Lung cancer is the leading cause of cancer death in the United States. Cigarette smoking causes 85% of lung cancer deaths, however only about 15% of smokers will develop lung cancer in their lifetime. Genetic variations can modify the effect of the exposure to cigarette smoke. Our lab studies genetic variations in enzymes that detoxify potent carcinogens from cigarette smoke (including nitrosamines such as NNK and NNAL). We have identified a whole gene deletion polymorphism in a carcinogen metabolizing gene (UGT2B17) that is associated with decreased carcinogen metab
Learning health care systems (LHCS) propose to advance health care by taking advantage of increasing efficiencies in data collection and analysis and information dissemination. A major premise of LHCS is broad and continuous access to patient data, extracted at the point of care, and stored and used primarily in de-identified form.
Now more than ever, electronic health records (EHRs) are generated in large quantities and diverse content. This explosion of information has naturally enabled powerful data analyses to potentially improve healthcare.
The main approaches for learning Bayesian networks can be classified as constraint-based, score-based or hybrid methods. Although high-dimensional consistency results are available for the constraint-based PC algorithm, such results have been lacking for score-based and hybrid methods, and most hybrid methods are not even proved to be consistent in the classical setting where the number of variables remains fixed.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) promise to provide a novel access channel for assistive technologies, including augmentative and alternative communication, and control of external devices for people with severe speech and physical impairments (SSPI). Research on the subject has been accelerating significantly in the last decade and the research community has taken great strides toward making EEG-based BCI a practical reality for individuals with SSPI.
Electronic health records (EHRs) now serve health care professionals in 95 percent of hospitals nationwide, a 9-fold increase from a decade ago.
The Public Health Dynamics Laboratory (PHDL) at the graduate school of Public Health at the University of Pittsburgh is a mathematical modeling group that uses computational methods to represent diseases and potential interventions. Initially funded through the Modeling Infectious Disease Agents Study (MIDAS), the PHDL has developed expertise in large scale, geospatially accurate agent-based modeling, primarily with a concentration on infectious diseases. These models require significant amounts of data, and we have championed he use of “synthetic” populations that statistically represent the real populations of interest. This talk will describe the ongoing modeling efforts at PHDL, our development of dynamic synthetic populations that age, marry, divorce and create social networks, and acquire non-infectious diseases, and describe our aspirational goals of the integration of individual data with complex, mechanistically-based models.
Many valuable datasets that could be used to counter epidemic threats are not used due to challenges in accessing and standardizing datasets, and in integrating data into novel analyses such as epidemic simulation. Our research aims to improve the acquisition, standardization, and integration of information about epidemic threats.