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Titolo:
Generation and evaluation of intraoperative inferences for automated health care briefings on patient status after bypass surgery
Autore:
Jordan, DA; McKeown, KR; Concepcion, KJ; Feiner, SK; Hatzivassiloglou, V;
Indirizzi:
Columbia Univ, Dept Comp Sci, New York, NY 10027 USA Columbia Univ New York NY USA 10027 Dept Comp Sci, New York, NY 10027 USA
Titolo Testata:
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
fascicolo: 3, volume: 8, anno: 2001,
pagine: 267 - 280
SICI:
1067-5027(200105/06)8:3<267:GAEOII>2.0.ZU;2-3
Fonte:
ISI
Lingua:
ENG
Soggetto:
CRITICALLY ILL; APACHE-III; ANESTHESIA; INFORMATION; PREDICTION; MORTALITY; LANGUAGE;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Social & Behavioral Sciences
Clinical Medicine
Citazioni:
23
Recensione:
Indirizzi per estratti:
Indirizzo: Jordan, DA Columbia Univ, Dept Comp Sci, 450 Comp Sci Bldg,1214 Amsterdam Ave, New York, NY 10027 USA Columbia Univ 450 Comp Sci Bldg,1214 Amsterdam Ave New York NY USA 10027
Citazione:
D.A. Jordan et al., "Generation and evaluation of intraoperative inferences for automated health care briefings on patient status after bypass surgery", J AM MED IN, 8(3), 2001, pp. 267-280

Abstract

Objective: The authors present a system that scans electronic records fromcardiac surgery and uses inference rules to identify and classify abnormalevents (e.g., hypertension) that may occur during critical surgical points(e.g., start of bypass). This vital information is used as the content of automatically generated briefings designed by MAGIC, a multimedia system that they are developing to brief intensive care unit clinicians on patient status after cardiac surgery. By recognizing patterns in the patient record,inferences concisely summarize detailed patient data. Design: The authors present the development of inference rules that identify important information about patient status and describe their implementation and an experiment they carried out to validate their correctness, re data for a set of 24 patients were analyzed independently by the system and by 46 physicians. Measurements: The authors measured accuracy, specificity, and sensitivity by comparing system inferences against physician judgments, in cases where all three physicians agreed and against the majority opinion in all cases. Results: For laboratory inferences, evaluation shows that the system has an average accuracy of 98 percent (full agreement) and 96 percent (majority model). An analysis of interrater agreement, however, showed that physicians do not agree on abnormal hemodynamic events and could not serve as a goldstandard for evaluating hemodynamic events. Analysis of discrepancies reveals possibilities for system improvement and causes of physician disagreement. Conclusions: This evaluation shows that the laboratory inferences of the system have high accuracy. The lack of agreement among physicians highlightsthe need for an objective quality-assurance tool for hemodynamic inferences. The system provides such a tool by implementing inferencing procedures established in the literature.

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Documento generato il 26/01/20 alle ore 10:32:50