title: Categorizing medications from unstructured clinical notes.
abstract: One of the important pieces of information in a patient's clinical record is the information about their medications. Besides administering information, it also consists of the category of the medication i.e. whether the patient was taking these medications at Home, were administered in the Emergency Department, during course of stay or on discharge etc. Unfortunately, much of this information is presently embedded in unstructured clinical notes e.g. in ER records, History & Physical documents etc. This information is required for adherence to quality and regulatory guidelines or for retrospective analysis e.g. CMS reporting. It is a manually intensive process to extract such information. This paper explains in detail a statistical NLP system developed to extract such information. We have trained a Maximum Entropy Markov model to categorize instances of medication names into previously defined categories. The system was tested on a variety of clinical notes from different institutions and we achieved an average accuracy of 91.3%.
authors.affiliation: Siemens Medical Solutions, USA Inc., Malvern, PA 19355.
authors.forename: Faisal
authors.initials: F
authors.lastname: Farooq
authors.forename: Shipeng
authors.initials: S
authors.lastname: Yu
authors.forename: Vikram
authors.initials: V
authors.lastname: Anand
authors.forename: Balaji
authors.initials: B
authors.lastname: Krishnapuram