Current Research

Research Spotlight

Decision Support Systems

Novel physiological monitoring systems producing highly informative signals and images, as well as machines collecting molecular data with high-throughput techniques, have provided researchers with a wealth of new knowledge. However, in order to conduct an in-depth study of the causes and mechanisms for many diseases and illnesses and design reliable decision support systems, the analysis of the data from a single high-throughput system or a single image/signal modality may not suffice. It has become evident that integration and processing of all sources of information, from molecular to macroscopic level is the only method of forming an accurate model and therefore generating reliable clinical recommendations.

On multiple clinical applications and in collaboration with researchers from diverse fields of research, we have been designing integrative frameworks to create computational models that integrate and process all typical sources of information including clinical images, clinical signals, proteomic and genomic data, and prior clinical knowledge, in order to form a single multi-faceted model of the process under study. Examples of such ensemble decision support systems include designing computational models for the wound healing process, signal processing methods to predict hemodynamic instability, computer aided decision support systems for traumatic brain injuries and computer aided decision support systems for traumatic pelvic/abdominal injuries.

Improving Care for Heart Failure Patients Using Tropical Geometry and Soft Computing

In this project, we aim to develop an automated decision-making system that can identify patients eligible for an HT/MCS that would facilitate primary care physicians or general cardiologists referring those patients for consideration of advanced therapies.

Project Lead: Yufeng Zhang, Oviyan Anbarasu

Clinical Leads: Jessie Golbus, Keith Aaronson

Inactive Project Team: Heming Yao, Jonathan Gryak, Renaid Kim, Harm Derksen, Shuyang Cheng, Cristian Minoccheri

Determining the appropriate timing of mechanical circulatory support (MCS) device implantation or heart transplantation (HT) for patients with advanced heart failure is essential as there may be a mortality cost to delayed care. Recognizing patients eligible for an HT/MCS requires expertise from providers trained in transplant cardiology. In this project, we aim to develop an automated decision-making system that can identify patients eligible for an HT/MCS that would facilitate primary care physicians or general cardiologists referring those patients for consideration of advanced therapies. We have developed a hybrid of fuzzy logic and neural networks with a genetic algorithm to identify patients that are eligible for HT/LVAD. Our model achieved better classification performance than other traditional ML algorithms. To overcome the optimization difficulty and computational burden in the existing model, we are developing a novel soft computing architecture with tropical geometry. The proposed network can be not only optimized by gradient-based methods but also close to piecewise linear to ensure the model’s interpretability.

Physiologic Closed Loop Control System for Diuretic Management

This project aims to develop a closed loop control system using explainable artificial intelligence methods to regulate diuretics for pediatric patients in the intensive care unit.

Project Lead: Matthew Hodgman

Clinical Lead: Daniel E. Ehrmann

Fluid overload is extremely harmful to patients and health systems and is associated with morbidity, mortality, and long-term sequalae that extend beyond discharge from the cardiac intensive care unit. Each day of failure to achieve net negative fluid balance after congenital cardiac surgery prolongs length of mechanical ventilation and hospital stay, which significantly increases hospital costs. The cornerstone of therapy for post-operative fluid overload is initiation and titration of diuretics (e.g., furosemide). However, this practice is imprecise and varies significantly between providers and heart centers, commonly leading to either under- or over-diuresis. Our multidisciplinary team proposes to solve this problem by building a system that improves how diuretic infusions are managed after congenital cardiac surgery. Specifically, we will leverage innovative computational techniques to develop a physiologic closed loop control system capable of automatically titrating a furosemide infusion to manage fluid overload. By using a PCLCS to titrate a diuretic infusion consistently and precisely, we aim to manage fluid overload more safely, effectively, and efficiently than the current standard of care.

Drug Development

The realm of drug discovery is undergoing a transformative evolution through the integration of machine learning. By fusing the precision of computational algorithms with the complexities of biochemical interactions, we are ushering in a new era of pharmaceutical innovation. Our approach involves leveraging vast datasets encompassing molecular structures, biological activities, and clinical outcomes to train sophisticated models. These models, powered by machine learning, unravel intricate patterns and hidden insights that human analysis alone might overlook. This synergy between artificial intelligence and pharmaceutical expertise expedites the identification of promising drug candidates, significantly expediting the research process. As we stand at the intersection of technology and medicine, our mission is to reshape the landscape of drug discovery, accelerating the delivery of safe and effective treatments to patients in need.

Prediction of Oncogenic Drug Combination Efficacy through Coupled Tensor-Tensor Completion

This project aims to utilize a coupled tensor-tensor completion method to predict the most effective combinations of cancer drug treatments for inhibiting tumor growth.

Project Lead: Morgen Henry

Oncogenic drug therapies are often combined to harness the synergistic effects of multiple medications while minimizing drug toxicity and resistance. However, clinically testing every possible drug combination is highly inefficient due to the significant time and resources required. To prioritize the most promising combinations for testing, computational methods can be employed to predict which combinations are likely to be most effective. Building on our previous work, a Feed-Forward Neural Network (FNN) that leveraged drug and tissue similarity within the National Cancer Institute’s drug combination efficacy dataset, this project proposes an enhanced approach using coupled tensor-tensor completion (CTTC). This method advances the FNN framework by retaining its foundational data (drug/tissue similarity) while improving prediction accuracy and computational scalability for identifying optimal drug combinations.

Generation of Novel Positive Allosteric Modulators for Opioid Receptors

This project aims to utilize generative AI to develop a novel non-addictive opioid alternative.

Project Lead: Emily Wittrup

Collaborators: Andrew Alt, Ph.D., Renju Jacob

Project Team: Ethan Schnathorst, Jett Rosen

The opioid crisis is a serious public health issue, causing over 80,000 overdose deaths in the U.S. in 2021 alone, which underscores the urgent need for safer pain medications that are not addictive. This project aims to transform the way drugs are designed using advanced artificial intelligence (AI) by combining scientific knowledge and medicinal chemistry. The main goal is to develop new molecules that boost the body’s natural pain relief system, providing effective pain relief without the risk of addiction linked to opioids. This project will create a new generative AI system that uses scientific rules to produce better solutions and a machine learning model that uncovers hidden scientific patterns, making the AI results more understandable and reliable. This cutting-edge approach will be used to create new pain relief medications which can then be tested in labs and iteratively improved.

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