Research Focus - Information Technologies, Including Natural Language Processing and Machine Learning, to Support Cancer Prevention and Care of Cancer Patients
Identification of physicians’ electronic health record (EHR) use patterns and their relationship with downstream outcomes (e.g., smoking screening rates) by using data mining methods and EHR audit log data
Generation of automatic outcome measures using structured and unstructured EHR data to support implementation programs for cancer prevention
Identification of a smoker’s behavior change during eHealth intervention
Development and application of statistical and machine learning methods for predictive modeling related to cancer prevention and prognosis
In the News
Getting Results…
Study: E-cigarettes not associated with greater quitting success among traditional smokers
A study by UMass Medical School researchers found that cigarette smokers who used both e-cigarettes and combustible cigarettes smoked more combustible cigarettes daily and had less success at abstaining than smokers who only used combustible cigarettes.
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, Medicine, Cancer Center, Resident, Lung Cancer, Rajani Sadasivam, Jinying Chen,