As the adoption of Electronic Health Records (EHRs) increases in the USA, the complexity of EHR data is growing dramatically. EHR data now covers diverse information about patients, including diagnosis, medication, lab results, genomic information and clinical notes. However, such large volumes of information do not readily provide accurate and succinct patient representations for effective and customized healthcare.
The move to Electronic Health Records and rapidly expanding availability of health-related information, from billing data body sensors, is providing unprecedented opportunities for innovative data driven solutions to problems in personalized medicine and population health. However, there are many formidable challenges in using EHR data that have limited their utility for clinical research so far, including diverse populations, heterogeneous and noisy information, longitudinal data, interpretability, domain constraints, and privacy concerns.
