학술논문

Preliminary Processing and Analysis of an Adverse Event Dataset for Detecting Sepsis-Related Events
Document Type
Conference
Source
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2021 IEEE International Conference on. :1605-1610 Dec, 2021
Subject
Bioengineering
Computing and Processing
Signal Processing and Analysis
Drugs
Performance evaluation
Quality assurance
Medical devices
Hospitals
Surveillance
Process control
Adverse events
Healthcare knowledge representation
Natural language processing
Quality improvement
Sepsis
Language
Abstract
Adverse event (AE) reports contain notes detailing procedural and guideline deviations, and unwanted incidents that can bring harm to patients. Available datasets mainly focus on vigilance or post-market surveillance of adverse drug reactions or medical device failures. The lack of clinical-related AE datasets makes it challenging to study healthcare-related AEs. AEs affect 10% of hospitalized patients, and almost half are preventable. Having an AE dataset can assist in identifying possible patient safety interventions and performing quality surveillance to lower AE rates. The free-text notes can provide insight into the cause of incidents and lead to better patient care. The objective of this study is to introduce a Norwegian AE dataset and present preliminary processing and analysis for sepsis-related events, specifically peripheral intravenous catheter-related bloodstream infections. Therefore, the methods focus on performing a domain analysis to prepare and better understand the data through screening, generating synthetic free-text notes, and annotating notes.