Development of an expert system for haemodynamic monitoring: Computerized symbolization of on-line monitoring data

Erkki Koski, Aki Mäkivirta, Tommi Sukuvaara, Aarno Kari

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The development of intelligent alarm systems for intensive care benefits from the transformation of data from a quantitative to a qualitative mode. We constructed a computerized algorithm for the symbolization of on-line monitoring data of heart rate, systemic arterial, pulmonary arterial and central venous pressures, as well as central and peripheral temperatures. We tested the ability of the algorithm to symbolize the levels of the parameters and to detect significant long-term trends in ten adult patients admitted to the intensive care unit after cardiac surgery. The estimations of an experienced clinician were taken as the ‘gold standard’. The symbolization of the levels of the monitored parameters was in agreement with the clinician in 99.4% of the estimations. The algorithm detected 93.0% of the trends correctly and also estimated their reliability. The clinician considered its estimations to be accurate in 96.2% of cases. On the other hand, the clinician considered unreliable 2.4% of all the trends detected and classified as reliable by the algorithm. The computerized algorithm for the symbolization of real-time monitoring data performed efficiently enough for its further use in expert systems for intelligent monitoring.
Original languageEnglish
Pages (from-to)289-293
Number of pages5
JournalJournal of Clinical Monitoring
Volume8
Issue number4
DOIs
Publication statusPublished - 1992
MoE publication typeA1 Journal article-refereed

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Expert Systems
Hemodynamics
Central Venous Pressure
Critical Care
Thoracic Surgery
Intensive Care Units
Heart Rate
Lung
Temperature

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Koski, Erkki ; Mäkivirta, Aki ; Sukuvaara, Tommi ; Kari, Aarno. / Development of an expert system for haemodynamic monitoring : Computerized symbolization of on-line monitoring data. In: Journal of Clinical Monitoring. 1992 ; Vol. 8, No. 4. pp. 289-293.
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abstract = "The development of intelligent alarm systems for intensive care benefits from the transformation of data from a quantitative to a qualitative mode. We constructed a computerized algorithm for the symbolization of on-line monitoring data of heart rate, systemic arterial, pulmonary arterial and central venous pressures, as well as central and peripheral temperatures. We tested the ability of the algorithm to symbolize the levels of the parameters and to detect significant long-term trends in ten adult patients admitted to the intensive care unit after cardiac surgery. The estimations of an experienced clinician were taken as the ‘gold standard’. The symbolization of the levels of the monitored parameters was in agreement with the clinician in 99.4{\%} of the estimations. The algorithm detected 93.0{\%} of the trends correctly and also estimated their reliability. The clinician considered its estimations to be accurate in 96.2{\%} of cases. On the other hand, the clinician considered unreliable 2.4{\%} of all the trends detected and classified as reliable by the algorithm. The computerized algorithm for the symbolization of real-time monitoring data performed efficiently enough for its further use in expert systems for intelligent monitoring.",
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Development of an expert system for haemodynamic monitoring : Computerized symbolization of on-line monitoring data. / Koski, Erkki; Mäkivirta, Aki; Sukuvaara, Tommi; Kari, Aarno.

In: Journal of Clinical Monitoring, Vol. 8, No. 4, 1992, p. 289-293.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Koski, Erkki

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AU - Sukuvaara, Tommi

AU - Kari, Aarno

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AB - The development of intelligent alarm systems for intensive care benefits from the transformation of data from a quantitative to a qualitative mode. We constructed a computerized algorithm for the symbolization of on-line monitoring data of heart rate, systemic arterial, pulmonary arterial and central venous pressures, as well as central and peripheral temperatures. We tested the ability of the algorithm to symbolize the levels of the parameters and to detect significant long-term trends in ten adult patients admitted to the intensive care unit after cardiac surgery. The estimations of an experienced clinician were taken as the ‘gold standard’. The symbolization of the levels of the monitored parameters was in agreement with the clinician in 99.4% of the estimations. The algorithm detected 93.0% of the trends correctly and also estimated their reliability. The clinician considered its estimations to be accurate in 96.2% of cases. On the other hand, the clinician considered unreliable 2.4% of all the trends detected and classified as reliable by the algorithm. The computerized algorithm for the symbolization of real-time monitoring data performed efficiently enough for its further use in expert systems for intelligent monitoring.

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