Recruiting

Medical AI

Sponsor:

Abramson Cancer Center at Penn Medicine

Code:

NCT06694181

Conditions

Artifical Inteligence

Cardiology

Breast Cancer

Sepsis

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

Interventions

AI-PERSONALIZED CLINICAL DECISION SUPPORT

Study Details

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.

Conditions

Artifical Inteligence

Cardiology

Breast Cancer

Sepsis

Study ID

NCT06694181

Start date

Nov 29, 2025

Status verified date

Feb, 2026

Completion date

Nov, 2028

Anticipated

Primary completion date

Nov, 2028

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

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.

Study Design

Enrollment

300000 participants

Anticipated

Interventions and Outcome Measures

Arms

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).

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.

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.

Interventions

AI-PERSONALIZED CLINICAL DECISION SUPPORT

AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT

Primary outcome measure

  • Neurosymbolic Learning Algorithms [ Time Frame: Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months ]
  • Explanation Methods [ Time Frame: 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 ]
  • Methods for Safety Guarantees [ Time Frame: 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 ]

Central Contacts and Locations

Central contacts

Haideliza Soto Calderon

215-220-9425haideliza.soto-calderon@pennmedicine.upenn.edu

Nicholas Bishop

nicholas.bishop@pennmedicine.upenn.edu

Locations

Hospital of the University of Pennsylvania

Recruiting

Philadelphia, Pennsylvania, United States, 19104

Contacts

Haideliza Soto Calderon

215-237-4509haideliza.soto-calderon@pennmedicine.upenn.edu

More Information

Sponsor

Abramson Cancer Center at Penn Medicine

Last update posted

Feb 25, 2026

Last verified

Feb, 2026

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-08. This information was provided to ClinicalTrials.gov by Abramson Cancer Center at Penn Medicine on 2026-02-25.