The RCMI Program in Health Disparities at Meharry Medical College - Supplement

梅哈里医学院的 RCMI 健康差异项目 - 补充

• 批准号: 10800382
• 负责人: Samuel Evans Adunyah
• 金额: $ 7.28万
• 依托单位: MEHARRY MEDICAL COLLEGE
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-30 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10800382
• 关键词: Abstinence; Address; Admission activity; Affect; Ambulatory Care Facilities; American; Applications Grants; Behavior; Characteristics; Clinical; Cocaine use disorder; Code; Cohort Studies; Communities; Complex; Data; Data Analytics; Data Science; Detection; Diagnosis; Disease; Economics; Education; Electronic Health Record; Employment Opportunities; Environment; Food; Future; Goals; Health; Housing; Individual; Machine Learning; Manuals; Measures; Mental disorders; Methods; Modeling; Names; Natural Language Processing; Natural Language Processing pipeline; Neighborhoods; Nutritional; Outcome; Output; Patients; Performance; Persons; Phenotype; Play; Postpartum Women; Pregnant Women; Prevalence; Public Health; Quality of Care; Reduce health disparities; Relapse; Residential Treatment; Retrieval; Risk; Risk Factors; Role; Sampling; Self Efficacy; Services; Structure; Substance Use Disorder; Surveys; Techniques; Testing; Text; Translating; Transportation; Underserved Population; Woman; black women; clinical care; clinical decision support; cohort; comorbidity; connected health; data infrastructure; electronic structure; experience; health care availability; health care service; health data; health disparity; health management; improved; marijuana use disorder; medical schools; mental health center; opioid use disorder; outcome prediction; poor health outcome; predictive modeling; programs; self esteem; social; social health determinants; structured data; tool; treatment program

PROJECT SUMMARY Substance use disorders (SUDs) are a major public health issue that has recently more than doubled in prevalence among Americans in the last few years, from affecting 20 million in 2018 to 46.3 million Americans in 2021. Those affected by SUD disproportionately experience negative social determinants of health (SDoH), including inadequate access to safe housing, transportation, education, employment opportunities, and nutritious foods. These SDoH are connected with low self-esteem, self-efficacy, and failed attempts at SUD abstinence. Collecting and integrating SDoH information as part of patients’ electronic health records (EHR) for clinical modeling could help uncover patient experiences and behaviors related to SUD, but the majority of SDoH are embedded in unstructured free text. Additionally, the SUD diagnosis is under represented in structural EHR. Therefore, an automated and more accurate approach is needed to extract SDoH and identify SUDs. Natural language processing (NLP) can unlock the information conveyed in clinical narratives, thus playing a critical role in real-world studies. Methods and tools are being developed to facilitate such extractions; however, these tools are still under study for cohort-specific samples and unstructured text analytics is complex. To advance health disparity studies and improve understanding of patient characteristics of the SUD patients from the underserved population at Meharry, it is a high priority to explore machine learning tools to extract SDoH factors and identify SUDs diagnosis. In this proposal, we will address the challenge of SDoH extraction and SUDs identification from unstructured clinical notes or patient surveys to generate a consistent framework that can aid in identifying, understanding, treating, and predicting SUDs and associated outcomes (e.g. relapse). We will solve this challenge by developing two NLP pipelines, one for SDoH extraction and one for SUDs identification. We will focus on SUD patients with cocaine, cannabis, and opioid use disorders. The SDoH NLP pipeline will mine and enrich data in five SDoH domains defined by CDC including economic stability, education, health care access, neighborhood environment, and social and community context. Name entity recognition methods will be investigated in the SUDs NLP pipeline. We will develop and test the two pipelines in a cohort of 200-500 patients and compare our pipeline-derived results to manual review outcome to measure the NLP model performance. We hypothesize that a high performance SDoH NLP pipeline will be developed that will fit to our application, and more SUD patients will be identified. The ultimate goal of our studies is to identify patients at risk of SUDs, identify risk factors associated with health outcomes, and improve patient health outcome prediction that will potentially help clinical decision support and healthcare management. In order to accomplish this, we will integrate the SDoH associated factors and SUD diagnosis that we obtained from the two NLP pipelines, along with other phenotype risk factors being extracted from EHR structured data. We hypothesize that the performance of the SUD predictive model will be increased after SDoH are included.

项目总结 物质使用障碍(Suds)是一个重大的公共卫生问题，最近在 过去几年在美国人中的流行率，从2018年的2000万人影响到4630万人 在2021年。受SUD影响的人不成比例地经历了负面的健康社会决定因素(SDoH)， 包括缺乏获得安全住房、交通、教育、就业机会和营养的机会 食物。这些SDoH与低自尊、自我效能感和尝试SUD禁欲的失败有关。 收集和整合SDoH信息作为患者电子健康记录(EHR)的一部分供临床使用 建模可以帮助揭示与SUD相关的患者经历和行为，但大多数SDoH是 嵌入在非结构化自由文本中。此外，结构性EHR中对SUD的诊断不足。 因此，需要一种自动化和更准确的方法来提取SDoH和识别SUD。天然 语言处理(NLP)可以解锁临床叙述中所传达的信息，因此起着至关重要的作用 在现实世界的研究中。正在开发方法和工具来促进这种提取；然而，这些工具 仍在研究特定于队列的样本，非结构化文本分析很复杂。增进健康 差异研究和提高对服务不足的SUD患者的患者特征的理解 在Meharry，探索机器学习工具来提取SDoH因素并识别 肥皂泡诊断。在本提案中，我们将解决SDoH提取和SoDS识别的挑战 从非结构化的临床记录或患者调查中生成一致的框架，以帮助 识别、了解、治疗和预测肥胖症及相关结果(例如复发)。我们 将通过开发两条NLP管道来解决这一挑战，一条用于SDoH提取，另一条用于SODS 身份证明。我们将重点关注有可卡因、大麻和阿片类药物使用障碍的SUD患者。SDoH NLP 管道将挖掘和丰富CDC定义的五个SDoH领域的数据，包括经济稳定、教育、 卫生保健可获得性、邻里环境以及社会和社区背景。名称实体识别 这些方法将在SODS NLP管道中进行调查。我们将在一个队列中开发和测试这两条管道 200-500名患者，并将我们的管道得出的结果与手动审查结果进行比较，以衡量NLP模型 性能。我们假设将开发一种高性能的SDoH NLP管道，该管道将适合我们的 应用，将识别更多的SUD患者。我们研究的最终目标是在 肥胖症的风险，确定与健康结果相关的风险因素，并改善患者的健康结果 这一预测可能有助于临床决策支持和医疗保健管理。为了完成 这，我们将结合SDoH相关因素和我们从两个NLP获得的SUD诊断 管道，以及从EHR结构化数据中提取的其他表型风险因素。我们假设 加入SDoH后，SUD预测模型的性能将得到提高。