KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare
Open accessAbstract
The goal of diagnosis prediction task is to predict the future health information of patients from their historical Electronic Healthcare Records (EHR). The most important and challenging problem of diagnosis prediction is to design an accurate, robust and interpretable predictive model. Existing work solves this problem by employing recurrent neural networks (RNNs) with attention mechanisms, but these approaches suffer from the data sufficiency problem. To obtain good performance with insufficient data, graph-based attention models are proposed. However, when the training data are sufficient, they do not offer any improvement in performance compared with ordinary attention-based models. To address these issues, we propose KAME, an end-to-end, accurate and robust model for predicting patients' future health information. KAME not only learns reasonable embeddings for nodes in the knowledge graph, but also exploits general knowledge to improve the prediction accuracy with the proposed knowledge attention mechanism. With the learned attention weights, KAME allows us to interpret the importance of each piece of knowledge in the graph. Experimental results on three real world datasets show that the proposed KAME significantly improves the prediction performance compared with the state-of-the-art approaches, guarantees the robustness with both sufficient and insufficient data, and learns interpretable disease representations.
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Cell morphology and tissue micro-architecture information can be aggregated from cell graphs to grade cancer histology images.
“Cell morphology and tissue micro-architecture information can be aggregated from cell graphs to grade cancer histology images (e.g., using GNNs (Section 2.2)) [8, 190, 191, 192].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [190]
Pooling with an attention mechanism can infer relevant patches in a histopathology image.
“An example aggregation method is pooling with an attention mechanism to infer relevant patches in the image [190].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [190]
A hierarchical knowledge graph was incorporated into learning representations in 6 of the 49 included studies.
“A hierarchical knowledge graph has been incorporated into learning representations in 6 studies [27,31–33,81,91].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [31]
An attention mechanism was adopted in 13 of the 49 included studies to improve performance and provide a degree of interpretability.
“To improve performance and provide a degree of interpretability to the model, an attention mechanism is adopted in 13 studies [6,14,27,31,43,57,64,72,76–78,80,88].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [31]
Month-level, year-level, and visit-level predictions are prevalent for long-term prediction tasks.
“Month-level [69,88], year-level [11,48,66,78,80], and visit-level predictions [31,38,56,59,64,70,71,76] are prevalent for those tasks.”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [31]
8 papers visualized with t-SNE to interpret the representations, and one study visualized with MDS.
“We observed 8 papers visualized with t-SNE to interpret the representations [14,27,31,60,66,73,75,79], and one study visualized with MDS [70].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [31]
These methods include developing additional components to models for capturing a hierarchical representation from event-level, to visit-level, to patient-level; for encoding the longitudinal factors between clinical events; for incorporating domain knowledge into the representation; and for adopting multiple modalities of EHR data.
“These methods include developing additional components to models for capturing a hierarchical representation from event-level, to visit-level, to patient-level [81,112]; for encoding the longitudinal factors between clinical events [65,77]; for incorporating domain knowledge into the representation [27,31–33,91]; and for adopting multiple modalities of EHR data [11,14,57].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [31]
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Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis
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UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
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Self-citationUA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction
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Diagnostic Prediction with Sequence-of-sets Representation Learning for Clinical Events
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Harnessing Prior Knowledge for Explainable Machine Learning: An Overview
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BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
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DNA-T: Deformable Neighborhood Attention Transformer for Irregular Medical Time Series
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Prediction of Treatment Medicines With Dual Adaptive Sequential Networks
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Medical Triage Chatbot Diagnosis Improvement via Multi-relational Hyperbolic Graph Neural Network
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Context-Aware and Time-Aware Attention-Based Model for Disease Risk Prediction With Interpretability
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Rare Disease Prediction by Generating Quality-Assured Electronic Health Records
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Incorporating medical code descriptions for diagnosis prediction in healthcare
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- Authors
- Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., & Gao, J.
- Affiliations
- University at Buffalo, State University of New York (US)
- Microsoft (United States) (US)
- Georgia State University (US)
- Kira Systems, Toronto, ON, Canada
- eHealth Inc., Mountain View, CA, USA
- Venue
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2018-10-17
- Pages
- 743-752
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W2896538705
- Cited by
- 213
- References
- 46
- Citations by year
- 2026: 16 · 2025: 30 · 2024: 27 · 2023: 26 · 2022: 27 · 2021: 42 · 2020: 30 · 2019: 13 · 2018: 2
- FWCI
- 8.65
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- Funders
- National Science Foundation (IIS-1553411, IIS-1747614)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- diagnostic prediction, electronic health records, knowledge graphs, interpretability, data sufficiency, model robustness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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