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Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With
Gunjan Tiyyagura1, Andrea G Asnes1, John M Leventhal1
1Yale University School of Medicine (G Tiyyagura, AG Asnes, JM Leventhal, ED Shapiro, M Auerbach, W Teng, E Powers, A Thomas, AL Hsiao), New Haven, CT.
Insights
A new natural language processing (NLP) algorithm can accurately identify infants with injuries linked to child abuse in electronic health records, improving early detection and intervention for at-risk children.
Area of Science:
- Medical Informatics
- Pediatric Emergency Medicine
- Child Abuse Detection
Background:
- Clinicians may overlook subtle injuries in infants that are indicative of physical child abuse.
- Failure to recognize these injuries can allow abuse to continue or escalate, posing significant risks to child welfare.
Purpose of the Study:
- To develop a natural language processing (NLP) algorithm for identifying infant injuries associated with physical abuse.
- To evaluate the accuracy of the developed NLP algorithm in detecting these high-risk injuries.
Main Methods:
- An NLP algorithm was created to detect ten specific injuries linked to infant abuse.
- The algorithm was refined through iterative review and applied to 1344 emergency department (ED) provider notes.
- Results were validated against expert review of the same clinical notes.
Main Results:
- The NLP algorithm demonstrated high accuracy in identifying high-risk injuries in infants.
- Sensitivity was 92.7% and specificity was 98.1%.
- Positive predictive value was 60.3% and negative predictive value was 99.8%.
Conclusions:
- An NLP algorithm can accurately identify infants with injuries suggestive of child abuse in ED settings.
- This tool shows promise in assisting clinicians to better recognize and address potential child abuse cases.
Objectives:
Medically minor but clinically important findings associated with physical child abuse, such as bruises in pre-mobile infants, may be identified by frontline clinicians yet the association of these injuries with child abuse is often not recognized, potentially allowing the abuse to continue and even to escalate. An accurate natural language processing (NLP) algorithm to identify high-risk injuries in electronic health record notes could improve detection and awareness of abuse. The objectives were to: 1) develop an NLP algorithm that accurately identifies injuries in infants associated with abuse and 2) determine the accuracy of this algorithm.
Methods:
An NLP algorithm was designed to identify ten specific injuries known to be associated with physical abuse in infants. Iterative cycles of review identified inaccurate triggers, and coding of the algorithm was adjusted. The optimized NLP algorithm was applied to emergency department (ED) providers' notes on 1344 consecutive sample of infants seen in 9 EDs over 3.5 months. Results were compared with review of the same notes conducted by a trained reviewer blind to the NLP results with discrepancies adjudicated by a child abuse expert.
Results:
Among the 1344 encounters, 41 (3.1%) had one of the high-risk injuries. The NLP algorithm had a sensitivity and specificity of 92.7% (95% confidence interval [CI]: 79.0%-98.1%) and 98.1% (95% CI: 97.1%-98.7%), respectively, and positive and negative predictive values were 60.3% and 99.8%, respectively, for identifying high-risk injuries.
Conclusions:
An NLP algorithm to identify infants with high-risk injuries in EDs has good accuracy and may be useful to aid clinicians in the identification of infants with injuries associated with child abuse.
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