Research

• This research advances our current work in large language models (LLMs) to define a customized intervention optimization model to include key self-regulation responses directly based on user’s input through trauma (fight, flight, freeze, and fawn) classification.
• Optimization of the model incorporates teams of licensed adult/child psychiatrists and psychologists to validate the model’s output and policy based on RLHF.

• Research advances our work on our August 2024 paper titled  “Advancing Health Literacy through Generative AI: The Utilization of Open-source LLMs for Text Simplification and Readability” to further improve enhanced simplifying of words from the Living Word Vocabulary + API lookup and simplification of new words and their respective meanings by grade reading level to enhance user comprehension.
• Enhancing Mental Health Intervention Efficacy through Multi-Source Data Integration and Advanced Machine Learning Models
• This research focuses on developing ML/DL models to evaluate the effectiveness of mental health interventions by analyzing integrated data from mobile apps, wearable devices, and self-reported assessments.

• Development of deep learning models to assess resource utilization, evaluate cost-effectiveness, and dynamically adjust interventions based on real-time user feedback.

• Research encompasses the objective of developing ML/DL models to generate cost-effective data, reports, and policies for optimizing mental health care across various stakeholders, including insurance companies, healthcare providers, and government agencies.

• Research reflects the key themes and challenges outlined in the problem statements, such as data interoperability, security, and the integration of Internet of Medical Things (IoMT) devices. It also highlights the proposed solutions involving AI and blockchain technologies.

In collaboration with Henry Ford Hospital, the goal of this research project is to develop a deep learning model capable of automated early detection of low bone mass on existing radiographic images of the knee. Our hope is through opportunistic radiographic screenings, such as of the knee, we can develop a deep learning algorithm which provides accurate information to identify if a patient has low bone mass and should be recommended for treatment options. Therefore, our hypothesis is that the use of this methodology will result in reduced imaging, leading to improved patient safety through less exposure to radiation, the reduction of healthcare imaging costs, and a reduction in the time to diagnosis and appropriate treatment for low bone mass.
Improving Classification of Benign Appearing Lung Nodules using Artificial Intelligence and Radiomics.

This collaborative research project with Henry Ford Hospital aims to fill a critical gap in lung cancer screening by harnessing radiomic features to enhance the predictive power of the Lung-RADS classification. By identifying low-risk nodules with higher malignancy potential, we can enable earlier and more tailored interventions, potentially improving patient survival rates and reducing the burden of lung cancer. Additionally earlier detection of malignant transformation may allow for intervention with targeted radiation therapies which might avoid more invasive surgical options. This project represents a novel approach to lung cancer screening, integrating radiomic analysis with existing classifications. By leveraging cutting-edge machine learning techniques to analyze imaging data, this research could lead to a paradigm shift in how we assess and manage lung nodule risk, making lung cancer screening more precise and personalized.

This collaborative research project with Henry Ford Hospital aims to fill a critical gap in lung cancer screening by harnessing radiomic features to enhance the predictive power of the Lung-RADS classification. By identifying low-risk nodules with higher malignancy potential, we can enable earlier and more tailored interventions, potentially improving patient survival rates and reducing the burden of lung cancer. Additionally earlier detection of malignant transformation may allow for intervention with targeted radiation therapies which might avoid more invasive surgical options. This project represents a novel approach to lung cancer screening, integrating radiomic analysis with existing classifications. By leveraging cutting-edge machine learning techniques to analyze imaging data, this research could lead to a paradigm shift in how we assess and manage lung nodule risk, making lung cancer screening more precise and personalized.

In collaboration with a nonprofit hospital network, Dr. Hembroff’s team is working on developing a full stack secure web-based patient registry for disease surveillance. Through FHIR interoperability and data-driven insights, the project enables real-time decision-making and policy development with rich visualizations aimed to improve public health outcomes and safety.

This collaborative project focuses on creating a secure patient registry for use in integrated healthcare settings. The registry records valuable information about patients’ behavioral health, so care management personnel can make informed decisions. By translating data into critical visualizations and analytics, the project enhances healthcare and outcomes for patients.

In a public health monitoring collaborative project, Dr. Hembroff has been leading the development of public health software at Michigan Technological University by utilizing crowdsourced data to provide real-time mapping of tick disease monitoring. This initiative, supported by the MI-SAPPHIRE grant, involves engaging the public in collecting ticks from Michigan's Upper Peninsula and nearby regions. The collected ticks are analyzed to generate up-to-date geolocation data, which is then visualized on an interactive dashboard. This approach not only focuses on data collection but also aims to educate the public on tick identification, associated risks, preventive measures, and treatment options. The integration of citizen science, technology, and education in this project creates a robust public health safety net, ensuring communities are well-informed and proactive in addressing tick-borne illnesses.