PDF(Pubmed)
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.
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.
摘要：
背景：从创伤后应激障碍（PTSD）等高危人群中提取领域标准（RDoC）的研究对于积极的心理健康改善和政策增强至关重要。收集的复杂性，集成,并为此目的有效利用临床笔记引入复杂性。
方法：在我们的研究中，我们创建了一个自然语言处理（NLP）工作流程来分析电子病历（EMR）数据，并使用预训练的基于变压器的自然语言模型来识别和提取领域标准的研究，all-mpnet-base-v2。随后，我们从100,000种临床笔记中构建了词典，并分析了匹兹堡大学医学中心38,807名PTSD患者的567万种临床笔记。随后，我们通过在两个用例中提取和可视化RDoC信息来展示我们方法的重要性:(i)跨多个患者群体,以及(ii)贯穿各种疾病轨迹.
结果：句子转换模型在所有RDoC领域都显示出很高的F1宏得分，以0.3的余弦相似度阈值实现最高性能。这确保了在所有RDoC域中至少80%的F1得分。该研究显示，心理治疗后PTSD患者的所有六个RDoC域均持续减少。我们发现，与PTSD男性相比，60.6％的PTSD女性至少有六个RDoC域的一个异常实例（51.3％），与男性（41.3％）相比，PTSD女性中有45.1％的感觉运动障碍水平更高。根据我们的记录，我们还发现57.3％的PTSD患者至少有六个RDoC域的一个异常实例。此外，与非退伍军人(分别为59.1%和49.2%)相比,退伍军人的阴性和阳性效价系统异常更高(分别为60%和51.9%).首次诊断PTSD后的领域与对创伤的线索反应性增强有关，自杀,酒精,和物质消费。
结论：这些发现为不同人群和疾病轨迹中的RDoC功能提供了初步见解。自然语言处理被证明对捕获实时，来自广泛临床记录的上下文相关RDoC实例。
方法：在我们的研究中，我们创建了一个自然语言处理（NLP）工作流程来分析电子病历（EMR）数据，并使用预训练的基于变压器的自然语言模型来识别和提取领域标准的研究，all-mpnet-base-v2。随后，我们从100,000种临床笔记中构建了词典，并分析了匹兹堡大学医学中心38,807名PTSD患者的567万种临床笔记。随后，我们通过在两个用例中提取和可视化RDoC信息来展示我们方法的重要性:(i)跨多个患者群体,以及(ii)贯穿各种疾病轨迹.
结果：句子转换模型在所有RDoC领域都显示出很高的F1宏得分，以0.3的余弦相似度阈值实现最高性能。这确保了在所有RDoC域中至少80%的F1得分。该研究显示，心理治疗后PTSD患者的所有六个RDoC域均持续减少。我们发现，与PTSD男性相比，60.6％的PTSD女性至少有六个RDoC域的一个异常实例（51.3％），与男性（41.3％）相比，PTSD女性中有45.1％的感觉运动障碍水平更高。根据我们的记录，我们还发现57.3％的PTSD患者至少有六个RDoC域的一个异常实例。此外，与非退伍军人(分别为59.1%和49.2%)相比,退伍军人的阴性和阳性效价系统异常更高(分别为60%和51.9%).首次诊断PTSD后的领域与对创伤的线索反应性增强有关，自杀,酒精,和物质消费。
结论：这些发现为不同人群和疾病轨迹中的RDoC功能提供了初步见解。自然语言处理被证明对捕获实时，来自广泛临床记录的上下文相关RDoC实例。
