Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records

Abstract

Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.

Methods: In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories.

Results: The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.

Conclusions: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Keywords: Clinical notes; Natural language processing; Post-traumatic stress disorder; Real-world evidence; Research of domain criteria.

Fig. 1

Iterative flow of our sentence dictionary development during ad-hoc collection, clinical engagement, and data integration. The steps of the iterative workflow correspond to. (A) Data collection (B) Building of keyword and sentence dictionary from literature [43, 44] and SMEs

Fig. 2

Diagrammatic representation of workflow of our study

Fig. 3

RDoC domains across different patient populations: male and female. *p value = 0.03552 (Wilcoxon signed-rank test)

Fig. 4

RDoC domains across different patient populations: veterans and non-veterans. *p value = 0.03125 (Wilcoxon signed-rank test)

Fig. 5

RDoC domains across different patient populations: PTSD patients based on gender and veteran status *p value = 0.03125 (Wilcoxon signed-rank test)

Fig. 6

RDoC domains across different patient populations: PTSD patients based on gender and non-veteran status *p value = 0.03125 (Wilcoxon signed-rank test)

Fig. 7

RDoC domains across different patient populations: PTSD patients before psychotherapy and after psychotherapy *p value = 0.03125 (Wilcoxon signed-rank test)

Fig. 8

Overall RDoC changes throughout disease trajectory (PTSD, SRE, ASUD). *p value = 0.00001475 (Friedman rank sum test). *p value = 0.00001475 (Friedman rank sum test). *p value = 0.00001475 (Friedman rank sum test)

Fig. 9

RDoC keywords for PTSD patients