scholarly journals Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Guo-Ping Liu ◽  
Jian-Jun Yan ◽  
Yi-Qin Wang ◽  
Jing-Jing Fu ◽  
Zhao-Xia Xu ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Chronic Gastritis ◽  
Information Gain ◽  
Diagnostic Model ◽  
Relevant Feature ◽  
Standard Scale ◽  
Multilabel Learning ◽  
Average Accuracy ◽  
Syndrome Diagnosis ◽  
Tcm Theory

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs).Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale.Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively.Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

scholarly journals Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Guo-Ping Liu ◽  
Jian-Jun Yan ◽  
Yi-Qin Wang ◽  
Wu Zheng ◽  
Tao Zhong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Practice ◽  
Chronic Gastritis ◽  
Diagnostic Model ◽  
Nonlinear Relationship ◽  
Cognitive Perspective ◽  
Multilabel Learning ◽  
Diagnostic Models ◽  
Syndrome Diagnosis ◽  
The Brain

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

scholarly journals Sociotherapy in the Time of COVID-19: A Critical Position Paper on the Importance of Sociology

2021 ◽  
pp. 193672442199827
Author(s):  
Sheila L. Cavanagh
Keyword(s):  
Clinical Practice ◽  
Behavioral Treatment ◽  
Health Professions ◽  
Position Paper ◽  
Diagnostic Model ◽  
Cognitive Behavioral Treatment ◽  
Critical Position ◽  
Freudian Psychoanalysis ◽  
The Social ◽  
Regulated Health Professions Act

This paper contends that sociotherapy, a sociologically informed approach to therapy, is a viable alternative to the diagnostic model recognized by the College of Registered Psychotherapists in Ontario (CRPO). The Psychotherapy Act (2007) along with the Regulated Health Professions Act (1991) gives the CRPO authorization to regulate the practice of psychotherapy and to control titles affiliated with the act of psychotherapy. I offer a discussion of sociotherapy and socioanalysis as clinical alternatives to the conservative and normalizing approaches endorsed by the College. I situate sociotherapy and socioanalysis in the discipline of sociology and in relation to Freudian psychoanalysis. I offer my own sociotherapeutic practice as an illustration of how the societal and the psychological, the social, and the psychic must be engaged in concert. I underscore the importance of dialogue, as opposed to diagnostics, interpretation as opposed to assessments and psychosocial contemplation as opposed to cognitive-behavioral treatment in clinical practice.

scholarly journals Enhanced Evidence-Based Chinese Medicine Clinical Practice Guidelines in Hong Kong: A Study Protocol for Three Common Diseases

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Nannan Shi ◽  
Linda L. D. Zhong ◽  
XueJie Han ◽  
Tat Chi Ziea ◽  
Bacon Ng ◽  
...  
Keyword(s):  
Hong Kong ◽  
Clinical Practice ◽  
Chinese Medicine ◽  
Text Mining ◽  
Cerebral Infarction ◽  
Study Protocol ◽  
Chronic Gastritis ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Evidence Based

We presented a study protocol of developing Chinese medicine clinical practice guidelines for three common diseases in Hong Kong, including insomnia, chronic gastritis, and cerebral infarction. This research project will be conducted in three phases. First phase is the preparation stage which consists of the establishment of steering committee and panel. Second phase involves 6 steps, which are searching and identifying evidence, text mining process, Delphi survey, synthesizing of data, consensus conference, and drafting guidelines. In this phase, text mining technique, evidence-based method, and formal consensus method are combined to get consolidated supporting data as the source of CM CPGs. The final phase comprised external reviews, dissemination, and updating. The outputs from this project will provide three CM CPGs for insomnia, chronic gastritis, and cerebral infarction for Hong Kong local use.

Age-specific survival in prostate cancer using machine learning

2020 ◽  
Vol 54 (2) ◽  
pp. 215-234
Author(s):  
M.N. Doja ◽  
Ishleen Kaur ◽  
Tanvir Ahmad
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Metastatic Prostate Cancer ◽  
Information Gain ◽  
Age Groups ◽  
Weighted Average ◽  
Age Group ◽  
Content Type ◽  
Complete Dataset ◽  
Average Accuracy

PurposeThe incidence of prostate cancer is increasing from the past few decades. Various studies have tried to determine the survival of patients, but metastatic prostate cancer is still not extensively explored. The survival rate of metastatic prostate cancer is very less compared to the earlier stages. The study aims to investigate the survivability of metastatic prostate cancer based on the age group to which a patient belongs, and the difference between the significance of the attributes for different age groups.Design/methodology/approachData of metastatic prostate cancer patients was collected from a cancer hospital in India. Two predictive models were built for the analysis-one for the complete dataset, and the other for separate age groups. Machine learning was applied to both the models and their accuracies were compared for the analysis. Also, information gain for each model has been evaluated to determine the significant predictors for each age group.FindingsThe ensemble approach gave the best results of 81.4% for the complete dataset, and thus was used for the age-specific models. The results concluded that the age-specific model had the direct average accuracy of 83.74% and weighted average accuracy of 79.9%, with the highest accuracy levels for age less than 60.Originality/valueThe study developed a model that predicts the survival of metastatic prostate cancer based on age. The study will be able to assist the clinicians in determining the best course of treatment for each patient based on ECOG, age and comorbidities.

scholarly journals A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shujie Xia ◽  
Jia Zhang ◽  
Guodong Du ◽  
Shaozi Li ◽  
Chi Teng Vong ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Syndrome Differentiation ◽  
Diagnostic Model ◽  
Multisystem Disease ◽  
K Value ◽  
Multilabel Learning ◽  
Complex Correlation ◽  
Tcm Syndrome ◽  
The Relationship ◽  
Differentiation Model

