My lab’s research focuses on machine learning (ML), systems informatics (SI) and their application to health outcomes, decision support, interpretable and credible models for precision medicine and health equity. The ever-increasing volumes of health data, such as electronic medical records, created potential to sharpen human beings’ decision-making and reasoning in cancer research and translational oncology. ML approaches have been proposed to unravel potential relationship among the inputs, outputs, and system status of complex systems from them. However, using ML approaches to integrate, extract, and interpret these health data for the improvement of cancer prevention and care is still challenging. It is important to develop findable, accessible, interoperable, and reusable (FAIR) ML approaches for this goal by bringing technological, biomedical, and behavioral scientists together. Therefore, my lab’s research interests include, but not limited to, the following aspects:
Develop interpretable ML-based new approaches to identify and control bias in health datasets, explore causal mechanistic pathway(s) among patients’ biophysical characteristics and cancer, and evaluate the contribution of healthcare factors to health outcomes.
Develop new ML models to fuse repository of clinical, genomic, imaging, and patient-reported information as well as biospecimens from a large cohort of patients in improving the accuracy and credibility of individual patients’ health outcomes prediction and clinical decision support by integrating interpretable ML, deep learning, and SI approaches.
Develop physician-in-the-loop algorithms to bridge the gap between ML and clinical decision making by allowing biomedical experts to participate in the process of ML algorithms.
Develop new techniques to evaluate potential healthcare quality with different healthcare policies in a community and identify the most suitable policy to improve the community’s healthcare quality by integrating ML and SI approaches.
Moreover, my lab intends to collaborate with other clinical and scientific departments and apply our research to improve cancer prevention and cure.