A computer scientist from the University of Texas at Arlington is developing a way to mine patients’ electronic medical record (EMR) data. Being able to exploit this wealth of data, which is already available but is very difficult to access, could make a significant difference in personalising medicine for patients.
Funded by a $461,098 grant, the National Science Foundation research project, led by Heng Huang of the university’s computer science & engineering department, is designed to use existing information in patients records to predict healthcare needs and identify the risks that can lead to readmission
“If collecting and deciphering this data can give doctors better information so they can give patients better health care, it will make a big difference,” Huang said. “We especially want to predict possible readmission dates for heart failure patients because timing is extremely important to them. It can be the difference between life and death.”
According to Huang, the project could help to maintain the balance between the best length of stay in hospital for a patient, and the need to control hospital costs.
Many groups are looking at using data mining and big data technologies to extract data from existing information stores. Technologies like GenoKey‘s, which are designed to find patterns in data using combinatorial techniques to analyse huge and complex datasets, have potential to revolutionise medicine, both on the lab bench and in the clinic.