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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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Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
(2008) · Journal of Machine Learning Research · Cited by 33,051
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251
(2013) · Cited by 5,726
LINE: Large-scale Information Network Embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Knowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915
Research works printed but not linked: 4
- Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015 . Neural Machine Translation by Jointly Learning to Align and Translate . In Proceedings of the 3rd International Conference on Learning Representations (ICLR'15) . Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of the 3rd International Conference on Learning Representations (ICLR'15) .
- 10.5555/2886521.2886624 · DOI
- The Theano Development Team . 2016 . Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688 (2016). The Theano Development Team. 2016. Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688 (2016). · arXiv
- 10.5555/2893873.2894046 · DOI
Cited by
Sorted by citations
Items 101–200 of 212
Predicting Future Disorders via Temporal Knowledge Graphs and Medical Ontologies
Postiglione, M., Bean, D. M., Kraljević, Ž., Dobson, R., & Moscato, V. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3390419 · Cited by 12
Perceived individual fairness with a molecular representation for medicine recommendations
Liu, H., Lin, H., Xu, B., Zhao, N., Wen, D., Zhang, X., & Lin, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108755 · Cited by 10
A One-Size-Fits-Three Representation Learning Framework for Patient Similarity Search
Huang, Y., Luo, F., Wang, X., Zhu, D., Li, B., & Luo, B. (2023) · Data Science and Engineering · doi:10.1007/s41019-023-00216-9 · Cited by 10
Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs
Wang, W., Feng, Y., Zhao, H., Wang, X., Cai, R., Cai, W., & Zhang, X. (2024) · Health Information Science and Systems · doi:10.1007/s13755-024-00278-7 · Cited by 9
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction
Li, Y., Zhu, Z., Guo, X., Li, S., Yang, Y., & Zhao, Y. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3605895 · Cited by 9
PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic RecordsPreprint
Bhoi, S., Lee, M. L., & Hsu, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.13569 · Cited by 9
MedReg-KG: KnowledgeGraph for Streamlining Medical Device Regulatory Compliance
Chattoraj, S., & Joshi, K. P. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825686 · Cited by 8
Boukhers, Z., Goswami, P., & Jürjens, J. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08581-2 · Cited by 8
Inheritance-Guided Hierarchical Assignment for Clinical Automatic Diagnosis
Du, Y., Luo, P., Hong, X., Xu, T., Zhang, Z., Ren, C., Zheng, Y., & Chen, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73200-4_31 · Cited by 8
Readmission Prediction with Knowledge Graph Attention and RNN-Based Ordinary Differential Equations
Pei, S., Niu, K., Peng, X., & Zeng, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-82153-1_46 · Cited by 8
MedSkim: Denoised Health Risk Prediction via Skimming Medical Claims Data
Cui, S., Luo, J., Ye, M., Wang, J., Wang, T., & Ma, F. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00018 · Cited by 8
Self-citationClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in MedicinePreprint
Johnson, R., Gottlieb, U., Shaham, G., Eisen, L., Waxman, J. G., Devons-Sberro, S., Ginder, C. R., Hong, P. J., Sayeed, R., Su, X., et al. (2024) · medRxiv · doi:10.1101/2024.12.03.24318322 · Cited by 8
Democratizing large language model-based graph data augmentation via latent knowledge graphs
Feng, Y., Chan, T. H., Yin, G., & Yu, L. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107777 · Cited by 12
Joint Medical Ontology Representation Learning for Healthcare Predictions
Wang, K., Chen, N., & Chen, T. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207355 · Cited by 7
Cheong, C. W., Yin, K., Cheung, W. K., Fung, B. C. M., & Poon, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3625224 · Cited by 8
Zhang, Z., Cui, H., Xu, R., Xie, Y., Ho, J. C., & Yang, C. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671594 · Cited by 7
Ma, F., Zhong, S., Gao, J., & Bian, L. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342141 · Cited by 7
Self-citationAugmenting features by relative transformation for small data
Li, H., Wen, G., Jia, X., Lin, Z., Zhao, H., & Xiao, X. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107121 · Cited by 7
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare
