3 research outputs found
Improving the Timeliness, Accuracy, and Completeness of Mortality Reporting Using FHIR Apps and Machine Learning
There are approximately 56 million deaths per year world-wide, with millions happening in the United States. Accurate and timely mortality reporting is essential for gathering this important public health data in order to formulate emergency response to epidemics and new disease threats, to prevent communicable diseases such as flu, and to determine vital statistics such as life expectancy, mortality trends, etc. However, accurate collection and aggregation of high-quality mortality data remains an ongoing challenge due to issues such as the average low frequency with which physicians perform death certification, inconsistent training in determining the causes of death, complex data flow between the funeral home, the certifying physician and the registrar, and non-standard practices of data acquisition and transmission. We propose a smart application for medical providers at the point-of-care which will use \glsfirst{fhir} to integrate directly with the medical record, provide the practitioner with context for the death, and use machine learning techniques to enable the reporting of an accurate and complete causal chain of events leading to the death.Ph.D
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
An estimated 180 papers focusing on deep learning and EHR were published
between 2010 and 2018. Despite the common workflow structure appearing in these
publications, no trusted and verified software framework exists, forcing
researchers to arduously repeat previous work. In this paper, we propose
Cardea, an extensible open-source automated machine learning framework
encapsulating common prediction problems in the health domain and allows users
to build predictive models with their own data. This system relies on two
components: Fast Healthcare Interoperability Resources (FHIR) -- a standardized
data structure for electronic health systems -- and several AUTOML frameworks
for automated feature engineering, model selection, and tuning. We augment
these components with an adaptive data assembler and comprehensive data- and
model- auditing capabilities. We demonstrate our framework via 5 prediction
tasks on MIMIC-III and Kaggle datasets, which highlight Cardea's human
competitiveness, flexibility in problem definition, extensive feature
generation capability, adaptable automatic data assembler, and its usability