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).

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.

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.

AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT

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.

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.

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.