Ye, M., Luo, J., Zheng, G., Xiao, C., Xiao, H., Wang, T., & Ma, F. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9994898 · Cited by 7
Self-citationNiu, S., Ma, J., Yin, Q., Bai, L., Chen, L., & Yang, X. (2024) · Annals of Operations Research · doi:10.1007/s10479-023-05817-1 · Cited by 11
Enhancing Personalized Healthcare via Capturing Disease Severity, Interaction, and Progression
Tan, Y., Zhou, Z., Yu, L., Liu, W., Chen, C., Ma, G., Hu, X., Hertzberg, V., & Yang, C. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00173 · Cited by 7
Hur, K., Lee, J., Oh, J., Price, W., Kim, Y., & Choi, E. (2021) · doi:10.2196/preprints.32523 · Cited by 7
Lv, H., Chen, Z., Yang, Y., Ma, G., Tan, Y., & Yang, C. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3651448 · Cited by 6
HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer’s Disease
Chen, Q., Wang, J., Wang, G., & Hong, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3594364 · Cited by 6
Knowledge‐aware representation learning for diagnosis prediction
Li, W., Li, H., Yang, B., Zhou, L., Yang, X., Zhang, M., & Wang, B. (2022) · Expert Systems · doi:10.1111/exsy.13175 · Cited by 9
AUTOMED: Automated Medical Risk Predictive Modeling on Electronic Health Records
Cui, S., Wang, J., Gui, X., Wang, T., & Ma, F. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995209 · Cited by 7
Self-citationInterpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge GraphsPreprint
Sun, Z., Dong, W., Shi, J., & Huang, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08300 · Cited by 6
Cao, Y., Wang, X., Wang, Q., Yuan, Z., Shi, Y., & Peng, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112373 · Cited by 5
Yin, C., Fu, S., Yao, B., Pham, T.-H., Cao, W., Wang, D., Caterino, J. M., & Zhang, P. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709402 · Cited by 11
Isgut, M., Hornback, A. M., Bao, H., Sun, Y., Choi, K., Anderson, B. J., Deshpande, S. R., Chang, A., & Wang, M. D. (2025) · Communications Medicine · doi:10.1038/s43856-025-01138-5 · Cited by 6
You, T., Dang, Q., Li, Q., Zhang, P., Wu, G., & Huang, W. (2024) · Information Systems · doi:10.1016/j.is.2024.102390 · Cited by 5
OntoMedRec: Logically-pretrained model-agnostic ontology encoders for medication recommendation
Tan, W., Wang, W., Zhou, X., Buntine, W., Bingham, G., & Yin, H. (2024) · World Wide Web · doi:10.1007/s11280-024-01268-1 · Cited by 7
Hyperthyroidism Progress Prediction with Enhanced LSTM
Lu, H., Wang, M., Zhao, W., Su, T., & Yang, J. Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60290-1_16 · Cited by 5
Li, Y., Zhang, X., Qian, B., Gao, Z., Guan, C., Zheng, Y., Zheng, H., Wu, F., & Li, C. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00045 · Cited by 5
Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data
Proios, D., Yazdani, A., Bornet, A., Ehrsam, J., Rekik, I., & Teodoro, D. (2023) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa60987.2023.10302556 · Cited by 5
DUGRA: Dual-Graph Representation Learning for Health Information Networks
Wang, Q., Fung, B. C. M., & Hung, P. C. K. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378420 · Cited by 7
Zhang, X., Qian, B., Li, Y., Gao, Z., Guan, C., Wang, R., Zheng, Y., Zheng, H., & Li, C. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00082 · Cited by 5
Lee, T., Hwang, J., & Lee, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05407 · Cited by 5
A Benchmark Dataset for Understandable Medical Language Translation.Preprint
Luo, J., Zheng, Z., Ye, H., Ye, M., Wang, Y., You, Q., Xiao, C., & Ma, F. (2020) · arXiv (Cornell University) · Cited by 5
Self-citationShalmani, M. A., Khani, M., Taleban, A., Yi, Z., Fink, J. T., Weber, C. E., Lu, Q., & Luo, J. (2026) · Neural Computing and Applications · doi:10.1007/s00521-025-11695-4 · Cited by 6
Wu, J., Mei, X., Mao, R., He, K., & Cambria, E. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103620 · Cited by 5
Multi-relational EHR representation learning with infusing information of Diagnosis and Medication
Shi, Y., Guo, Y., Wu, H., Li, J., & Li, X. (2021) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00241 · Cited by 5
Kumar, S., Nanelia, A., Mariappan, R., Rajagopal, A., & Rajan, V. (2021) · JMIR Medical Informatics · doi:10.2196/28842 · Cited by 4
The Intelligent Diagnostic System for Common Diseases using the Optimized Medical Knowledge Graph
Wang, J., Liu, N., Hu, Q., Wu, Z., Dong, W., & Shen, Y. (2020) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc48107.2020.9148406 · Cited by 4
Multi-view Integration Learning for Irregularly-sampled Clinical Time SeriesPreprint
Lee, Y., Jun, E., & Suk, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.09986 · Cited by 6
