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<p>In this paper, firstly, we study the reinforcement learning algorithm, which is a related technology of knowledge graph, and use reinforcement learning to structure the application framework of health care knowledge graph and construct the domain dictionary. And based on the BitLab21 dataset, entity relationship annotation is performed on the named entity recognition result set to realize entity relationship extraction of clinical electronic medical records. Then, the HacRED dataset is used to evaluate the deep learning model, and comparison tests and ablation experiments are conducted to verify the effectiveness of reinforcement learning for constructing knowledge graphs, respectively. Finally, a deviation monitoring method and a data feature extraction method for the case dataset are proposed, and a comparative study of different feature extraction methods is conducted to illustrate the advantages of feature fusion in distinguishing abnormal deviation patterns. All three features have relatively good recognition accuracy for normal patterns, which can reach 100%. The error curves perform well, and their training and testing errors converge very quickly to below 0.01 with less than 5 iterations and achieve nearly 100% recognition accuracy. Reinforcement learning-based knowledge mapping of case datasets and feature fusion-based data machine bias detection can improve the efficiency of medical detection.</p>
</abstract>

In this paper, firstly, we study the reinforcement learning algorithm, which is a related technology of knowledge graph, and use reinforcement learning to structure the application framework of health care knowledge graph and construct the domain dictionary. And based on the BitLab21 dataset, entity relationship annotation is performed on the named entity recognition result set to realize entity relationship extraction of clinical electronic medical records. Then, the HacRED dataset is used to evaluate the deep learning model, and comparison tests and ablation experiments are conducted to verify the effectiveness of reinforcement learning for constructing knowledge graphs, respectively. Finally, a deviation monitoring method and a data feature extraction method for the case dataset are proposed, and a comparative study of different feature extraction methods is conducted to illustrate the advantages of feature fusion in distinguishing abnormal deviation patterns. All three features have relatively good recognition accuracy for normal patterns, which can reach 100%. The error curves perform well, and their training and testing errors converge very quickly to below 0.01 with less than 5 iterations and achieve nearly 100% recognition accuracy. Reinforcement learning-based knowledge mapping of case datasets and feature fusion-based data machine bias detection can improve the efficiency of medical detection.

Bias detection and knowledge graph comparison analysis for medical record datasets

Luoyang Campus, PLA Information Engineering University

the First Affiliated Hospital of Zhengzhou University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, firstly, we study the reinforcement learning algorithm, which is a related technology of knowledge graph, and use reinforcement learning to...

Bias detection and knowledge graph comparison analysis for medical record datasets

Publié en ligne: 02 oct. 2023

Pages: -

Reçu: 20 nov. 2022

Accepté: 12 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00459

Mots clés
Medical record dataset, Bias detection, Knowledge graph, Reinforcement learning, Feature extraction

© 2023 Xiaoyu Yi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Bias detection and knowledge graph comparison analysis for medical record datasets

Publié en ligne: 02 oct. 2023

Pages: -

Reçu: 20 nov. 2022

Accepté: 12 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00459

Mots clésMedical record dataset, Bias detection, Knowledge graph, Reinforcement learning, Feature extraction

© 2023 Xiaoyu Yi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Medical record dataset, Bias detection, Knowledge graph, Reinforcement learning, Feature extraction