ◽  
Stage Iv Breast Cancer ◽  
Electronic Health

scholarly journals Correction: Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis

10.2196/30153 ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. e30153
Author(s):  
Maciej Rybinski ◽  
Xiang Dai ◽  
Sonit Singh ◽  
Sarvnaz Karimi ◽  
Anthony Nguyen
Keyword(s):  
Natural Language Processing ◽  
Family History ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Family History Information ◽  
Health Records ◽  
History Information ◽  
Electronic Health

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