Poor electronic medical record (EMR) usability is detrimental to both clinicians and patients. A better EMR would provide concise, context sensitive patient data, but doing so entails the difficult task of knowing which data are relevant.
Mobile technologies such as smartphones and wearable sensors offer unprecedented opportunities to sense and intervene on patient health behaviors in real-time, in real-world contexts, and at enormous scale. This talk will highlight recent and ongoing patient-centered research using mobile sensing and machine learning to predict clinical cancer outcomes such as readmissions and treatment toxicities.
Epidemics can and should be forecast, to improve decision making by governments, institutions and individuals. The goal of the Delphi group at Carnegie Mellon University is to make epidemiological forecasting as universally accepted and useful as weather forecasting is today.
The overarching goal of Dr. Jonassaint’s program of research is to improve behavioral and physical health and reduce health disparities by using mobile multimedia technology to deliver evidence-based interventions to underserved populations.
Genome scale molecular datasets are often highly structured, with many correlated observations. This general phenomenon can be related to the underlying data generating process.
Cancer is often associated with aberrant gene expression at the post-transcriptional level. However, it has not been fully understood how post-transcriptional regulation alters gene expression for cancer. Since hundreds RNAs interact with otherhundreds RNAs simultaneously at the level, their tumorigenic mechanisms need to be understood in consideration of the interactions.
The PRoBE laboratory for Pattern Recognition from Biomedical Evidence has been studying genomic, proteomic, imaging, microbiome and metabolomics data for early detection and monitoring of diverse diseases including cancers of the lung, breast and esophagus, as well as adverse cardiovascular events.