Detection of cancer recurrence from Thai-English electronic medical records using sentence embeddings
Clinical Scorecard: Identification of Cancer Recurrence through Thai-English Electronic Medical Records Utilizing Sentence Embeddings
At a Glance
Category Detail
Condition Cancer Recurrence Detection Key Mechanisms Sentence-bidirectional encoder representations from transformers (SBERT) models Target Population Patients with breast, colorectal, cervical, and head and neck cancers Care Setting Multicentre oncology hospitals in Thailand
Key Highlights
Developed and validated SBERT models for cancer recurrence detection in Thai-English EMRs MetBERT achieved highest AUPRC for locoregional versus no recurrence and locoregional versus distant recurrence Bilingual-SBERT demonstrated robust performance during external validation Low AUPRC values indicate extreme class imbalance in recurrence prevalence Models suitable for clinical integration as a screening tool for cancer registry workflows Guideline-Based Recommendations
Diagnosis
Utilize SBERT models for detecting cancer recurrence in EMRs Management
Implement bilingual-SBERT as a screening tool for prioritizing high-probability records Monitoring & Follow-up
Regularly validate model performance with external datasets Risks
Consider the impact of class imbalance on model performance Patient & Prescribing Data
Patients with breast, colorectal, cervical, and head and neck cancers
Models can streamline the identification of recurrence, reducing manual workload for registrars
Clinical Best Practices
Integrate sentence embedding frameworks into clinical workflows Ensure continuous external validation of models in diverse clinical settings Address data completeness and accuracy challenges in EMRs Related Resources & Content
