• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.39-44
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• 2021

In this paper, we propose a method of disease diagnosis system that can diagnose the health status of household pets for the people who lack veterinary knowledge. The proposed diagnosis system holds 50 different kinds of diseases with the symptoms for each of them as a database to provide results from symptom input. Each disease database has its own symptom codes for a disease, and by using the disease database, FCM clustering technique is applied to disease which outputs membership degree to determine diseases close to the input symptom as a pet diagnosis result. The implementation results of the proposed pet diagnosis system were obtained by the number of selected symptoms and the possibility values of the diseases that have the selected symptoms being sorted in descending order to derive top 3 diseases closest to the pet's symptom.

• Herbal Formula Science
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• v.25 no.1
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• pp.39-49
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• 2017

Objectives : The contents of the literature associated with the medical records were entered into the database. We want to find the structure and search methods for efficient utilization of the database. Methods : The contents were entered into the database using the 'Access 2014 of the MS'. The Query Sentences were created and utilized for a search. Results : We could find information about the prescriptions, medical records and patients by the herbs and symptom combinations using the single table named 'Integrated Knowledge' and queries. Integrated Knowledge is a table that gathered patient information, prescription information and symptom information together. Conclusions : If you store patient, prescription and symptom information on a single table, you could search and use the results by various combinations of the various elements included in the table. These results could help curing patients on the basis of evidence-based treatment at the clinics.

• Safety and Health at Work
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• v.11 no.3
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• pp.361-366
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• 2020

Background: This study aimed to identify the validity of breast cancer symptom questionnaire of worker's special health examination and its relationship with breast ultrasonography findings in young female night workers. Methods: The breast cancer symptom questionnaire data of worker's special health examination and breast ultrasonography results in young female shift workers who worked in one electronic manufacture company were collected from 2014 to 2018. Results: Of the 857 workers, 18 had a Breast Imaging Reporting and Database System category 4 or higher. Among other variables, shift work tenure alone was associated with the risk of having a Breast Imaging Reporting and Database System category higher than 4. The sensitivity, specificity, positive predictive value, and negative predictive value of the symptom questionnaire were 16.7%, 87.7%, 2.8%, and 98.0%, respectively. Conclusion: The current breast cancer symptom questionnaire of the worker's special health examination is inappropriate due to its low sensitivity and positive predictive value. In the future, female night workers will need alternative measures for more accurate screening for breast cancer.

• Journal of applied mathematics & informatics
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• v.32 no.5_6
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• pp.737-746
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• 2014

Treatment method refers to a principle or method for treating diseases in Traditional Korean Medicine(TKM). As doctors determine the ideal treatment for a patient's disease or symptom, they are also able to prescribe effective treatment means for the diseases or symptom such as medicinal materials, prescription, acupuncture and moxibustion. Therefore, if significant symptom-treatment method combinations are found from literature or database, proper treatment means for the patient's diseases or symptom may be presented to TKM doctors and enhanced treatment accuracy and efficiency can be expected. This study aims to analyze the relation between symptom and treatment method by interpreting hypotheses through null hypotheses to find significant symptom-treatment method combinations. This combinations suggested in this study will be compared with TKM experts analysis result to find an objective analysis method and eventually apply the method to medical big data, e.g., a huge amount of literature or treatment records.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.16 no.2
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• pp.33-46
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• 2012

Purpose : Nowadays excrement-related disease that repeats constipation and diarrhea is on the increase due to the change of dietary and lack of exercise, etc. We analyzed Bangyakhappyeon in order to find out the materia medica which is used for the excrement patterns. Methods : The database used in present thesisis consist of disease pattern, nature of medicinals and materia medica from Bangyakhappyeon was constructed. We analyzed the nature of medicinals of excrement patterns(or symptom) by frequency analysis and network analysis, and also searched main materia medica of excrement patterns(or symptom) by frequency analysis and rule mining. Results : We analyzed the nature of medicinals of excrement patterns(or symptom) in Bangyakhappyeon. And we researched the high frequency materia medica, high specificity materia medica and high frequent paired-drugs as main materia medica of excrement patterns(or symptom). Conclusion : This study found the information about frequency relationship between excrement patterns(or symptoms) and materia medica.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.29-35
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• 2021

