The research of Lixia Yao, Ph.D., focuses on mining, integrating, visualizing and transforming structured and unstructured textual data in health care. Dr. Yao distills electronic health records, patient secure messages, claims databases, literature, patents and social media data into meaningful biomedical knowledge and informatics applications.
- Quantifying unmet medical needs. Even as population health improves and longevity increases globally, there will always be more biomedical problems than solutions. Strategically prioritizing and allocating the limited societal resources to discover and develop cost-effective pharmaceuticals, medical devices and other diagnostics for diseases and medical conditions with the highest return on investment motivates many governmental and private funding agencies, pharmaceutical and biotech companies, clinicians, and scientists. Unfortunately, due to the complexity of the biomedical research ecosystem and the scarcity of relevant data, no systematic studies have been done to comprehensively survey the past allocation of resources (funding, attention from the scientific community and clinical development, for example) or guide the future redistribution of resources for maximal societal benefit. Dr. Yao develops quantitative metrics, namely disease-specific Research Opportunity Index (ROI) and Public Health Index (PHI), to gauge the imbalance between the disease burden associated with a particular disease or all medical conditions as a whole and the associated resource allocation over time, which is a critical step for various stakeholders along the health care value chain to better distribute resources and prioritize efforts.
- Mining patient-reported outcomes from heterogeneous data sources. One challenge the U.S. health care system faces is that the needs, values and preferences of individual patients are not incorporated in medical decision-making. Patients thus become less engaged and adherent with their treatment plans, receive suboptimal outcomes, and feel unsatisfied with their care. As a whole, the health care system becomes underperforming, despite escalating costs. Dr. Yao aims to analyze patient-reported outcomes and experiences data in electronic health records and social media platforms (such as self-reported health-related quality of life, symptoms, functions, satisfaction with care, adherence to medications or other therapies, and perceived value of treatment improve patient care) and provide such feedback to clinicians to promote shared decision-making.
- Developing informatics applications for characterizing drug targets, quantifying drug toxicity and identifying drug-repositioning opportunities. Drug discovery and development is a lengthy and expensive process, generally taking about 12 years and costing $1.2 billion to bring a new drug to patients. Early in her career, Dr. Yao proposed to use data-driven computational approaches to accelerate the drug discovery and development process, with the ultimate goal of bringing more-effective, safer and more-affordable drugs to patients in a shorter time. More specifically she developed databases and bioinformatics tools to characterize drug targets, quantify drug toxicity and identify drug-repositioning opportunities.
Significance to patient care
Dr. Yao's work in biomedical informatics accelerates the pace of biomedical knowledge discovery and implementation, with the ultimate goal of improving the availability and outcomes, and reducing costs of U.S. health care.
- Editorial board member, JAMIA Open, 2018-present
- Vice chair, AMIA Knowledge Discovery and Data Mining Working Group, 2018-present
- Academic editor, PLOS One, 2015-2018
- Associate editor, BMC Medical Informatics and Decision Making, 2017
- Early career reviewer, Biomedical Computing and Health Informatics Study Section, Center for Scientific Review, National Institutes of Health, 2017
- Best Paper Award, Data Mining in Biomedical Informatics and Healthcare Workshop, International Conference on Data Mining, IEEE, 2015
- Recipient, Annual Student Research Award, Nucleic Acids Research, Oxford University Press, 2008