Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 31,795
- 试验地点
- 2
- 主要终点
- Identification of Patients with Severe Asthma
研究概览
简要总结
The study aims to to use new technologies (ML, AI, NLP), to autonomously identify moderate to severe asthma populations within an EHR system, describe differences in treatment patterns across different populations, and determine trial eligibility.
Primary Objectives Please ensure you detail primary objectives Aim 1. Determine and validate a diagnosis of severe asthma (SA) using predictive features obtained from the Scripps Health EHR.
- Aim 1a: Use ML applied to structured EHR data to predict SA. Use the opinion of 2 specialty-trained physicians and ATS guidelines to determine model accuracy.
- Aim 1b: Use NLP applied to unstructured text to predict SA. Determine model accuracy as above in Aim 1a.
- Aim 1c: Use a combination of ML applied to structured data to predict SA. Determine model accuracy as above in Aim 1a.
详细描述
Asthma is a heterogeneous disease. The heterogeneity of asthma is supported by clinical observations and genome wide association studies (GWASs) that have identified over 200 asthma susceptibility loci in the DNA. These genetic 'hot spots' are near inflammatory cytokines, growth factors, and other inflammatory proteins knowingly linked to airway inflammation, including cytokines IL-4, -5, -13, -25, -33, and TSLP.
Novel monoclonal antibody therapies have drastically changed the treatment of moderate-to-severe asthma. Novel monoclonal antibody therapies introduced in the last 7 years have greatly advanced treatment options for moderate-to-severe asthma patients. These therapies effectively reduce or eliminate severe exacerbations, prevent hospitalizations, and improve patients' quality of life. However, many severe asthma patients, particularly those living in underserved areas, are still being overtreated with steroids and undertreated with monoclonal antibodies.
The 21st Century Cures Act will Change the Landscape of Research. The 21st Century Cures Act reinforced the use of real-world data (RWD) and real-world evidence (RWE) to support clinical trials, aid in drug coverage decisions, develop national treatment guidelines as well as standardized decision support tools. An underutilized source of RWE/D are electronic health records (EHR). Machine Learning (ML), AI, and natural language processing (NLP) are developing technologies that will greatly advance our ability to leverage data in EHR systems.
The study aims to use new technologies (ML, AI, NLP), to autonomously identify moderate to severe asthma populations within an EHR system, describe differences in treatment patterns across different populations, and determine trial eligibility.
Primary Objectives Please ensure you detail primary objectives Aim 1. Determine and validate a diagnosis of severe asthma (SA) using predictive features obtained from the Scripps Health EHR.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 6 Years 至 85 Years（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •- Demographics: Males ~ 40%, Blacks ~ 5-10%, Hispanic ~15-30%, Urban ~80-90%
排除标准
- 未提供
研究组 & 干预措施
Severe Asthma
Patients with Severe or Uncontrolled Asthma
干预措施: Recommendation for the diagnoses and treatment of Severe Asthma (Other)
结局指标
主要结局
Identification of Patients with Severe Asthma
时间窗: 4 years
Identify patients with severe asthma and compare diagnoses to that of medical professionals
次要结局
未报告次要终点