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Enhancing unsupervised medical entity linking with multi-instance learning.

Cheng Yan^1,2, Yuanzhe Zhang^3,4, Kang Liu^1,2

¹National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

BMC Medical Informatics and Decision Making

|November 18, 2021

Summary

This summary is machine-generated.

This study introduces an unsupervised method for linking Chinese medical symptoms to ICD10 codes, improving accuracy in informal online consultations. The multi-instance learning approach enhances performance over basic models.

Keywords:

Medical entity linking Multiple instance learning Unsupervised learning

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Area of Science:

Natural Language Processing
Medical Informatics
Machine Learning

Background:

Medical mentions require linking to knowledge bases (KB) for downstream applications.
Informal medical language in online consultations presents unique challenges for accurate medical mention linking.
Linking medical mentions to structured knowledge bases like ICD10 is crucial for applications such as disease diagnosis.

Purpose of the Study:

To propose an unsupervised method for linking Chinese medical symptom mentions to ICD10 classifications.
To address the challenges of vague and casual language in colloquial medical contexts.
To improve the accuracy of medical entity linking in informal settings.

Main Methods:

Developed an unsupervised entity linking model utilizing multi-instance learning (MIL).
Built upon a basic embedding similarity-based unsupervised entity linking method (BEL).
Constructed a dataset from an unlabeled Chinese medical consultation corpus and employed various encoders for representation and a ranking network for entity scoring.

Main Results:

Achieved 60.34% accuracy on a test dataset annotated by medical professionals.
Demonstrated a performance improvement of 1.72% over the fundamental BEL model.
Validated the effectiveness of the proposed unsupervised entity linking method.

Conclusions:

The proposed unsupervised entity linking method using MIL effectively links Chinese medical symptoms to ICD10 codes.
The model outperforms the baseline BEL method, offering a valuable approach for medical entity linking.
This research provides insights for future advancements in unsupervised medical entity linking.