Knowledge graph driven medicine recommendation system using graph neural networks on longitudinal medical records.
Attention mechanism
Graph neural network
Knowledge graphs
Medicine recommendation
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
26 Oct 2024
26 Oct 2024
Historique:
received:
14
05
2024
accepted:
08
10
2024
medline:
26
10
2024
pubmed:
26
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
Medicine recommendation systems are designed to aid healthcare professionals by analysing a patient's admission data to recommend safe and effective medications. These systems are categorised into two types: instance-based and longitudinal-based. Instance-based models only consider the current admission, while longitudinal models consider the patient's medical history. Electronic Health Records are used to incorporate medical history into longitudinal models. This project proposes a novel Knowledge Graph-Driven Medicine Recommendation System using Graph Neural Networks, KGDNet, that utilises longitudinal EHR data along with ontologies and Drug-Drug Interaction knowledge to construct admission-wise clinical and medicine Knowledge Graphs for every patient. Recurrent Neural Networks are employed to model a patient's historical data, and Graph Neural Networks are used to learn embeddings from the Knowledge Graphs. A Transformer-based Attention mechanism is then used to generate medication recommendations for the patient, considering their current clinical state, medication history, and joint medical records. The model is evaluated on the MIMIC-IV EHR data and outperforms existing methods in terms of precision, recall, F1 score, Jaccard score, and Drug-Drug Interaction control. An ablation study on our models various inputs and components to provide evidence for the importance of each component in providing the best performance. Case study is also performed to demonstrate the real-world effectiveness of KGDNet.
Identifiants
pubmed: 39455647
doi: 10.1038/s41598-024-75784-5
pii: 10.1038/s41598-024-75784-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25449Informations de copyright
© 2024. The Author(s).
Références
Zhang, Y., Chen, R., Tang, J., Stewart, W.F., & Sun, J. Leap: Learning to prescribe effective and safe treatment combinations for multimorbidity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’17, pp. 1315–1324. Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3097983.3098109
Gong, F., Wang, M., Wang, H., Wang, S. & Liu, M. Smr: Medical knowledge graph embedding for safe medicine recommendation. Big Data Res. 23, 100174. https://doi.org/10.1016/j.bdr.2020.100174 (2021).
doi: 10.1016/j.bdr.2020.100174
Shang, J., Xiao, C., Ma, T., Li, H., & Sun, J. GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination. arXiv preprint arXiv:1809.01852 (2018).
Yang, C., Xiao, C., Ma, F., Glass, L., & Sun, J. SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations (2022).
Yang, C., Xiao, C., Glass, L. & Sun, J. Change Matters: Medication Change Prediction with Recurrent Residual Networks (2021)
Wu, R., Qiu, Z., Jiang, J., Qi, G. & Wu, X. Conditional generation net for medication recommendation. In: Proceedings of the ACM Web Conference 2022. WWW ’22, pp. 935–945. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3485447.3511936 .
Mi, J., Zu, Y., Wang, Z. & He, J. Acdnet: Attention-guided collaborative decision network for effective medication recommendation. J. Biomed. Inf. 149, 104570. https://doi.org/10.1016/j.jbi.2023.104570 (2024).
doi: 10.1016/j.jbi.2023.104570
Johnson, A. E. et al. Mimic-iii, a freely accessible critical care database. Sci. Data 3(1), 1–9 (2016).
doi: 10.1038/sdata.2016.35
Johnson, A. E. et al. Mimic-iv, a freely accessible electronic health record dataset. Sci. Data 10(1), 1 (2023).
doi: 10.1038/s41597-022-01899-x
pubmed: 36596836
pmcid: 9810617
Huang, X., Zhang, J., Xu, Z., Ou, L. & Tong, J. A knowledge graph based question answering method for medical domain. PeerJ Comput. Sci. 7, 667 (2021).
