Association of Preinjury Medical Diagnoses With Pediatric Persistent Postconcussion Symptoms in Electronic Health Records

Applications of Natural Language Processing in Otolaryngology: A Scoping Review

OBJECTIVE

To review the current literature on the applications of natural language processing (NLP) within the field of otolaryngology.

DATA SOURCES

MEDLINE, EMBASE, SCOPUS, Cochrane Library, Web of Science, and CINAHL.

METHODS

The preferred reporting Items for systematic reviews and meta-analyzes extension for scoping reviews checklist was followed. Databases were searched from the date of inception up to Dec 26, 2023. Original articles on the application of language-based models to otolaryngology patient care and research, regardless of publication date, were included. The studies were classified under the 2011 Oxford CEBM levels of evidence.

RESULTS

One-hundred sixty-six papers with a median publication year of 2024 (range 1982, 2024) were included. Sixty-one percent (102/166) of studies used ChatGPT and were published in 2023 or 2024. Sixty studies used NLP for clinical education and decision support, 42 for patient education, 14 for electronic medical record improvement, 5 for triaging, 4 for trainee education, 4 for patient monitoring, 3 for telemedicine, and 1 for medical translation. For research, 37 studies used NLP for extraction, classification, or analysis of data, 17 for thematic analysis, 5 for evaluating scientific reporting, and 4 for manuscript preparation.

CONCLUSION

The role of NLP in otolaryngology is evolving, with ChatGPT passing OHNS board simulations, though its clinical application requires improvement. NLP shows potential in patient education and post-treatment monitoring. NLP is effective at extracting data from unstructured or large data sets. There is limited research on NLP in trainee education and administrative tasks. Guidelines for NLP use in research are critical.

A Narrative Review Of Risk Factors And Predictors For Poor Outcome And Prolonged Recovery After A Mild Traumatic Brain Injury

MATERIAL AND METHODS

A comprehensive search of the main scientific databases (PubMed, Web of Science, Embase, and Cochrane Library) was performed using keywords, such as: "prolonged post-concussion syndrome", combined with "risk factors", "predictors", and "outcomes".

RESULTS

Multiple studies reported more than one risk factor for PPCS development following mTBIs that were generally the results of sports-related concussions and car accidents. The most prevalent risk factor associated with PPCS was the female sex. Social factors/personality traits, anxiety, mental health disorders, or other health conditions from their past medical history, the occurrence of headache/migraines during TBI recovery, somatization, physical activity, and litigation were also reported to contribute to PPCS risk.

CONCLUSIONS

An exhaustive approach is required to mitigate the risk of PPCS and to ensure optimal recovery after concussive events. However, larger prospective cohort studies evaluating patients that were examined and treated with standardized protocols could be needed to further validate these associations and mandate the highest risk factors for delayed recovery.

Natural language processing in clinical neuroscience and psychiatry: A review

Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services.