AI Article Synopsis
- Identifying adverse events in clinical documents is crucial for both retrospective research and monitoring treatment safety and cost-effectiveness.
- The study proposed semi-automated methods for detecting muscle weakness in preoperative clinical notes, aiming to improve predictions of paresis through images.
- The combined approach using semi-expert and machine learning methods achieved high sensitivity, specificity, and AUC scores, indicating its effectiveness for creating reliable training datasets for supervised machine learning.
Article Abstract
Identifying adverse events in clinical documents is demanded in retrospective clinical research and prospective monitoring of treatment safety and cost-effectiveness. We proposed and evaluated a few methods of semi-automated muscle weakness detection in preoperative clinical notes for a larger project on predicting paresis by images. The combination of semi-expert and machine learning methods demonstrated maximized sensitivity = 0.860 and specificity = 0.919, and largest AUC = 0.943 with a 95% CI [0.874; 0.991], outperforming each method used individually. Our approaches are expected to be effective for autoshaping a well- verified training dataset for supervised machine learning.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/SHTI200143 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!