doi: 10.7717/peerj-cs.667
Tatonetti, N. P., Ye, P. P., Daneshjou, R. & Altman, R. B. Data-driven prediction of drug effects and interactions. Sci. Trans. Med. 4(125), 125–3112531. https://doi.org/10.1126/scitranslmed.3003377 (2012).
doi: 10.1126/scitranslmed.3003377
Smithburger, P. L., Kane-Gill, S. L. & Seybert, A. L. Drug-drug interactions in the medical intensive care unit: an assessment of frequency, severity and the medications involved. Int. J. Pharmacy Practice 20(6), 402–408 (2012).
doi: 10.1111/j.2042-7174.2012.00221.x
Nyamabo, A. K., Yu, H. & Shi, J.-Y. SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction. Briefings Bioinf. 22(6), 133. https://doi.org/10.1093/bib/bbab133 (2021).
doi: 10.1093/bib/bbab133
Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A. & Stewart, W. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. Adv. Neural Inf. Process. Syst. 29 (2016)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L. & Polosukhin, I. Attention Is All You Need (2023)
Ba, J.L., Kiros, J.R. & Hinton, G.E. Layer Normalization (2016)
Tan, Y., Kong, C., Yu, L., Li, P., Chen, C., Zheng, X., Hertzberg, V.S. & Yang, C. 4sdrug: Symptom-based set-to-set small and safe drug recommendation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD ’22, pp. 3970–3980. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3534678.3539089 .
Sun, H. et al. Debiased, longitudinal and coordinated drug recommendation through multi-visit clinic records. Adv. Neural Inf. Process. Syst. 35, 27837–27849 (2022).
Yang, N., Zeng, K., Wu, Q. & Yan, J. Molerec: Combinatorial drug recommendation with substructure-aware molecular representation learning. In: Proceedings of the ACM Web Conference 2023. WWW ’23, pp. 4075–4085. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3543507.3583872 .
Veli?kovi?, P., Cucurull, G., Casanova, A., Romero, A., Liò, P. & Bengio, Y. Graph Attention Networks (2018)
Kipf, T.N. & Welling, M. Semi-Supervised Classification with Graph Convolutional Networks (2017)
Wu, J., Yu, X., He, K., Gao, Z. & Gong, T. Promise: A pre-trained knowledge-infused multimodal representation learning framework for medication recommendation. Inf. Process. Manage. 61(4), 103758 (2024).
doi: 10.1016/j.ipm.2024.103758
Kim, T., Heo, J., Kim, H., Shin, K. & Kim, S.-W. Vita: ‘carefully chosen and weighted less’ is better in medication recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 8600–8607 (2024)
Abul-Husn, N. S. & Kenny, E. E. Personalized medicine and the power of electronic health records. Cell 177(1), 58–69 (2019).
doi: 10.1016/j.cell.2019.02.039
pubmed: 30901549
pmcid: 6921466
Menachemi, N. & Collum, T.H. Benefits and drawbacks of electronic health record systems. Risk management and healthcare policy, 47–55 (2011)
Nigo, M. et al. Deep learning model for personalized prediction of positive mrsa culture using time-series electronic health records. Nat. Commun. 15(1), 2036 (2024).
doi: 10.1038/s41467-024-46211-0
pubmed: 38448409
pmcid: 10917736
Li, L. et al. Real-world data medical knowledge graph: construction and applications. Artif. Intell. Med. 103, 101817. https://doi.org/10.1016/j.artmed.2020.101817 (2020).
doi: 10.1016/j.artmed.2020.101817
pubmed: 32143785
Lee, E., Lee, D., Baek, J. H., Kim, S. Y. & Park, W.-Y. Transdiagnostic clustering and network analysis for questionnaire-based symptom profiling and drug recommendation in the uk biobank and a korean cohort. Sci. Rep. 14(1), 4500 (2024).
doi: 10.1038/s41598-023-49490-7
pubmed: 38402308
pmcid: 10894302
Zhang, Y. et al. Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network. Nat. Comput. Sci. 3(12), 1023–1033 (2023).
