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Liu Q, Tian Y, Zhou T, Lyu K, Xin R, Shang Y, Liu Y, Ren J, Li J. A few-shot disease diagnosis decision making model based on meta-learning for general practice. Artif Intell Med 2024; 147:102718. [PMID: 38184346 DOI: 10.1016/j.artmed.2023.102718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/12/2023] [Accepted: 11/12/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Diagnostic errors have become the biggest threat to the safety of patients in primary health care. General practitioners, as the "gatekeepers" of primary health care, have a responsibility to accurately diagnose patients. However, many general practitioners have insufficient knowledge and clinical experience in some diseases. Clinical decision making tools need to be developed to effectively improve the diagnostic process in primary health care. The long-tailed class distributions of medical datasets are challenging for many popular decision making models based on deep learning, which have difficulty predicting few-shot diseases. Meta-learning is a new strategy for solving few-shot problems. METHODS AND MATERIALS In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n-way, k-shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n×k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset. RESULT Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90.02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29.13 % and 21.63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results. CONCLUSION The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.
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Affiliation(s)
- Qianghua Liu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou 311100, China
| | - Kewei Lyu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China
| | - Ran Xin
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou 311100, China
| | - Yong Shang
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou 311100, China
| | - Ying Liu
- General Practice Department, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Jingjing Ren
- General Practice Department, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China; Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou 311100, China.
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Cinaglia P, Cannataro M. Identifying Candidate Gene-Disease Associations via Graph Neural Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:909. [PMID: 37372253 DOI: 10.3390/e25060909] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023]
Abstract
Real-world objects are usually defined in terms of their own relationships or connections. A graph (or network) naturally expresses this model though nodes and edges. In biology, depending on what the nodes and edges represent, we may classify several types of networks, gene-disease associations (GDAs) included. In this paper, we presented a solution based on a graph neural network (GNN) for the identification of candidate GDAs. We trained our model with an initial set of well-known and curated inter- and intra-relationships between genes and diseases. It was based on graph convolutions, making use of multiple convolutional layers and a point-wise non-linearity function following each layer. The embeddings were computed for the input network built on a set of GDAs to map each node into a vector of real numbers in a multidimensional space. Results showed an AUC of 95% for training, validation, and testing, that in the real case translated into a positive response for 93% of the Top-15 (highest dot product) candidate GDAs identified by our solution. The experimentation was conducted on the DisGeNET dataset, while the DiseaseGene Association Miner (DG-AssocMiner) dataset by Stanford's BioSNAP was also processed for performance evaluation only.
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Affiliation(s)
- Pietro Cinaglia
- Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
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Zhang Q, Li M, Dong W, Zuo M, Wei S, Song S, Ai D. An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7773259. [PMID: 35528358 PMCID: PMC9071985 DOI: 10.1155/2022/7773259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 11/17/2022]
Abstract
Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper.
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Affiliation(s)
- Qingchuan Zhang
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Menghan Li
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Wei Dong
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Min Zuo
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Siwei Wei
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Shaoyi Song
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Dongmei Ai
- Basic Experimental of Natural Science, University of Science and Technology Beijing, Beijing 100083, China
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Cho YR, Hu X. Network-based approaches in bioinformatics and biomedicine. Methods 2021; 198:1-2. [PMID: 34958915 DOI: 10.1016/j.ymeth.2021.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Affiliation(s)
- Young-Rae Cho
- Division of Software, Yonsei University - Mirae Campus, Wonju, Republic of Korea.
| | - Xiaohua Hu
- College of Computing & Informatics, Drexel University, Philadelphia, PA, USA
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