Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases
The healthcare domain is a knowledge-intensive domain. The quality of clinical diagnosis relies mainly on the medical knowledge and experience held by doctors. However, the ability of a single doctor is very limited, so the quality of clinical diagnosis is currently not high. In this paper, an aided...
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2025-01-01
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| author | Deyan Chen Hong Zhao Xia Zhang |
| author_facet | Deyan Chen Hong Zhao Xia Zhang |
| author_sort | Deyan Chen |
| collection | DOAJ |
| description | The healthcare domain is a knowledge-intensive domain. The quality of clinical diagnosis relies mainly on the medical knowledge and experience held by doctors. However, the ability of a single doctor is very limited, so the quality of clinical diagnosis is currently not high. In this paper, an aided diagnosis method based on domain semantic knowledge bases is proposed. First, a domain semantic knowledge base is established by extracting and refining the knowledge of the medicine subject matter domain from the Freebase resource description framework dumps. Then, based on the semantic knowledge base, the algorithms for calculating the weights of the symptoms in the knowledge base, the relative weights of the diseases related to the input symptom set from a patient, and the related symptom set related to the input symptom set from the patient are proposed. Finally, the clinical medical record data of several common diseases are selected to make an evaluation on the proposed method. For each medical record, the symptom information is extracted from the chief complaint as the patient’s input symptom set. Based on the input symptom set, the method of this paper is used to obtain the list of related diseases and the ranking of disease relative weights. From the disease relevance rankings, the Top-1 (first diagnosis) and Top-3 (first three diagnoses) are compared with the doctor’s diagnoses in the medical records. Among them, the ovarian cyst has the highest Top-1 and Top-3 hit rates of 67.3% and 89.1%, respectively. Followed by acute upper respiratory tract infection, Top-1 and Top-3 hit rates are 56.6% and 85.2%, respectively. The average Top-1 and Top-3 hit rates are 47.9% and 79.7%, respectively. Compared with the relevant methods, the method of this paper is better. The evaluation results show that based on the domain semantic knowledge base and the aided diagnosis method of diseases constructed in this paper, it is possible to provide the aided diagnosis services of a large number of common diseases for general practitioners (especially inexperienced doctors) at the grass-roots level as well as self-diagnosis services of diseases for patients. |
| format | Article |
| id | doaj-art-efdb54a0bc214f899dc04ade11f56afe |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-efdb54a0bc214f899dc04ade11f56afe2025-08-20T03:55:48ZengIEEEIEEE Access2169-35362025-01-011312276912278110.1109/ACCESS.2018.28757228807365Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge BasesDeyan Chen0https://orcid.org/0000-0001-8141-662XHong Zhao1Xia Zhang2https://orcid.org/0000-0003-2199-1088School of Computer Science and Engineering, Northeastern University, Shenyang, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang, ChinaThe healthcare domain is a knowledge-intensive domain. The quality of clinical diagnosis relies mainly on the medical knowledge and experience held by doctors. However, the ability of a single doctor is very limited, so the quality of clinical diagnosis is currently not high. In this paper, an aided diagnosis method based on domain semantic knowledge bases is proposed. First, a domain semantic knowledge base is established by extracting and refining the knowledge of the medicine subject matter domain from the Freebase resource description framework dumps. Then, based on the semantic knowledge base, the algorithms for calculating the weights of the symptoms in the knowledge base, the relative weights of the diseases related to the input symptom set from a patient, and the related symptom set related to the input symptom set from the patient are proposed. Finally, the clinical medical record data of several common diseases are selected to make an evaluation on the proposed method. For each medical record, the symptom information is extracted from the chief complaint as the patient’s input symptom set. Based on the input symptom set, the method of this paper is used to obtain the list of related diseases and the ranking of disease relative weights. From the disease relevance rankings, the Top-1 (first diagnosis) and Top-3 (first three diagnoses) are compared with the doctor’s diagnoses in the medical records. Among them, the ovarian cyst has the highest Top-1 and Top-3 hit rates of 67.3% and 89.1%, respectively. Followed by acute upper respiratory tract infection, Top-1 and Top-3 hit rates are 56.6% and 85.2%, respectively. The average Top-1 and Top-3 hit rates are 47.9% and 79.7%, respectively. Compared with the relevant methods, the method of this paper is better. The evaluation results show that based on the domain semantic knowledge base and the aided diagnosis method of diseases constructed in this paper, it is possible to provide the aided diagnosis services of a large number of common diseases for general practitioners (especially inexperienced doctors) at the grass-roots level as well as self-diagnosis services of diseases for patients.https://ieeexplore.ieee.org/document/8807365/Ontologydomain semantic knowledge basesaided diagnosis of diseasessymptom weightsdisease relative weightsrelated symptoms |
| spellingShingle | Deyan Chen Hong Zhao Xia Zhang Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases IEEE Access Ontology domain semantic knowledge bases aided diagnosis of diseases symptom weights disease relative weights related symptoms |
| title | Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases |
| title_full | Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases |
| title_fullStr | Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases |
| title_full_unstemmed | Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases |
| title_short | Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases |
| title_sort | research on the aided diagnosis method of diseases based on domain semantic knowledge bases |
| topic | Ontology domain semantic knowledge bases aided diagnosis of diseases symptom weights disease relative weights related symptoms |
| url | https://ieeexplore.ieee.org/document/8807365/ |
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