doi: 10.1038/s43588-023-00558-4
pubmed: 38177736
Wang, Y., Yang, Z. & Yao, Q. Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning. Commun. Med. 4(1), 59 (2024).
doi: 10.1038/s43856-024-00486-y
pubmed: 38548835
pmcid: 10978847
Wishart, D. S. et al. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, 668–672. https://doi.org/10.1093/nar/gkj067 (2006).
doi: 10.1093/nar/gkj067
Dai, Y., Wang, S., Xiong, N. N. & Guo, W. A survey on knowledge graph embedding: Approaches, applications and benchmarks. Electronics[SPACE] https://doi.org/10.3390/electronics9050750 (2020).
doi: 10.3390/electronics9050750
Corso, G., Stark, H., Jegelka, S., Jaakkola, T. & Barzilay, R. Graph neural networks. Nat. Rev. Methods Primers 4(1), 17 (2024).
doi: 10.1038/s43586-024-00294-7
Zhou, J. et al. Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020).
doi: 10.1016/j.aiopen.2021.01.001
He, Q., Li, X. & Cai, B. Graph neural network recommendation algorithm based on improved dual tower model. Sci. Rep. 14(1), 3853 (2024).
doi: 10.1038/s41598-024-54376-3
pubmed: 38360899
pmcid: 11315888
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O. & Dahl, G.E. Neural Message Passing for Quantum Chemistry (2017)
Shang, J., Ma, T., Xiao, C. & Sun, J. Pre-training of graph augmented transformers for medication recommendation. arXiv preprint arXiv:1906.00346 (2019)
Liu, T., Shen, H., Chang, L., Li, L. & Li, J. Iterative heterogeneous graph learning for knowledge graph-based recommendation. Sci. Rep. 13(1), 6987 (2023).
doi: 10.1038/s41598-023-33984-5
pubmed: 37117327
pmcid: 10147700
Schlichtkrull, M. et al. Modeling relational data with graph convolutional networks. In The Semantic Web (eds Gangemi, A. et al.) 593–607 (Springer, 2018).
doi: 10.1007/978-3-319-93417-4_38
Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X. & Chua, T.-S. Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. WWW ’17, pp. 173–182. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https://doi.org/10.1145/3038912.3052569
Wang, S., Qiao, J. & Feng, S. Prediction of lncrna and disease associations based on residual graph convolutional networks with attention mechanism. Sci. Rep. 14(1), 5185 (2024).
doi: 10.1038/s41598-024-55957-y
pubmed: 38431702
pmcid: 11319593
Hasibi, R., Michoel, T. & Oyarzún, D. A. Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality. npj Syst. Biol. Appl. 10(1), 24 (2024).
doi: 10.1038/s41540-024-00348-2
pubmed: 38448436
pmcid: 10917767
Radford, A. Improving language understanding by generative pre-training (2018)
Bedi, S., Jain, S.S. & Shah, N.H. Evaluating the clinical benefits of llms. Nature Medicine, 1–2 (2024)
Wu, L. et al. A survey on large language models for recommendation. World Wide Web 27(5), 60 (2024).
doi: 10.1007/s11280-024-01291-2
Hager, P., Jungmann, F., Holland, R., Bhagat, K., Hubrecht, I., Knauer, M., Vielhauer, J., Makowski, M., Braren, R. & Kaissis, G., et al. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nature medicine, 1–10 (2024)
Jiang, P., Xiao, C., Cross, A. & Sun, J. GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs (2024). arxiv:2305.12788
Liu, Q., Wu, X., Zhao, X., Zhu, Y., Zhang, Z., Tian, F. & Zheng, Y. Large Language Model Distilling Medication Recommendation Model (2024). arxiv:2402.02803
Dosovitskiy, A. & Djolonga, J. You only train once: Loss-conditional training of deep networks. In: International Conference on Learning Representations (2019)