Cristin-resultat-ID: 1900901
Sist endret: 25. mars 2021, 11:19
NVI-rapporteringsår: 2020
Resultat
Vitenskapelig artikkel
2020

A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

Bidragsytere:
  • Noha El-Ganainy
  • Ilangko Balasingham
  • Per Steinar Halvorsen og
  • Leiv Arne Rosseland

Tidsskrift

IEEE Access
ISSN 2169-3536
e-ISSN 2169-3536
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2020
Publisert online: 2020
Volum: 8
Open Access

Importkilder

Scopus-ID: 2-s2.0-85102789443

Klassifisering

Vitenskapsdisipliner

Medisinsk teknologi

Emneord

Medisinsk teknologi

HRCS

  • Helsekategori: 20 - Generell helserelevans
    Aktivitet: 5.3 - Medisinsk utstyr

Beskrivelse Beskrivelse

Tittel

A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

Sammendrag

Mean arterial pressure (MAP) is an important clinical parameter to evaluate the health of critically ill patients in intensive care units. Thus, the real time clinical decision support systems detecting anomalies and deviations in MAP enable early interventions and prevent serious complications. The state-of-the-art decision support systems are based on a three-phase method that applies offline training, transfer learning, and retraining at the bedside. Their applicability in critical care units is challenging with delay and inaccuracy. In this article, we propose a real time clinical decision support system forecasting the MAP status at the bedside using a new machine learning structure. The proposed system works in real time at the bedside without requiring the offline phase for training using large datasets. It thereby enables timely interventions and improved healthcare services. The proposed machine learning structure includes two stages. Stage I applies online learning using hierarchical temporal memory (HTM) to enable real time stream processing and provides unsupervised predictions. To the best of our knowledge, this is the first time it is applied to medical signals. Stage II is a long short-term memory (LSTM) classifier that forecasts the status of the patient's MAP ahead of time based on Stage I stream predictions. We perform a thorough performance evaluation of the proposed system and compare it with the state-of-the-art systems employing logistic regression (LR). The comparison shows the proposed system outperforms LR in terms of the classification accuracy, recall, precision, and area under the receiver operation curve (AUROC).

Bidragsytere

Noha El-Ganainy

  • Tilknyttet:
    Forfatter
    ved Institutt for elektroniske systemer ved Norges teknisk-naturvitenskapelige universitet

Ilangko Balasingham

  • Tilknyttet:
    Forfatter
    ved Intervensjonssenteret ved Oslo universitetssykehus HF
  • Tilknyttet:
    Forfatter
    ved Institutt for elektroniske systemer ved Norges teknisk-naturvitenskapelige universitet

Per Steinar Halvorsen

  • Tilknyttet:
    Forfatter
    ved Intervensjonssenteret ved Oslo universitetssykehus HF
  • Tilknyttet:
    Forfatter
    ved Intervensjonssenteret ved Universitetet i Oslo

Leiv Arne Rosseland

  • Tilknyttet:
    Forfatter
    ved Akuttklinikken ved Universitetet i Oslo
  • Tilknyttet:
    Forfatter
    ved Avdeling for FoU, Akuttklinikken ved Oslo universitetssykehus HF
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