Hybrid architecture based intelligent diagnosis assistant for GP
Authors: Wang, R., Jayathunge, K., Page, R., Li, H., Zhang, J.J. and Yang, X.
Journal: BMC Medical Informatics and Decision Making
Volume: 24
Issue: 1
eISSN: 1472-6947
DOI: 10.1186/s12911-023-02398-8
Abstract:As the first point of contact for patients, General Practitioners (GPs) play a crucial role in the National Health Service (NHS). An accurate primary diagnosis from the GP can alleviate the burden on specialists and reduce the time needed to re-confirm the patient’s condition, allowing for more efficient further examinations. However, GPs have broad but less specialized knowledge, which limits the accuracy of their diagnosis. Therefore, it is imperative to introduce an intelligent system to assist GPs in making decisions. This paper introduces two data augmentation methods, the Complaint Symptoms Integration Method and Symptom Dot Separating Method, to integrate essential information into the Integration dataset. Additionally, it proposes a hybrid architecture that fuses the features of words from different representation spaces. Experiments demonstrate that, compared to commonly used pre-trained attention-based models, our hybrid architecture delivers the best classification performance for four common neurological diseases on the enhanced Integration dataset. For example, the classification accuracy of the BERT+CNN hybrid architecture is 0.897, which is a 5.1% improvement over both BERT and CNN with 0.846. Finally, this paper develops an AI diagnosis assistant web application that leverages the superior performance of this architecture to help GPs complete primary diagnosis efficiently and accurately.
https://eprints.bournemouth.ac.uk/39326/
Source: Scopus
Hybrid architecture based intelligent diagnosis assistant for GP.
Authors: Wang, R., Jayathunge, K., Page, R., Li, H., Zhang, J.J. and Yang, X.
Journal: BMC Med Inform Decis Mak
Volume: 24
Issue: 1
Pages: 15
eISSN: 1472-6947
DOI: 10.1186/s12911-023-02398-8
Abstract:As the first point of contact for patients, General Practitioners (GPs) play a crucial role in the National Health Service (NHS). An accurate primary diagnosis from the GP can alleviate the burden on specialists and reduce the time needed to re-confirm the patient's condition, allowing for more efficient further examinations. However, GPs have broad but less specialized knowledge, which limits the accuracy of their diagnosis. Therefore, it is imperative to introduce an intelligent system to assist GPs in making decisions. This paper introduces two data augmentation methods, the Complaint Symptoms Integration Method and Symptom Dot Separating Method, to integrate essential information into the Integration dataset. Additionally, it proposes a hybrid architecture that fuses the features of words from different representation spaces. Experiments demonstrate that, compared to commonly used pre-trained attention-based models, our hybrid architecture delivers the best classification performance for four common neurological diseases on the enhanced Integration dataset. For example, the classification accuracy of the BERT+CNN hybrid architecture is 0.897, which is a 5.1% improvement over both BERT and CNN with 0.846. Finally, this paper develops an AI diagnosis assistant web application that leverages the superior performance of this architecture to help GPs complete primary diagnosis efficiently and accurately.
https://eprints.bournemouth.ac.uk/39326/
Source: PubMed
Hybrid Architecture Based Intelligent Diagnosis Assistant for GP
Authors: Wang, R., Kavisha, J., Rupert, P., Hailing, L., Jian Jun, Z. and Xiaosong, Y.
