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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing.

Suzanna Schmeelk¹, Martins Samuel Dogo², Yifan Peng³

¹St. John's University, Queens, New York.

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences

|May 9, 2022

Summary

This summary is machine-generated.

This study introduces methods to classify sensitive information risk in clinical notes, crucial for protecting patient data. An SVM model achieved a 0.792 F1-score, enhancing health information security.

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Area of Science:

Health Informatics
Natural Language Processing
Cybersecurity

Background:

Clinical notes in electronic medical records are vital for patient care and research.
Increasing patient access to clinical notes necessitates robust data protection methods.
Existing de-identification techniques inadequately address sensitive information risk classification.

Purpose of the Study:

To investigate methods for identifying and classifying security/privacy risks within clinical notes.
To develop models that can detect sensitive information for improved data protection.
To aid in identifying clinical notes that may require further de-identification.

Main Methods:

Developed and tested several machine learning models using unigram and word2vec features.
Employed different classifiers to categorize sentence-level risk.
Utilized the i2b2 de-identification dataset for experimental evaluation.

Main Results:

The Support Vector Machine (SVM) classifier with word2vec features achieved a maximum F1-score of 0.792.
The models demonstrated effectiveness in categorizing sentence risk within clinical notes.
Identified specific features and classifiers that perform well for risk assessment.

Conclusions:

The developed models offer a promising approach to assessing and mitigating cyber risks in clinical notes.
Classifying sensitive information risk is a fundamental step towards safeguarding patient health information.
Future work should align risk assessment with global regulatory requirements.