To describe the development and evaluation of computational tools to identify concepts within medical curricular documents, using information derived from the National Library of Medicine's Unified Medical Language System (UMLS). The long-term goal of the KnowledgeMap (KM) project is to provide faculty and students with an improved ability to develop, review, and integrate components of the medical school curriculum.

We developed the KnowledgeMap (KM) system as an online, concept-based database of medical school curriculum documents. It uses the KM concept indexer to map full-text documents and match search queries to concepts in the Unified Medical Language System (UMLS). In this paper, we describe the design of KM and report the first seven months of its implementation into a medical school. Despite being emphasized in only two first year courses and one fourth year course, students from all four classes used KM to search and browse documents.

Often, medical educators and students do not know where important concepts are taught and learned in medical school. Manual efforts to identify and track concepts covered across the curriculum are inaccurate and resource intensive.

Electrocardiogram (ECG) impressions represent a wealth of medical information for potential decision support and drug-effect discovery. Much of this information is inaccessible to automated methods in the free-text portion of the ECG report. We studied the application of the KnowledgeMap concept identifier (KMCI) to map Unified Medical Language System (UMLS) concepts from ECG impressions. ECGs were processed by KMCI and the results scored for accuracy by multiple raters. Reviewers also recorded unidentified concepts through the scoring interface.

Many authors have reported on the use of the Unified Medical Language System (UMLS) to match concepts in free text. Unmatched search strings may be due to misspellings, concepts not in the UMLS, or searches for words not expected to be in the UMLS (e.g., names of people or places). We mapped search strings from a full-text, concept-based curriculum database to UMLSconcepts and performed a failure analysis. The majority of unmatched text strings were medically related (71.7%).

Electrocardiogram (ECG) impressions provide significant information for decision support and clinical research. We investigated the presence of QT prolongation, an important risk factor for sudden cardiac death, compared to the automated calculation of corrected QT (QTc) by ECG machines. We integrated a negation tagging algorithm into the KnowledgeMap concept identifier (KMCI), then applied it to impressions from 44,080 ECGs to identify Unified Medical Language System concepts. We compared the instances of QT prolongation identified by KMCI to the calculated QTc.

To determine whether the integration of an automated electronic clinical portfolio into clinical clerkships can improve the quality of feedback given to students on their patient write-ups and the quality of students' write-ups.

Typically detected via electrocardiograms (ECGs), QT interval prolongation is a known risk factor for sudden cardiac death. Since medications can promote or exacerbate the condition, detection of QT interval prolongation is important for clinical decision support. We investigated the accuracy of natural language processing (NLP) for identifying QT prolongation from cardiologist-generated, free-text ECG impressions compared to corrected QT (QTc) thresholds reported by ECG machines.

Currently, many medical educators track trainee clinical experience using student-created manual logs. Using a web-based portfolio system that captures all notes written by trainees in the electronic medical record, we examined a graduating medical student's clinical notes to determine if we could automatically assess exposure to 10 institution-defined core clinical topics. We located all biomedical concepts in his clinical notes, divided by note section, using the KnowledgeMap concept identifier. Notes were ranked according to the concepts matching each core topic's concept list.

Clinical documentation is often expressed in natural language text, yet providers often use common organizations that segment these notes in sections, such as history of present illness or physical examination. We developed a hierarchical section header terminology, supporting mappings to LOINC and other vocabularies; it contained 1109 concepts and 4332 synonyms. Physicians evaluated it compared to LOINC and the Evaluation and Management billing schema using a randomly selected corpus of history and physical notes.