ArticleFrontiers in bioengineering and biotechnology2020
Explainable Prediction of Medical Codes With Knowledge Graphs.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Interaction of artificial intelligence, mental disorders, and diverse data modalities: Potential treatment management based on the "method-disease-data" axis.Neural regeneration research · 2026Article
- Knowledge graph embedding and alignment of incomplete electronic health records for critical care applications.Journal of biomedical semantics · 2026Article
- Leveraging sequence-to-sequence models for semantic annotation of Dutch pathology reports.Journal of pathology informatics · 2026Article
- From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?Computational and structural biotechnology journal · 2024Review
- Knowledge-graph-based explainable AI: A systematic review.Journal of information science · 2024Article
- Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label.Bioengineering (Basel, Switzerland) · 2024Article
- [Overview of the application of knowledge graphs in the medical field].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Prediction of Cholecystokinin-Secretory Peptides Using Bidirectional Long Short-term Memory Model Based on Transfer Learning and Hierarchical Attention Network Mechanism.Biomolecules · 2023Article
- Article
- AnEMIC: A Framework for Benchmarking ICD Coding Models.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2022Article
- Automated clinical coding: what, why, and where we are?NPJ digital medicine · 2022Review
- Review
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5 authors.
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Abstract
International Classification of Diseases (ICD) is an authoritative health care classification system of different diseases. It is widely used for disease and health records, assisted medical reimbursement decisions, and collecting morbidity and mortality statistics. The most existing ICD coding models only translate the simple diagnosis descriptions into ICD codes. And it obscures the reasons and details behind specific diagnoses. Besides, the label (code) distribution is uneven. And there is a dependency between labels. Based on the above considerations, the knowledge graph and attention mechanism were expanded into medical code prediction to improve interpretability. In this study, a new method called G_Coder was presented, which mainly consists of Multi-CNN, graph presentation, attentional matching, and adversarial learning. The medical knowledge graph was constructed by extracting entities related to ICD-9 from freebase. Ontology contains 5 entity classes, which are disease, symptom, medicine, surgery, and examination. The result of G_Coder on the MIMIC-III dataset showed that the micro-F1 score is 69.2% surpassing the state of art. The following conclusions can be obtained through the experiment: G_Coder integrates information across medical records using Multi-CNN and embeds knowledge into ICD codes. Adversarial learning is used to generate the adversarial samples to reconcile the writing styles of doctor. With the knowledge graph and attention mechanism, most relevant segments of medical codes can be explained. This suggests that the knowledge graph significantly improves the precision of code prediction and reduces the working pressure of the human coders.
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