Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact.
AMIA Annu Symp Proc
; 2022: 405-414, 2022.
Article
em En
| MEDLINE
| ID: mdl-37128388
ABSTRACT
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning (ML) models capable of accurately adjudicating Vital Sign (VS) alerts raised at the bedside of hemodynamically monitored patients as real or artifact. Previous studies have utilized supervised ML techniques that require substantial amounts of hand-labeled data. However, manually harvesting such data can be costly, time-consuming, and mundane, and is a key factor limiting the widespread adoption of ML in healthcare (HC). Instead, we explore the use of multiple, individually imperfect heuristics to automatically assign probabilistic labels to unlabeled training data using weak supervision. Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Artefatos
/
Sinais Vitais
/
Aprendizado de Máquina Supervisionado
/
Monitorização Fisiológica
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
AMIA Annu Symp Proc
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Panamá