Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing

Epilepsia ◽  
2019 ◽  
Author(s):  
Kristen Barbour ◽  
Dale C. Hesdorffer ◽  
Niu Tian ◽  
Elissa G. Yozawitz ◽  
Patricia E. McGoldrick ◽  
...  
Keyword(s):  
Risk Factors ◽  
Natural Language Processing ◽  
Natural Language ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Medical Records ◽  
Automated Detection ◽  
Sudden Unexpected Death ◽  
Unexpected Death

scholarly journals Development of phenotype algorithms using electronic medical records and incorporating natural language processing

BMJ ◽  
2015 ◽  
Vol 350 (apr24 11) ◽  
pp. h1885-h1885 ◽  
Author(s):  
K. P. Liao ◽  
T. Cai ◽  
G. K. Savova ◽  
S. N. Murphy ◽  
E. W. Karlson ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Medical Records

scholarly journals A Strategy for Prioritizing Electronic Medical Records Using Structured Analysis and Natural Language Processing

2017 ◽  
Vol 22 (1) ◽  
pp. 7-31 ◽  
Author(s):  
Alexandra Pomares Quimbaya ◽  
Rafael A. Gonzalez ◽  
Oscar Muñoz ◽  
Olga Milena García ◽  
Wilson Ricardo Bohorquez
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Medical Records ◽  
Information Needs ◽  
Software Tool ◽  
High Sensitivity ◽  
University Hospital ◽  
Structured Analysis

Objective: Electronic medical records (EMR) typically contain both structured attributes as well as narrative text. The usefulness of EMR for research and administration is hampered by the difficulty in automatically analyzing their narrative portions. Accordingly, this paper proposes SPIRE, a strategy for prioritizing EMR, using natural language processing in combination with analysis of structured data, in order to identify and rank EMR that match specific queries from clinical researchers and health administrators. Materials and Methods: The resulting software tool was evaluated technically and validated with three cases (heart failure, pulmonary hypertension and diabetes mellitus) compared against expert obtained results. Results and Discussion: Our preliminary results show high sensitivity (70%, 82% and 87% respectively) and specificity (85%, 73.7% and 87.5%) in the resulting set of records. The AUC was between 0.84 and 0.9. Conclusions: SPIRE was successfully implemented and used in the context of a university hospital information system, enabling clinical researchers to obtain prioritized EMR to solve their information needs through collaborative search templates with faster and more accurate results than other existing methods.

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