Yale 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.
Yale 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.
Acad Pediatr. 2022 Aug;22(6):981-988. doi: 10.1016/j.acap.2021.11.004. Epub 2021 Nov 12.
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.
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.
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.
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.
与身体虐待相关的医学上轻微但临床上重要的发现，如婴幼儿期的瘀伤，可能被一线临床医生发现，但这些损伤与虐待的关联常常未被识别，这可能导致虐待继续甚至升级。一种准确的自然语言处理（NLP）算法，可以识别电子健康记录中的高风险损伤，从而提高虐待的检测和意识。目的是：1）开发一种能够准确识别与虐待相关的婴儿损伤的 NLP 算法，2）确定该算法的准确性。
设计了一种 NLP 算法来识别已知与婴儿身体虐待相关的十种特定损伤。通过反复审查，确定不准确的触发因素，并调整算法的编码。优化后的 NLP 算法应用于 9 家急诊室在 3.5 个月内连续观察的 1344 例婴儿的急诊室医生记录。结果与对同一记录进行的由一名接受过培训的评审员进行的审查进行了比较，该评审员对 NLP 结果不知情，差异由虐待儿童专家进行裁决。
在 1344 次就诊中，有 41 次（3.1%）出现了一种高风险损伤。NLP 算法的敏感性和特异性分别为 92.7%（95%置信区间[CI]：79.0%-98.1%）和 98.1%（95% CI：97.1%-98.7%），阳性和阴性预测值分别为 60.3%和 99.8%，用于识别高风险损伤。
一种用于识别急诊科高风险损伤的婴儿的 NLP 算法具有良好的准确性，可能有助于临床医生识别与虐待相关的损伤的婴儿。