While the population of pet dogs and pet-related markets are increasing, there is no convenient and reliable tool for pet health monitoring for pet owners/caregivers. In this paper, we propose a mobile platform-based pre-diagnosis system that pet owners can use for pre-diagnosis and obtaining information on coping strategies based on their observations of the pet dog's abnormal behavior. The proposed system constructs symptom-disease association databases for 100 frequently observed diseases under veterinarian guidance. Then, we apply the possibilistic fuzzy C-means algorithm to form the "probable disease" set and the "doubtable disease" set from the database. In the experiment, we found that the proposed system found almost all diseases correctly, with an average of 4.5 input symptoms and outputs 1.5 probable and one doubtable disease on average. The utility of this system is to alert the owner's attention to the pet dog's abnormal behavior and obtain an appropriate coping strategy before consult a veterinarian.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.327-332
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• 2009

In this paper, we propose the diagnosis system that can predict pet's state of health for pet lovers lacking a technical knowledge of dog-diseases. The proposed system deduces diseases of dogs from input symptoms by our database constructed with 105 kinds of diseases and symptoms. First, a disease is clustered by ART2, the self-learning method in neural network and secondly, the result values, outputs and the weight values clustered by the algorithm are stored to database. Finally, our system diagnoses the state of health by means of comparing the learned information of diseases with the input vectors of each symptom and the related results of questions on diseases. The correct information of diseases and symptom diagnosing is important to predict the state of health of dogs. Therefore, in this paper, the proposed system can manage symptoms and diseases efficiently by database and ART2. We ask veterinary specialist with the efficiency of our system. As a result, we could confirm the possibility as the auxiliary diagnosis system for dog diseases.

• Journal of Society of Preventive Korean Medicine
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• v.17 no.3
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• pp.129-141
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• 2013

Objectives : Anorexia is the primary symptom impinging cancer patients' Quality of Life. It is usually accompanied by gastrointestinal symptoms(GI symptoms). Thus, to measure anorexia symptom precisely, assessing anorexia and GI symptoms together is recommended. This study was designed to analyze cancer-related anorexia assessment tools, extract GI symptoms included in these tools and investigate usefulness of instruments in clinical trials. Methods : Instruments were selected by searching PubMed, PROQOLID database. We analyzed instruments by number of items, assessment method, type of question, GI symptoms. Results : 9 instruments were selected to assess cancer-related anorexia symptom. Most tools adopt Likert scale as response scale and 'during past week' as recall period. Assessment method of all 9 instruments is the self-administration. Questions measuring anorexia are able to be sorted into 3 forms (frequency, severeness, distress of anorexia symptom). Among the GI symptoms, nausea is included in all 9 instruments. In clinical trials of cancer-related anorexia, Edmonton Symptom Assessment Scale(ESAS) and Functional Assessment of Anorexia/Cachexia Therapy Questionnaire(FAACT) were selected as endpoint measure. Conclusions : The result showed that FAACT is the only specialized tool to assess cancer-related anorexia. To measure cancer-related anorexia precisely, the need to develop new instrument exists.

• The Journal of Korean Medicine
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• v.41 no.3
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• pp.1-8
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• 2020

Objectives: This paper aims to investigate the Donguibogam-based pattern diagnosis by applying natural language processing and machine learning. Methods: A database has been constructed by gathering symptoms and pattern diagnosis from Donguibogam. The symptom sentences were tokenized with nouns, verbs, and adjectives with natural language processing tool. To apply symptom sentences into machine learning, Word2Vec model has been established for converting words into numeric vectors. Using the pair of symptom's vector and pattern diagnosis, a pattern prediction model has been trained through Logistic Regression. Results: The Word2Vec model's maximum performance was obtained by optimizing Word2Vec's primary parameters -the number of iterations, the vector's dimensions, and window size. The obtained pattern diagnosis regression model showed 75% (chance level 16.7%) accuracy for the prediction of Six-Qi pattern diagnosis. Conclusions: In this study, we developed pattern diagnosis prediction model based on the symptom and pattern diagnosis from Donguibogam. The prediction accuracy could be increased by the collection of data through future expansions of oriental medicine classics.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.23-29
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• 2009

The heart sends blood to the body with heart rate. When heart rate for men is from 60 to 80 per minute, he is generally normal. However, if heart rate is less than the normal heart rate, the symptom is called by bradycardia. Otherwise, the symptom is called by tachycardia. These symptoms make him even to death. Therefore, heartbeat rate has a very important role in a healthy life. In this study, we studied on auto-extracting methods of heart rates from ECG, and compared them with those measured by naked eyes. The first auto-extracting method employs the 2-order differential equations to extract heart rate. The second method uses the autocorrelation coefficients to detect heart rate. To verify its efficacy and validity in practical applications, these method has been applied to MIT/BIH database.