Automatic quality of life prediction using electronic medical records.

Health related quality of life (HRQOL) is an important variable used for prognosis and measuring outcomes in clinical studies and for quality improvement. We explore the use of a general pur-pose natural language processing system Metamap in combination with Support Vector Machines (SVM) for predicting patient responses on standardized HRQOL assessment instruments from text of physicians notes. We surveyed 669 patients in the Mayo Clinic diabetes registry using two instruments designed to assess functioning: EuroQoL5D and SF36/SD6.

A hybrid approach to determining modification of clinical diagnoses.

Health care providers that use electronic medical records maintain an administrative database of diagnoses generated by physicians in the course of medical care delivery. This database is subsequently used for billing and reimbursement but can also be used to identify patients for clinical research. In this paper we present a hybrid rule-based and machine learning technique for automatic determination of whether a diagnosis is confirmed, probable or represents a history of a disorder.

Abbreviation and acronym disambiguation in clinical discourse.

Use of abbreviations and acronyms is pervasive in clinical reports despite many efforts to limit the use of ambiguous and unsanctioned abbreviations and acronyms. Due to the fact that many abbreviations and acronyms are ambiguous with respect to their sense, complete and accurate text analysis is impossible without identification of the sense that was intended for a given abbreviation or acronym. We present the results of an experiment where we used the contexts harvested from the Internet through Google API to collect contextual data for a set of 8 acronyms found in clinical notes at the Mayo Clinic.