My research is focused on the human biometric data (such as motion) to guide the design and manufacturing of assistive and proactive devices. Embedded and external sensors generate ample data which require scientific approaches to analyze and create knowledge. I have worked closely with the University of Michigan Orthotics and Prosthetics Center in the design and manufacturing of custom assistive devices using 3D-printing and cyber-based design. The goal is to create a cyber-physical system that can acquire the data from scanning, sensors, human motion, user feedback, clinician diagnosis into quantitative health metrics and guidelines to improve the quality of care for people with needs.
Our team develops machine learning algorithms for the enhancement of outcomes in cataract surgery, the most commonly performed surgery in the world. Our works focuses on developing models for postoperative refraction after cataract surgery and analysis of surgical quality.
Professor Kowalski’s recent research analyzes experiments and clinical trials with the goal of designing policies to target insurance expansions and medical treatments to individuals who stand to benefit from them the most. Her research has also explored the impact of previous Medicaid expansions, the Affordable Care Act, the Massachusetts health reform of 2006, and employer-sponsored health insurance plans. She has also used cutting-edge techniques to estimate the value of medical spending on at-risk newborns.
We have developed and tested machine learning approaches to integrate quantitative markers for diagnosis and assessment of progression of TMJ OA, as well as extended the capabilities of 3D Slicer4 into web-based tools and disseminated open source image analysis tools. Our aims use data processing and in-depth analytics combined with learning using privileged information, integrated feature selection, and testing the performance of longitudinal risk predictors. Our long term goals are to improve diagnosis and risk prediction of TemporoMandibular Osteoarthritis in future multicenter studies.
The Spectrum of Data Science for Diagnosis of Osteoarthritis of the Temporomandibular Joint
As a board-certified ophthalmologist and glaucoma specialist, I have more than 15 years of clinical experience caring for patients with different types and complexities of glaucoma. In addition to my clinical experience, as a health services researcher, I have developed experience and expertise in several disciplines including performing analyses using large health care claims databases to study utilization and outcomes of patients with ocular diseases, racial and other disparities in eye care, associations between systemic conditions or medication use and ocular diseases. I have learned the nuances of various data sources and ways to maximize our use of these data sources to answer important and timely questions. Leveraging my background in HSR with new skills in bioinformatics and precision medicine, over the past 2-3 years I have been developing and growing the Sight Outcomes Research Collaborative (SOURCE) repository, a powerful tool that researchers can tap into to study patients with ocular diseases. My team and I have spent countless hours devising ways of extracting electronic health record data from Clarity, cleaning and de-identifying the data, and making it linkable to ocular diagnostic test data (OCT, HVF, biometry) and non-clinical data. Now that we have successfully developed such a resource here at Kellogg, I am now collaborating with colleagues at > 2 dozen academic ophthalmology departments across the country to assist them with extracting their data in the same format and sending it to Kellogg so that we can pool the data and make it accessible to researchers at all of the participating centers for research and quality improvement studies. I am also actively exploring ways to integrate data from SOURCE into deep learning and artificial intelligence algorithms, making use of SOURCE data for genotype-phenotype association studies and development of polygenic risk scores for common ocular diseases, capturing patient-reported outcome data for the majority of eye care recipients, enhancing visualization of the data on easy-to-access dashboards to aid in quality improvement initiatives, and making use of the data to enhance quality of care, safety, efficiency of care delivery, and to improve clinical operations. .
Most of my research related to data science involves decision making around clinical trials. In particular, I am interested in how databases of past clinical trial results can inform future trial design and other decisions. Some of my work has involved using machine learning and mathematical optimization to design new combination therapies for cancer based on the results of past trials. Other work has used network meta-analysis to combine the results of randomized controlled trials (RCTs) to better summarize what is currently known about a disease, to design further trials that would be maximally informative, and to study the quality of the control arms used in Phase III trials (which are used for drug approvals). Other work combines toxicity data from clinical trials with toxicity data from other data sources (claims data and adverse event reporting databases) to accelerate detection of adverse drug reactions to newly approved drugs. Lastly, some of my work uses Bayesian inference to accelerate clinical trials with multiple endpoints, learning the link between different endpoints using past clinical trial results.
Dr. Douville is a critical care anesthesiologist with an investigative background in bioinformatics and perioperative outcomes research. He studies techniques for utilizing health care data, including genotype, to deliver personalized medicine in the perioperative period and intensive care unit. His research background has focused on ways technology can assist health care delivery to improve patient outcomes. This began designing microfluidic chips capable of recreating fluid mechanics of atelectatic alveoli and monitoring the resulting barrier breakdown real-time. His interest in bioinformatics was sparked when he observed how methodology designed for tissue engineering could be modified to the nano-scale to enable genomic analysis. Additionally, his engineering training provided the framework to apply data-driven modeling techniques, such as finite element analysis, to complex biological systems.
I use machine-learning techniques to implement decision support systems and tools that facilitate more personalized care for disease management and healthcare utilization to ultimately deliver efficient, effective, and equitable therapy for chronic diseases. To test and advance these general principles, I have built operational programs that are guiding—and improving—patient care in costly in low resource settings, including emerging countries.
Dr. Valley’s research focuses on understanding and improving decision-making in the intensive care unit (ICU). His primary line of research seeks to identify the patients most likely to benefit from intensive care, allowing clinicians to safely triage patients between the ICU and the general ward. Ultimately, he hopes to identify ICU-based therapies that can be transferred to the general ward to improve hospital efficiency and reduce healthcare costs. Dr. Valley’s research interests also include enhancing diagnosis in critical illness, improving the ICU experience for family members of ICU patients, and reducing barriers to cost-effective pulmonary and critical care.