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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 (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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Self-citationHarerimana, G., Kim, G. I., Kim, J. W., & Jang, B. (2023) · IEEE Access · doi:10.1109/access.2023.3320179 · Cited by 15
Interpretable Disease Progression Prediction Based on Reinforcement Reasoning Over a Knowledge Graph
Sun, Z., Dong, W., Shi, J., & Huang, Z. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3331847 · Cited by 24
Li, Z., Yao, H., & Ma, F. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371874 · Cited by 14
Self-citationComparative effectiveness of medical concept embedding for feature engineering in phenotyping
Lee, J., Liu, C., Kim, J. H., Butler, A. M., Shang, N., Pang, C., Natarajan, K., Ryan, P. B., Ta, C., & Weng, C. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab028 · Cited by 16
Hou, W., Wang, J., Lin, Q., Wang, X., & Huang, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3595140 · Cited by 14
Multilevel Self-Attention Model and its Use on Medical Risk Prediction
Zeng, X., Feng, Y., Moosavinasab, S., Lin, D., Lin, S. M., & Liu, C. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0011 · Cited by 13
Li, Q., Chen, L., Cai, Y., & Wu, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33380-4_16 · Cited by 13
How to Empower Disease Diagnosis in a Medical Education System Using Knowledge Graph
Ansong, S., Eteffa, K. F., Li, C., Sheng, M., Zhang, Y., & Xing, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30952-7_52 · Cited by 13
Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction
Johnson, J. M., & Khoshgoftaar, T. M. (2020) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic50333.2020.00026 · Cited by 14
Multi-disease Predictive Analytics: A Clinical Knowledge-aware Approach
Qiu, L., Gorantla, S., Rajan, V., & Tan, B. C. Y. (2021) · ACM Transactions on Management Information Systems · doi:10.1145/3447942 · Cited by 14
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
Khodadadi, A., Bousejin, N. G., Molaei, S., Chauhan, V. K., Zhu, T., & Clifton, D. A. (2023) · Sensors · doi:10.3390/s23146571 · Cited by 15
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records
Kerdabadi, M. N., Moghaddam, A. H., Liu, B., Liu, M., & Yao, Z. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614824 · Cited by 13
An, Y., Tang, H., Jin, B., Xu, Y., & Wei, X. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02325-x · Cited by 17
Multi-modal Contrastive Learning for Healthcare Data Analytics
Li, R., & Gao, J. (2022) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi54592.2022.00029 · Cited by 15
Self-citationA review and systematic guide to counteracting medical data scarcity for AI applications
Gröger, F., Amruthalingam, L., Lionetti, S., Navarini, A. A., Ille, F., & Pouly, M. (2025) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2025.100220 · Cited by 13
Advances in Mining Heterogeneous Healthcare Data
Ma, F., Ye, M., Luo, J., Xiao, C., & Sun, J. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3470789 · Cited by 11
Self-citationIntelligent Medical Diagnosis Model Based on Graph Neural Networks for Medical Images
Sharma, A., Sharma, A., & Guo, K. (2025) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70020 · Cited by 11
Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction
Wang, L., Liu, Q., Zhang, M., Hu, Y., Wu, S., & Wang, L. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3310478 · Cited by 10
Metadata
- 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
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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.
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