Towards knowledge-infused automated disease diagnosis assistant.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
11 Jun 2024
11 Jun 2024
Historique:
received:
11
06
2023
accepted:
27
01
2024
medline:
12
6
2024
pubmed:
12
6
2024
entrez:
11
6
2024
Statut:
epublish
Résumé
With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor-patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor's effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants.
Identifiants
pubmed: 38862529
doi: 10.1038/s41598-024-53042-y
pii: 10.1038/s41598-024-53042-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
13442Informations de copyright
© 2024. The Author(s).
Références
Cohen, R. A. & Adams, P. F. Use of the internet for health information: United states, 2009. In NCHS Data Brief 1–8 (2011).
George, P. P. et al. Online elearning for undergraduates in health professions: A systematic review of the impact on knowledge, skills, attitudes and satisfaction. J. Glob. Health4 (2014).
Wei, Z. et al. Task-oriented dialogue system for automatic diagnosis. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 201–207 (2018).
Teixeira, M. S., Maran, V. & Dragoni, M. The interplay of a conversational ontology and ai planning for health dialogue management. In Proceedings of the 36th Annual ACM Symposium on Applied Computing 611–619 (2021).
Liao, K. et al. Task-oriented dialogue system for automatic disease diagnosis via hierarchical reinforcement learning. arXiv:2004.14254 (2020).
Peng, Y.-S., Tang, K.-F., Lin, H.-T. & Chang, E. Refuel: Exploring sparse features in deep reinforcement learning for fast disease diagnosis. Adv. Neural. Inf. Process. Syst. 31, 7322–7331 (2018).
Yuan, Q., Chen, J., Lu, C. & Huang, H. The graph-based mutual attentive network for automatic diagnosis. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence 3393–3399 (2021).
Yu, C., Liu, J., Nemati, S. & Yin, G. Reinforcement learning in healthcare: A survey. ACM Comput. Surv. (CSUR) 55, 1–36 (2021).
doi: 10.1145/3477600
Kumar, Y., Koul, A., Singla, R. & Ijaz, M. F. Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda. J. Ambient Intell. Human. Comput. 1–28 (2022).
Kao, H.-C., Tang, K.-F. & Chang, E. Context-aware symptom checking for disease diagnosis using hierarchical reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence vol. 32 (2018).
Ramos, J. et al. Using tf–idf to determine word relevance in document queries. In Proceedings of the First Instructional Conference on Machine Learning vol. 242, 29–48 (Citeseer, 2003).
Davenport, T. & Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc. J. 6, 94 (2019).
doi: 10.7861/futurehosp.6-2-94
pubmed: 31363513
pmcid: 6616181
Miotto, R., Wang, F., Wang, S., Jiang, X. & Dudley, J. T. Deep learning for healthcare: Review, opportunities and challenges. Brief. Bioinform. 19, 1236–1246 (2018).
doi: 10.1093/bib/bbx044
pubmed: 28481991
Ventres, W. et al. Physicians, patients, and the electronic health record: An ethnographic analysis. Ann. Fam. Med. 4, 124–131 (2006).
doi: 10.1370/afm.425
pubmed: 16569715
pmcid: 1467009
Li, Y. et al. Behrt: Transformer for electronic health records. Sci. Rep. 10, 1–12 (2020).
Li, T., Wang, Z., Lu, W., Zhang, Q. & Li, D. Electronic health records based reinforcement learning for treatment optimizing. Inf. Syst. 104, 101878 (2022).
doi: 10.1016/j.is.2021.101878
Mnih, V. et al. Playing Atari with deep reinforcement learning. arXiv:1312.5602 (2013).
Nemesure, M. D., Heinz, M. V., Huang, R. & Jacobson, N. C. Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Sci. Rep. 11, 1–9 (2021).
doi: 10.1038/s41598-021-81368-4
Rasmy, L., Xiang, Y., Xie, Z., Tao, C. & Zhi, D. Med-bert: Pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit. Med. 4, 86 (2021).
doi: 10.1038/s41746-021-00455-y
pubmed: 34017034
pmcid: 8137882
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018).
Kormilitzin, A., Vaci, N., Liu, Q. & Nevado-Holgado, A. Med7: A transferable clinical natural language processing model for electronic health records. Artif. Intell. Med. 118, 102086 (2021).
doi: 10.1016/j.artmed.2021.102086
pubmed: 34412834
Menachemi, N. & Collum, T. H. Benefits and drawbacks of electronic health record systems. Risk Manage. Healthc. Policy 4, 47 (2011).
doi: 10.2147/RMHP.S12985
Tang, K.-F., Kao, H.-C., Chou, C.-N. & Chang, E. Y. Inquire and diagnose: Neural symptom checking ensemble using deep reinforcement learning. In NIPS Workshop on Deep Reinforcement Learning (2016).
Dietterich, T. G. Hierarchical reinforcement learning with the MAXQ value function decomposition. J. Artif. Intell. Res. 13, 227–303 (2000).
doi: 10.1613/jair.639
Chen, J., Li, D., Chen, Q., Zhou, W. & Liu, X. Diaformer: Automatic diagnosis via symptoms sequence generation. In Proceedings of the AAAI Conference on Artificial Intelligence vol. 36, 4432–4440 (2022).
Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016).
Veličković, P. et al. Graph attention networks. arXiv:1710.10903 (2017).
Dwivedi, V. P. & Bresson, X. A generalization of transformer networks to graphs. arXiv:2012.09699 (2020).
Rampášek, L. et al. Recipe for a general, powerful, scalable graph transformer. arXiv:2205.12454 (2022).
Zhu, H. & Koniusz, P. Simple spectral graph convolution. In International Conference on Learning Representations (2021).
Li, G., Müller, M., Ghanem, B. & Koltun, V. Training graph neural networks with 1000 layers. In International Conference on Machine Learning 6437–6449 (PMLR, 2021).
Brody, S., Alon, U. & Yahav, E. How attentive are graph attention networks? arXiv:2105.14491 (2021).
Zhang, Z. et al. Ernie: Enhanced language representation with informative entities. arXiv:1905.07129 (2019).
Yasunaga, M., Ren, H., Bosselut, A., Liang, P. & Leskovec, J. QA-GNN: Reasoning with language models and knowledge graphs for question answering. arXiv:2104.06378 (2021).
Zhang, X. et al. Greaselm: Graph reasoning enhanced language models for question answering. arXiv:2201.08860 (2022).
Yasunaga, M. et al. Deep bidirectional language-knowledge graph pretraining. arXiv:2210.09338 (2022).
Milewski, V., de Lhoneux, M. & Moens, M.-F. Finding structural knowledge in multimodal-bert. arXiv:2203.09306 (2022).
Liu, J. et al. Generated knowledge prompting for commonsense reasoning. arXiv:2110.08387 (2021).
Dong, C., Wang, Y., Zhang, Q. & Wang, N. The methodology of dynamic uncertain causality graph for intelligent diagnosis of vertigo. Comput. Methods Programs Biomed. 113, 162–174 (2014).
doi: 10.1016/j.cmpb.2013.10.002
pubmed: 24176413
Dong, C. & Zhang, Q. The cubic dynamic uncertain causality graph: A methodology for temporal process modeling and diagnostic logic inference. IEEE Trans. Neural Netw. Learn. Syst. RD 31, 4239–4253. https://doi.org/10.1109/TNNLS.2019.2953177 (2020).
doi: 10.1109/TNNLS.2019.2953177
Deng, N. & Zhang, Q. The application of dynamic uncertain causality graph based diagnosis and treatment unification model in the intelligent diagnosis and treatment of hepatitis B. Symmetry 13, 1185 (2021).
doi: 10.3390/sym13071185
Zhong, C. et al. Hierarchical reinforcement learning for automatic disease diagnosis. Bioinformatics (2022).
Xu, L. et al. End-to-end knowledge-routed relational dialogue system for automatic diagnosis. In Proceedings of the AAAI Conference on Artificial Intelligence vol. 33, 7346–7353 (2019).
Yan, G. et al. M[Formula: see text]-meddialog: A dataset and benchmarks for multi-domain multi-service medical dialogues. arXiv:2109.00430 (2021).
Zeng, G. et al. Meddialog: Large-scale medical dialogue dataset. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020).
Liu, W. et al. Meddg: A large-scale medical consultation dataset for building medical dialogue system. CoRR arXiv:2010.07497 (2020).
Fleiss, J. L., Levin, B. & Paik, M. C. Statistical Methods for Rates and Proportions (John Wiley & Sons, 2013).
Liu, F., Shareghi, E., Meng, Z., Basaldella, M. & Collier, N. Self-alignment pretraining for biomedical entity representations. arXiv:2010.11784 (2020).
Chen, Q., Zhuo, Z. & Wang, W. Bert for joint intent classification and slot filling. arXiv:1902.10909 (2019).
Zhang, Z., Cui, P. & Zhu, W. IEEE Trans. Knowl. Data Eng. (Deep learning on graphs A survey, 2020).
Bahdanau, D., Cho, K. & Bengio, Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014).
Yasunaga, M., Leskovec, J. & Liang, P. Linkbert: Pretraining language models with document links. arXiv:2203.15827 (2022).
Zhang, S. et al. Knowledge-rich self-supervision for biomedical entity linking. In Findings of the Association for Computational Linguistics: EMNLP 2022, 868–880 (2022).
Tiwari, A., Saha, S. & Bhattacharyya, P. A knowledge infused context driven dialogue agent for disease diagnosis using hierarchical reinforcement learning. Knowl. Based Syst. 242, 108292 (2022).
doi: 10.1016/j.knosys.2022.108292
Yuan, Z. et al. Coder: Knowledge-infused cross-lingual medical term embedding for term normalization. J. Biomed. Inform. 126, 103983 (2022).
doi: 10.1016/j.jbi.2021.103983
pubmed: 34990838
Welch, B. L. The generalization of student’s’ problem when several different population variances are involved. Biometrika 34, 28–35 (1947).
pubmed: 20287819