Work

Building ICU artifact detection models with more data in less time.

Year: 2001

Type: article

Abstract: As many as 86% of intensive care unit (ICU) alarms are false. Multiple signal integration of temporal monitor data by decision tree induction may improve artifact detection. We explore the effect of data granularity on model-building by comparing models made from 1-second versus 1-minute data. Models developed from 1-minute data remained effective when tested on 1-second data. Model development using 1-minute data means that more hours of ICU monitoring (including more artifacts) can be processed in less time. Compression of temporal data by arithmetic mean, therefore, can be an effective method for decreasing knowledge discovery processing time without compromising learning.

Source: Europe PMC (PubMed Central)

Authors: C L Tsien, I S Kohane, N McIntosh

Institutions: Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Affiliated Emergency Medicine Residency

Language: English

Topic: Healthcare Technology and Patient Monitoring

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W1659725278

FWCI: 0.389

Cites: 12

Cited by: 14

Related to: 10