SAFE AND EXPLAINABLE AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 300,000
- 试验地点
- 1
- 主要终点
- Neurosymbolic Learning Algorithms
研究概览
简要总结
While current AI technology is suitable for automating some repetitive clinical tasks, technical challenges remain in solving critical and gainful problems in the domains of patient and disease management. The proposed research seeks to address issues in medical AI, such as integrating medical knowledge effectively, making AI recommendations explainable to clinicians, and establishing safety guarantees.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Cardiology 18 years of age and older, admitted to any of the Penn Medicine hospitals from 2017 to the present. Sepsis 18 years of age at the time of presentation to an emergency department or admission to any Penn Medicine hospital from July 1, 2017, onward will be eligible as this represents the population at risk for acquiring sepsis Oncology 18 years of age and older with a diagnosis of invasive breast cancer (Stage 1-4) in the Penn Cancer registry
排除标准
- •All prediction models will exclude patients under the age of 18 from their patient data sets.
- •Cardiology Patients whose primary admission diagnosis was cardiac arrest Sepsis Those with pre-existing limitations on life-sustaining therapy will be excluded because their eligibility for sepsis definitions, care received, and outcomes, may be significantly and variably affected by pre-existing limitations on care. Oncology There are no other exclusions.
研究组 & 干预措施
Cardiology
The primary objective in this clinical case scenario is to evaluate an ML model utilizing real-time cardiac telemetry, as well as other clinical, demographic, and imaging structured data sources, among hospitalized, intensive care unit (ICU) patients to predict impending inhospital cardiac arrest, identify potentially reversible causes of cardiac arrest, and predict which patients may have impending cardiac arrest due to shockable rhythms i.e. ventricular tachycardia (VT) or ventricular fibrillation (VF).
干预措施: AI-PERSONALIZED CLINICAL DECISION SUPPORT (Other)
Oncology - Breast Cancer
The primary objective in this clinical case scenario is to evaluate an ML model utilizing structured and unstructured data from clinical, demographic, and tumor molecular and germline sequencing, among outpatients with cancer, to predict short-term mortality and/or symptom decline. The model for prediction to treatment response in breast cancer patients will be compared with two prognostic tools: 1) Conversation Connect, a previously validated machine learning mortality prediction tool that has been used at the University of Pennsylvania for routine clinical decision support, and 2) the Elixhauser Comorbidity Index, a comorbidity-based prognostic index used commonly in research and risk-adjustment.
干预措施: AI-PERSONALIZED CLINICAL DECISION SUPPORT (Other)
Sepsis
The primary objective in this clinical case scenario is to develop and evaluate an ML model that utilizes multidmodal clinical data (e.g., structured EHR data such as demographics, laboratory test results, and vital signs; unstructured EHR data including the text of clinical encounter notes and, where available, waveforms from real-time cardiac, hemodynamic, and respiratory monitoring devices) to predict the need for initiation of broad-spectrum antimicrobial therapy for hospitalized patients with sepsis. With a focus on implementable and explainable AI, we will produce well calibrated predictions that are also clinically meaningful at the bedside to aid real-time decision-making about diagnosis and treatment initiation. The model for timely diagnosis and intervention in sepsis will be compared with widely used commercial and open-source sepsis prediction models.
干预措施: AI-PERSONALIZED CLINICAL DECISION SUPPORT (Other)
结局指标
主要结局
Neurosymbolic Learning Algorithms
时间窗: Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months
Develop and evaluate novel algorithms for training neurosymbolic models. We will develop data- and compute-efficient algorithms for end-to-end training of neurosymbolic models. This task will reduce the burden on clinician experts to provide fine-grained labels on voluminous EHR data.
Explanation Methods
时间窗: Prototype and develop new explanation algorithms; 18 months. Derive certified guarantees for explanations; 18 months. Benchmark and evaluate the explanation algorithms; 24 months. Extend certificates to new properties and tasks; 30 months. Publ
We will develop new explainable AI techniques that come with verifiable guarantees. These guarantees will enable trust and transparency in AI at a fundamental level.
Methods for Safety Guarantees
时间窗: Prototype and develop new rule learning algorithms; 30 months. Scale rule learning algorithms to larger data settings; 36 months. Incorporate new primitives to express complex rules; 36 months. Implement rule learning algorithms on baseline tasks
We will develop new algorithms that can scalably extract complex logical rules governing safety within the data that have statistical guarantees. These techniques will be rooted in statistical analysis and assist users in identifying out of distribution data and detecting anomalies.
次要结局
未报告次要终点