Improving biomedical fields with knowledge graphs: Domains, Tasks, and Applications
Baazouzi, W., & Kachroudi, M. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.238 · Cited by 3
Alanazi, A., Almutib, A., & Aldosari, B. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13040680 · Cited by 3
Chio, K., Zhu, W., He, L., Zhang, D., Xu, Y., & Luo, W. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10393910 · Cited by 3
KAFE: Knowledge and Frequency Adapted Embeddings
Ashfaq, A., Lingman, M., & Nowaczyk, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95470-3_10 · Cited by 3
Zhang, F., Wu, T., Wang, Y., Cai, Y., Xiao, C., Zhao, E., Glass, L. M., & Sun, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01099 · Cited by 10
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable PrognosisPreprint
Ma, L., Ma, X., Gao, J., Zhang, C., Yu, Z., Jiao, X., Ruan, W., Wang, Y., Tang, W., & Wang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08848 · Cited by 5
Self-citationExplainable Health Risk Predictor with Transformer-based Medicare Claim EncoderPreprint
Lahlou, C., Crayton, A., Trier, C., & Willett, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.09428 · Cited by 7
SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors
Wang, Y., Wang, P., Yin, C., & Zhang, P. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737440 · Cited by 3
Feng, X., Zhu, S., Fang, L., Zhu, H., Cai, G., Shen, Y., & Ning, G. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3647877 · Cited by 2
Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach
Zhang, M., Zhang, L., & Liu, T. (2024) · Electronics · doi:10.3390/electronics13020367 · Cited by 2
SeFNet: Linking tabular datasets with semantic feature nets
Woźnica, K., Wilczyński, P., & Biecek, P. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103436 · Cited by 4
Peng, X., Long, G., Shen, T., Wang, S., Chengqi Zhang, B., Clarke, A., & Schlegel, C. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228235 · Cited by 9
Therapeutic Prediction task on Electronic Health Record using DeBERTa
Gupta, A., Jain, N., & Chaurasiya, V. K. (2022) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon56432.2022.9986398 · Cited by 3
Ren, H., Wang, J., & Zhao, W. X. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557440 · Cited by 3
DL-BERT: a time-aware double-level BERT-style model with pre-training for disease prediction
Chen, X., Lin, J., & An, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020513 · Cited by 5
Bridging Stepwise Lab-Informed Pretraining and Knowledge-Guided Learning for Diagnostic Reasoning
Hu, P., Lu, C. H., Wang, F., & Ning, Y. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3670251 · Cited by 5
Challenges and Considerations with Code-Mixed NLP for Multilingual SocietiesPreprint
Srivastava, V. P., & Singh, M. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07823 · Cited by 13
Peng, X., Long, G., Shen, T., Wang, S., Niu, Z., & Chengqi Zhang, B. (2021) · arXiv (Cornell University) · Cited by 2
Medication Prediction Using Memory Augmented Heterogeneous Information Fusion Network
An, Y., Sun, Y., Jin, B., & Wei, X. (2022) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom57025.2022.00037 · Cited by 2
An efficient multi-view and multi-channel disease risk prediction model
Wang, Z., Li, R., Wang, J., Zhao, H., Yan, S., & Ma, L. (2023) · International Conference on Cyber-Physical Social Intelligence (ICCSI) · doi:10.1109/iccsi58851.2023.10303920 · Cited by 1
Zhang, H., Lv, H., Tan, Y., Ma, G., Wang, F., & Yang, C. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10687504 · Cited by 1
MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records
Yang, E., Hu, P., Han, X., & Ning, Y. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825511 · Cited by 2
Li, S., Dong, F., Li, W., & Li, W. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130519 · Cited by 2
Yang, Z., He, S., Chen, Z., Zhang, H., & Wang, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_41 · Cited by 1
DMAP: A Deep-Model Driven Multi-Agent Framework for Reliable Diagnosis Prediction
Yu, W., Ma, G., Lv, H., Lin, Z., Chen, X., & Tan, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356738 · Cited by 1
Deep Knowledge Reasoning guided Disease Prediction
Liang, Y., Wang, H., & Zhang, W. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945606 · Cited by 2
Knowledge Injected Multimodal Irregular EHRs Model for Medical Prediction
Liu, S., & Chen, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-67751-9_3 · Cited by 1
MISDP: multi-task fusion visit interval for sequential diagnosis prediction
Zhu, S., Yang, R., Pan, Z., Tian, X., & Ji, H. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05998-x · Cited by 1
ProAID: path-based reasoning for self-attentional disease prediction