Journal: BMC Medical Informatics and Decision Making
Abstract:As the first point of contact for patients, General Practitioners (GPs) play a crucial role in the National Health Service (NHS). Anaccurate primary diagnosis from the GP can alleviate the burden on specialists and reduce the time needed to re-confirm the patient’scondition, allowing for more efficient further examinations. However, GPs have broad but less specialized knowledge, which limits theaccuracy of their diagnosis. Therefore, it is imperative to introduce an intelligent system to assist GPs in making decisions. This paperintroduces two data augmentation methods, the Complaint Symptoms Integration Method and Symptom Dot Separating Method, tointegrate essential information into the Integration dataset. Additionally, it proposes a hybrid architecture that fuses the features ofwords from different representation spaces. Experiments demonstrate that, compared to commonly used pre-trained attention-basedmodels, our hybrid architecture delivers the best classification performance for four common neurological diseases on the enhancedIntegration dataset. For example, the classification accuracy of the BERT+CNN hybrid architecture is 0.897, which is a 5.1%improvement over both BERT and CNN with 0.846. Finally, this paper develops an AI diagnosis assistant web application thatleverages the superior performance of this architecture to help GPs complete primary diagnosis efficiently and accurately.
https://eprints.bournemouth.ac.uk/39326/
Source: Manual
Hybrid architecture based intelligent diagnosis assistant for GP.
Authors: Wang, R., Jayathunge, K., Page, R., Li, H., Zhang, J.J. and Yang, X.
Journal: BMC medical informatics and decision making
Volume: 24
Issue: 1
Pages: 15
eISSN: 1472-6947
ISSN: 1472-6947
DOI: 10.1186/s12911-023-02398-8
Abstract:As the first point of contact for patients, General Practitioners (GPs) play a crucial role in the National Health Service (NHS). An accurate primary diagnosis from the GP can alleviate the burden on specialists and reduce the time needed to re-confirm the patient's condition, allowing for more efficient further examinations. However, GPs have broad but less specialized knowledge, which limits the accuracy of their diagnosis. Therefore, it is imperative to introduce an intelligent system to assist GPs in making decisions. This paper introduces two data augmentation methods, the Complaint Symptoms Integration Method and Symptom Dot Separating Method, to integrate essential information into the Integration dataset. Additionally, it proposes a hybrid architecture that fuses the features of words from different representation spaces. Experiments demonstrate that, compared to commonly used pre-trained attention-based models, our hybrid architecture delivers the best classification performance for four common neurological diseases on the enhanced Integration dataset. For example, the classification accuracy of the BERT+CNN hybrid architecture is 0.897, which is a 5.1% improvement over both BERT and CNN with 0.846. Finally, this paper develops an AI diagnosis assistant web application that leverages the superior performance of this architecture to help GPs complete primary diagnosis efficiently and accurately.
https://eprints.bournemouth.ac.uk/39326/
Source: Europe PubMed Central
Hybrid Architecture Based Intelligent Diagnosis Assistant for GP
Authors: Wang, R., Jayathunge, K., Page, R., Li, H., Zhang, J.J. and Yang, X.
Journal: BMC Medical Informatics and Decision Making
Volume: 24
ISSN: 1472-6947
Abstract:As the first point of contact for patients, General Practitioners (GPs) play a crucial role in the National Health Service (NHS). Anaccurate primary diagnosis from the GP can alleviate the burden on specialists and reduce the time needed to re-confirm the patient’scondition, allowing for more efficient further examinations. However, GPs have broad but less specialized knowledge, which limits theaccuracy of their diagnosis. Therefore, it is imperative to introduce an intelligent system to assist GPs in making decisions. This paperintroduces two data augmentation methods, the Complaint Symptoms Integration Method and Symptom Dot Separating Method, tointegrate essential information into the Integration dataset. Additionally, it proposes a hybrid architecture that fuses the features ofwords from different representation spaces. Experiments demonstrate that, compared to commonly used pre-trained attention-basedmodels, our hybrid architecture delivers the best classification performance for four common neurological diseases on the enhancedIntegration dataset. For example, the classification accuracy of the BERT+CNN hybrid architecture is 0.897, which is a 5.1%improvement over both BERT and CNN with 0.846. Finally, this paper develops an AI diagnosis assistant web application thatleverages the superior performance of this architecture to help GPs complete primary diagnosis efficiently and accurately.
https://eprints.bournemouth.ac.uk/39326/
Source: BURO EPrints