Patient deterioration detection using one-class classification via cluster period estimation subtask

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50021114" target="_blank" >RIV/62690094:18470/24:50021114 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0020025523015608" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025523015608</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ins.2023.119975" target="_blank" >10.1016/j.ins.2023.119975</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Patient deterioration detection using one-class classification via cluster period estimation subtask

Popis výsledku v původním jazyce

Deterioration is the significant degradation of the physical state prior to death. Detecting the deterioration of patients could provide an early warning to their families in instances of homecare, to clinicians treating hospitalized patients and to nurses by clients of retirement homes. Traditional supervised machine learning is not helpful for this purpose because the deterioration has individual differences for each patient, and the model cannot access the information about deterioration from healthy patients. This paper applies one-class classification (OCC) to detect deterioration changes. OCC can provide an early warning because the model can learn from only normal conditions. In particular, a one-class time-series classification (OCTSC) algorithm has been developed by combining K-means clustering with sliding windows and a linear regression subtask. The core idea is to detect the change in the signal period related to heart/breathing rate. For this purpose, clustering is applied to sliding windows, and the period is estimated using linear regression for the time index of arbitrary cluster. The deterioration change is detected by unseen scores computed as the error of linear regression subtask for cluster index.

Název v anglickém jazyce

Patient deterioration detection using one-class classification via cluster period estimation subtask

Popis výsledku anglicky

Deterioration is the significant degradation of the physical state prior to death. Detecting the deterioration of patients could provide an early warning to their families in instances of homecare, to clinicians treating hospitalized patients and to nurses by clients of retirement homes. Traditional supervised machine learning is not helpful for this purpose because the deterioration has individual differences for each patient, and the model cannot access the information about deterioration from healthy patients. This paper applies one-class classification (OCC) to detect deterioration changes. OCC can provide an early warning because the model can learn from only normal conditions. In particular, a one-class time-series classification (OCTSC) algorithm has been developed by combining K-means clustering with sliding windows and a linear regression subtask. The core idea is to detect the change in the signal period related to heart/breathing rate. For this purpose, clustering is applied to sliding windows, and the period is estimated using linear regression for the time index of arbitrary cluster. The deterioration change is detected by unseen scores computed as the error of linear regression subtask for cluster index.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information sciences

ISSN

0020-0255

e-ISSN

1872-6291

Svazek periodika

657

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

"Article Number: 119975"

Kód UT WoS článku

001138331600001

EID výsledku v databázi Scopus

2-s2.0-85180414570