Lü, X., Cui, L., Sun, Z., & Zhu, Y. (2021) · Knowledge and Information Systems · doi:10.1007/s10115-021-01617-w · Cited by 1
Liu, Y., Wu, J., Wei, Y., Mao, B., Li, C., & Gong, T. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669339 · Cited by 1
State of Art Survey for Deep Learning Effects on Semantic Web Performance
Mehyadin, A. E., Zeebaree, S. R. M., Sadeeq, M. A. M., Shukur, H. M., Alkhayyat, A. H., & Sharif, K. H. (2021) · International Conference on Contemporary Information Technology and Mathematics (ICCITM) · doi:10.1109/iccitm53167.2021.9677798 · Cited by 2
Auto-Regressive Self-Attention Models for Diagnosis Prediction on Electronic Health Records
Wullschleger, P., Lionetti, S., Daly, D., Volpe, F., & Caro, G. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020339 · Cited by 1
Semantic-based Attention model for Hospital Readmission Prediction
Im, S. J., Xu, Y., & Watson, J. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020315 · Cited by 2
Liu, C., Xu, Y., & Zhu, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11760 · Cited by 1
Self-supervision for health insurance claims data: a Covid-19 use casePreprint
Apostolova, E., Karim, F., Muscioni, G., Rana, A., & Clyman, J. I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.14591
Unifying Heterogenous Electronic Health Records Systems via Text-Based Code EmbeddingPreprint
Hur, K., Lee, J., Oh, J., Price, W., Kim, Y., & Choi, E. (2021) · arXiv (Cornell University)
Machine Teaching-Based Efficient Labelling for Cross-unit Healthcare Data Modelling
Wang, Y., Peng, X., Clarke, A., Schlegel, C., & Jiang, J. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97546-3_26
Multi-Cause Learning for Diagnosis Prediction
Wang, L., Liu, Q., Ma, H., Wu, S., & Wang, L. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-9297-1_23
Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction
Wang, M., Xiong, Y., Zhang, Y., Yu, P. S., & Zhu, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30678-5_20
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He, L., Wang, J., & Li, X. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7869-4_8
Liu, G., Ye, J., & Wang, B. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2238-9_24
Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows
Tan, Y., Zhang, H., Zhou, Z., Ma, G., Wang, F., Liu, W., Liao, X., Hertzberg, V., & Yang, C. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3651457
Integrating Multimodal Patient Data into Attention-Based Graph Networks for Disease Risk Prediction
Huang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66535-6_22
Multi-scale and Multi-level Attention Based on External Knowledge in EHRs
Le, D. T., & Le, B. H. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-5937-8_10
Towards Automatically Refining Low-Quality Domain Knowledge: A Case Study in Healthcare
Bielski, P., Jendral, S., Witterauf, L., & Bach, J. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74633-8_25
Prototype-Guided Contrastive Knowledge Graph Representation Learning for Diagnosis Prediction
Park, W., Mulyadi, A. W., Kang, E., & Suk, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-8702-9_18
Xu, F., Yu, H., Su, Z., Zhang, P., & Wang, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92741-6_13
TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory Encoding
Liang, Y. T., Chen, Y., Fang, Y., Lakshmanan, L. V. S., & Ma, C. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715545
Multi-Ontology Integration with Dual-Axis Propagation for Medical Concept Representation
Kerdabadi, M. N., Moghaddam, A. H., Wang, D., & Yao, Z. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761388 · Cited by 3
Multimorbidity measurement strategies for predicting hospital visits
Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z
Yu, X., Zhang, K., Lv, H., Ma, G., Lin, Z., Chen, X., & Tan, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356567
$\mathrm{D}^{2}$ KGMed: Dynamic Diagnostic Knowledge Graphs for Medical Diagnosis Prediction
Zhang, J., Zheng, G., Lv, H., Luo, L., Ma, G., Lin, Z., Chen, X., & Tan, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357158
Kang, Y., Li, Z., Pu, B., Dong, X., Yang, J., Zhao, L., Ma, B., Li, N., Chen, J., & Yu, P. S. (2026) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2026.3664408
Hải, N. M., Nam, N. P., & Thinh, N. V. (2026) · IEEE Access · doi:10.1109/access.2026.3670686 · Cited by 1
ULoR: Uncertainty-Aware Leave-One-Out Refinement Framework for Diagnosis Prediction
Zhang, J., Shao, W., & Tan, Y. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792963
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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