Imaging genomics is an emerging data science field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal or disordered biological structures and functions, and to impact the development of new diagnostic, therapeutic and preventative approaches.
Technology to collect and analyse data relating to human health and behaviour will increasingly become a part of everyday life. For the first time, human kind has the opportunity to “time-travel” but not in a mechanical device but through simulation that will allow representations of the past (e.g.
Data science is accelerating the translation of biological and biomedical data to advance the detection, diagnosis, treatment, and prevention of diseases, including the recently announced BRAIN and Precision Medicine initiatives.
RRIDs are persistent unique identifiers that track the use of key biological resources such as antibodies, cell lines, organisms and software and data projects in the biomedical literature. Reproducibility is a very hard problem to solve, but there are some aspects that will improve reproducibility very quickly that we can implement today.
Health IT and particularly the electronic health record must meet many needs unique to healthcare, and this has typically been fraught with difficulty. This includes assisting work that is extremely complex, high-stakes, collaborative, rapidly changing, and with many stakeholders. Ongoing protests by provider groups at the national level have called attention to the difficulties in current systems, particularly with regard to safety and usability. This talk will describe a different, user-composable approach to the design of healthcare software, with details about our AHRQ-funded studies examining its advantages of human-computer interaction and cognition, safety, fit to task, communication/collaboration, and rapid change to meet emergent needs. It will also describe opportunities for future work which may be conducted at Pittsburgh.
In the world of ever growing biomedical data, patient care can be improved with multidisciplinary science including information science, medicine, genetics, and epidemiology. The concept of translational epidemiology has been defined as a fundamental science for moving laboratory discoveries into public health practice.
Physicians spend only about 33% percent of their time on direct clinical interactions and nearly 49% on EHR and desk work. In this talk I will discuss how technologies such as artificial intelligence and natural language technologies can help physicians to spend more time with the patient and less time in front of the computer. I will discuss some of M*Modals core artificial intelligence and natural language understanding technologies.