A case study of applying text analysis to identify possible adverse drug interactions: The case of Adalat (Nifedipine).

Abstract

Adalat (Nifedipine) is a calcium-channel blocker that is also used as an antihypertensive drug. The drug was approved by the US Food and Drug Administration in 1985 but was discontinued in 1996 on account, among other things, of interactions with other medications. Nonetheless, Adalat is still used in other countries to treat congestive heart failure. We examine all the congestive heart failure electronic health records of the largest medical center in Israel to discover whether, possibly, taking Adalat with other medications is associated with patient death. This study examines a semantic space built by running latent semantic analysis on the entire corpus of congestive heart failure electronic health records of that medical center, encompassing 8 years of data on almost 12,000 patients. Through this semantic space, the most highly correlated medications and medical conditions that co-occurred with Adalat were identified. This was done separately for men and women. The results show that Adalat is correlated with different medications and conditions across genders. The data also suggest that taking Adalat with Captopril (angiotensin-converting enzyme inhibitor) or Rulid (antibiotic) might be dangerous in both genders. The study thus demonstrates the potential of applying latent semantic analysis to identify potentially dangerous drug interactions that may have otherwise gone under the radar.