artificial intelligence for the public’s health
Researchers in the College of Public Health and Health Professions are using artificial intelligence tools to improve population health and treatment interventions.
Studies led by faculty in both public health and health professions disciplines explore real-world health issues, including the outcomes of pharmaceutical treatments in large populations, the effects of re-purposing drugs for other health conditions, the impact of environmental contaminants on the risk of diseases, such as cancer, and the use of assistive technology to improve daily life for older adults and people with disabilities.
Scientists are developing fair and equitable models that not only recognize social bias and health disparity, but also can be acted upon in interventions. Examples include increasing access to care for vulnerable and underserved populations and reducing stigma in order to improve quality of life for people living with HIV.
Research across disciplines includes fusing molecular epidemiology and deep learning methods to track and curb transmission of infectious diseases, as well as AI-empowered neurocognitive research.
Methodological approaches include advancements in machine learning and “causal AI” with strong biostatistical foundations, such as efficient multi-omics big data analysis, deep propensity networks, automated learning of causal effects from large-scale electronic health records, multi-site large clinical trials, and Bayesian dynamic trials.
Dr. Indahlastari serves as a research assistant professor of clinical and health psychology. Her broader research interests are in optimizing and personalizing existing medical devices through the use of computational modeling, such as machine learning and finite element methods, with the goal of achieving precision medicine that is tailored to each person.
Dr. Benos is a professor in the department of epidemiology. His group works on the intersection of machine learning, computational biology and systems medicine. The ultimate goal of the group is to identify risk factors and mechanisms affecting aging and contributing to the onset and progression of chronic diseases and cancer. They develop and use probabilistic graphical models and other machine learning methods to integrate and mine high-dimensional, multi-modal biomedical data and to investigate biological processes pertinent to health and disease. The disease focus of the lab includes chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, cardiovascular diseases and alcoholic hepatitis. Other ongoing projects are related to the identification of microbiome contributions to clinical outcomes in critically ill patients and the understanding of the mechanisms of cancer immunoprevention.
As an incoming associate professor in the department of biostatistics, Dr. Chen focuses on developing deep learning and statistical methods and software for analyzing large-scale multi-omics data, which include but are not limited to genetics, single-cell genomics and metagenomics. In particular, he is interested in applying the developed methods to study aging and cancer, as well as disseminating the developed software for public health researchers to use.
Dr. Gullett is a research assistant professor in the department of clinical and health psychology. His research is focused on the use of machine learning methods to predict intervention outcomes and disease progression in older adults with mild cognitive impairment, and the relationship of white matter microstructure with clinical disorders and their associated neuropsychological function.
Dr. Hammarlund is an assistant professor in the department of health services research, management and policy. His research merges health economics with innovations in artificial intelligence to investigate the role of social factors in the delivery of healthcare with the goal to better target policy solutions to disparities in health.
Dr. Liang is an assistant professor in the department of biostatistics. In his research, he applies statistical and machine learning techniques to large databases like electronic health records, to help health care providers make decisions based on patient-level information. These may include decisions about treatment, tailored cancer surveillance strategy and individualized risk prediction.
As an associate professor in the department of environmental and global health, Dr. Lin’s research focuses on the development and application of computational technologies to address research questions related to nanomedicine, animal-derived food safety assessment, and environmental chemical risk assessment. The long-term goal is to develop AI-assisted computational approaches to support decision-making in human, animal and environmental health.
An associate professor in the department of biostatistics, Dr. Xiao focuses on the development and application of powerful and efficient statistical methods for high throughput genetics and genomics data. Her work includes ongoing projects in cancer, aging and other public health related outcomes, with the goal of providing efficient statistical tools to integrate genetic and genomic data into the practice of precision medicine.
Dr. Mattia Prosperi and colleagues are using an AI technique known as deep learning to study patterns of HIV transmission. Deep learning methods use artificial neural networks that learn from complex data sets. The researchers plan to identify social, demographic and behavioral risk profiles that will enable more powerful predictions about future trends, including where HIV transmission clusters are likely to occur.
Dr. Adam Woods studies the use of non-invasive transcranial direct current stimulation for improving brain health among older adults. With support from a new grant, Woods and his team are using neuroimaging-derived computational modeling and artificial intelligence-based machine learning methods to better understand the mechanisms of treatment response and to develop precise individualized models for dosing.
The college has launched a certificate program for undergrads titled Artificial Intelligence in Healthcare and Public Health. The nine-credit certificate includes three courses: “Higher Thinking for Healthy Humans: AI in Healthcare and Public Health,” “Ethics in AI: Who’s Protecting Our Health” and “Data Visualization in the Health Sciences.”
The college is developing a certificate in Artificial Intelligence Research Methodologies in Healthcare and Public Health for graduate students that will focus on using AI to answer health-related research questions. Once these are fully established, the courses will be developed and submitted to the Graduate Council for approval.
Prosperi will work with the associate deans for research and education and the department chairs to expand PHHP’s AI efforts in research, teaching…
Her fellowship project will use large-scale electronic health records databases for prognostic, diagnostic and treatment outcome prediction studies.
To combat antibiotic misuse, researchers developed a decision-making tool that tells doctors the probability the culprit is solely a virus.
For more information on AI activities at PHHP, contact the college’s coordinator for AI, Dr. Mattia Prosperi.
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