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OBJECTIVE - 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.
METHODS - After integrating negation detection to a locally developed natural language processor, the KnowledgeMap concept identifier, we evaluated NLP-based detection of QT prolongation compared to the calculated QTc on a set of 44,318 ECGs obtained from hospitalized patients. We also created a string query using regular expressions to identify QT prolongation. We calculated sensitivity and specificity of the methods using manual physician review of the cardiologist-generated reports as the gold standard. To investigate causes of "false positive" calculated QTc, we manually reviewed randomly selected ECGs with a long calculated QTc but no mention of QT prolongation. Separately, we validated the performance of the negation detection algorithm on 5000 manually categorized ECG phrases for any medical concept (not limited to QT prolongation) prior to developing the NLP query for QT prolongation.
RESULTS - The NLP query for QT prolongation correctly identified 2364 of 2373 ECGs with QT prolongation with a sensitivity of 0.996 and a positive predictive value of 1.000. There were no false positives. The regular expression query had a sensitivity of 0.999 and positive predictive value of 0.982. In contrast, the positive predictive value of common QTc thresholds derived from ECG machines was 0.07-0.25 with corresponding sensitivities of 0.994-0.046. The negation detection algorithm had a recall of 0.973 and precision of 0.982 for 10,490 concepts found within ECG impressions.
CONCLUSION - NLP and regular expression queries of cardiologists' ECG interpretations can more effectively identify QT prolongation than the automated QTc intervals reported by ECG machines. Future clinical decision support could employ NLP queries to detect QTc prolongation and other reported ECG abnormalities.
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. The algorithm for negation detection had a recall of 0.973 and precision of 0.982 over 10,490 concepts. A concept query for QT prolongation matched 2,364 ECGs with precision of 1.00. The positive predictive value of the common QTc cutoffs was 6-21%. ECGs not identified by KMCI as prolonged but with QTc>450ms revealed potential causes of miscalculated QTc intervals in 96% of the cases; no definite concept query false negatives were detected. We conclude that a natural language processing system can effectively identify QT prolongation and other cardiac diagnoses from ECG impressions for potential decision support and clinical research.
CONTEXT - Although patient safety is a major problem, most health care organizations rely on spontaneous reporting, which detects only a small minority of adverse events. As a result, problems with safety have remained hidden. Chart review can detect adverse events in research settings, but it is too expensive for routine use. Information technology techniques can detect some adverse events in a timely and cost-effective way, in some cases early enough to prevent patient harm.
OBJECTIVE - To review methodologies of detecting adverse events using information technology, reports of studies that used these techniques to detect adverse events, and study results for specific types of adverse events.
DESIGN - Structured review.
METHODOLOGY - English-language studies that reported using information technology to detect adverse events were identified using standard techniques. Only studies that contained original data were included.
MAIN OUTCOME MEASURES - Adverse events, with specific focus on nosocomial infections, adverse drug events, and injurious falls.
RESULTS - Tools such as event monitoring and natural language processing can inexpensively detect certain types of adverse events in clinical databases. These approaches already work well for some types of adverse events, including adverse drug events and nosocomial infections, and are in routine use in a few hospitals. In addition, it appears likely that these techniques will be adaptable in ways that allow detection of a broad array of adverse events, especially as more medical information becomes computerized.
CONCLUSION - Computerized detection of adverse events will soon be practical on a widespread basis.
For Interactive Patient II, a multimedia case simulation designed to improve history-taking skills, we created a new natural language interface called GRASP (General Recognition and Analysis of Sentences and Phrases) that allows students to interact with the program at a higher level of realism. Requirements included the ability to handle ambiguous word senses and to match user questions/queries to unique Canonical Phrases, which are used to identify case findings in our knowledge database. In a simulation of fifty user queries, some of which contained ambiguous words, this tool was 96% accurate in identifying concepts.