Clinical trial NCT06694181: Safe and Explainable AI
Official title: SAFE AND EXPLAINABLE AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Artifical Inteligence
Cardiology
Breast Cancer
Sepsis
Lead sponsor: Abramson Cancer Center at Penn Medicine
Sponsor class: OTHER
First posted: 2024-11-19
Enrollment: 300000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study will look at EMR data from cardiac, oncology, and patients at risk for acquiring sepsis.

Each group has clearly defined inclusion-exclusion criteria. For the machine learning model to predict ventricular arrhythmias and in-hospital cardiac arrest, we will construct patient cohorts consisting of adult (i.e., 18 years of age or older) patients admitted to Sickbay-accessible bed within a Penn Medicine hospital from 2020 to the present as well as patients identified as having experienced cardiac arrest using EHR records.

Brief summary: 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.


Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria 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.

Primary outcomes:
measure: Neurosymbolic Learning Algorithms
timeFrame: Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months
measure: Explanation Methods
timeFrame: 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
measure: Methods for Safety Guarantees
timeFrame: 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
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