Tag: machine learning

Research from our center shows women and elderly at higher risk of dangerous drug interactions

Drug interaction network

A new study led by researchers in our center has found that women and older adults who use multiple prescription drugs are significantly more likely to be prescribed pills whose combination produces dangerous side effects.

The analysis, conducted in the Brazilian health care system and recently published in the journal npj Digital Medicine, revealed a 60 percent increased risk for adverse drug reaction in women compared to men — and a 90 percent increased risk in cases of medicines whose interaction is known to produce dangerous reactions. In older people, one in every four people prescribed multiple medicines over age 55 received drugs with an interaction — reaching one in every three for ages 70 to 79.… continue reading.

NIH Project to study Drug-Drug Interaction


Prof. Luis Rocha from CNETS at IU Bloomington, Prof. Lang Li from IUPUI Medical School, and Prof. Hagit Shatkay from the University of Delaware have been awarded a four-year, $1.7M grant from NIH/NLM to study the large-scale extraction of drug-Interaction from medical text. Drug-drug interaction (DDI) leads to adverse drug reactions, emergency room visits and hospitalization, thus posing a major challenge to public health. To circumvent risk to patients, and to expedite biomedical research, both clinicians and biologists must have access to all available knowledge about potential DDI, and understand both causes and consequences of such interactions. However, mere identification of interactions does not directly support such understanding, as evidence for DDI varies broadly, from reports of molecular interactions in basic-science journals, to clinical descriptions of adverse-effects in a myriad of medical publications. This project will develop tools that focus directly on large-scale identification and gathering of various types of reliable experimental evidence of DDI from diverse sources. The successful completion of the project will provide clinicians and biologists with substantiated knowledge about drug interactions and with informatics tools to obtain such information on a large-scale, laying the basis for preventing adverse drug reactions and for exploring alternative treatments. … continue reading.