PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: In our study, we created an 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, allmpnet-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.
UNASSIGNED: The sentence transformer model demonstrated superior 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. Women had the highest abnormalities of sensorimotor systems, while veterans had the highest abnormalities of negative and positive valence systems. The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.
UNASSIGNED: 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.
UNASSIGNED: In our study, we created an 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, allmpnet-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.
UNASSIGNED: The sentence transformer model demonstrated superior 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. Women had the highest abnormalities of sensorimotor systems, while veterans had the highest abnormalities of negative and positive valence systems. The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.
UNASSIGNED: 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：
背景技术从创伤后应激障碍（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域中都表现出优异的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。女性感觉运动系统异常最高，而退伍军人的阴性和阳性效价系统异常最高。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。