Background. Metabolic syndrome (MS) is a complex multisystem disease. Traditional Chinese medicine (TCM) is effective in preventing and treating MS. Syndrome differentiation is the basis of TCM treatment, which is composed of location and/or nature syndrome elements. At present, there are still some problems for objective and comprehensive syndrome differentiation in MS. This study mainly proposes a solution to two problems. Firstly, TCM syndromes are concurrent, that is, multiple TCM syndromes may develop in the same patient. Secondly, there is a lack of holistic exploration of the relationship between microscopic indexes, and TCM syndromes. In regard to these two problems, multilabel learning (MLL) method in machine learning can be used to solve them, and a microcosmic syndrome differentiation model can also be built innovatively, which can provide a foundation for the establishment of the next model of multidimensional syndrome differentiation in MS. Methods. The standardization scale of TCM four diagnostic information for MS was designed, which was used to obtain the results of TCM diagnosis. The model of microcosmic syndrome differentiation was constructed based on 39 physicochemical indexes by MLL techniques, called ML-kNN. Firstly, the multilabel learning method was compared with three commonly used single learning algorithms. Then, the results from ML-kNN were compared between physicochemical indexes and TCM information. Finally, the influence of the parameter k on the diagnostic model was investigated and the best k value was chosen for TCM diagnosis. Results. A total of 698 cases were collected for the modeling of the microcosmic diagnosis of MS. The comprehensive performance of the ML-kNN model worked obviously better than the others, where the average precision of diagnosis was 71.4%. The results from ML-kNN based on physicochemical indexes were similar to the results based on TCM information. On the other hand, the k value had less influence on the prediction results from ML-kNN. Conclusions. In the present study, the microcosmic syndrome differentiation model of MS with MLL techniques was good at predicting syndrome elements and could be used to solve the diagnosis problems of multiple labels. Besides, it was suggested that there was a complex correlation between TCM syndrome elements and physicochemical indexes, which worth future investigation to promote the development of objective differentiation of MS.

Prosvms based diagnostic model of chronic gastritis in TCM

2014 ◽  
Author(s):  
Jian-Jun Yan ◽  
Tao Zhong ◽  
Guo-Ping Liu ◽  
Yi-Qin Wang ◽  
Rui Guo ◽  
...  
Keyword(s):  
Chronic Gastritis ◽  
Diagnostic Model

scholarly journals Neurological Disorder Detection of Brain Abnormal Activities Using a New Enhanced Computer-aided Model

2021 ◽  
Vol 2128 (1) ◽  
pp. 012017
Author(s):  
D Shoieb ◽  
S Youssef
Keyword(s):  
Brain Regions ◽  
Autism Spectrum ◽  
Diagnostic Model ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Clinical Interviews ◽  
Number Of Children ◽  
Average Accuracy ◽  
Proposed Model ◽  
Diagnostic Time

Abstract In the field of neurodevelopmental disorders, Autism Spectrum Disorders (ASD) are recognized as one of the dramatically increased etiologically and clinically heterogeneous diseases. Although, increasing the number of children who have difficulties in communication or suffer from sudden malfunction of the brain, the current diagnostic approaches for those kind of disease are time-consuming and are mainly based on clinical interviews. In this paper, a new enhanced diagnostic model is introduced addressing many of the challenges which threats the analysis of Electroencephalography (EEG) signals. A preprocessing stage is proposed to choose the key segment of EEG channel and remove the artifacts in the EEG signals to enhance their quality. The proposed model uses a set of discriminative features based on discrete wavelet transform (DWT) such as skewness, standard division, shannon entropy and relative wave energy. Also, extracting cross correction between brain regions to detect abnormal connectivity and synchronization. Two EEG datasets are used to verify the accuracy of the proposed model. The fusion of two EEG dataset helps in building a more generalized mode to diagnose epilepsy and ASD. In the fused dataset, the combination of the mentioned features and Random Forest have produced a very promising diagnosis result with minimum diagnostic time, with an average accuracy equals to 96.78%. The proposed model obtained better classification accuracy compared to the existing methods.

scholarly journals Machine Learning for the Preliminary Diagnosis of Dementia

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Fubao Zhu ◽  
Xiaonan Li ◽  
Haipeng Tang ◽  
Zhuo He ◽  
Chaoyang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Information Gain ◽  
Diagnostic Model ◽  
Preliminary Diagnosis ◽  
Early Stages ◽  
Item Questionnaire ◽  
Diagnostic Models ◽  
Diagnosis Of Dementia ◽  
Bayes Algorithm

Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.

Chronic Gastritis Syndrome Diagnosis and Symptom Selection with Ensemble Learning

2021 ◽  
Author(s):  
Qian Liu ◽  
Jingbin Niu ◽  
Weixi Mao ◽  
Yixin Zheng ◽  
Guoping Liu ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Chronic Gastritis ◽  
Syndrome Diagnosis

Early Prediction of Malignant Mesothelioma: An Approach towards Non-invasive Method

2021 ◽  
Vol 16 ◽  
Author(s):  
Shakir Shabbir ◽  
M. Shahzad Asif ◽  
Talha Mahboob Alam ◽  
Zeeshan Ramzan
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Malignant Mesothelioma ◽  
Information Gain ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Machine Learning Techniques ◽  
Diagnostic Model ◽  
Diagnostic Measures

Background: Malignant Mesothelioma (MM) is a rare but aggressive tumor that arises in the lungs. Commonly, costly imaging and laboratory resources, i.e., X-ray imaging, magnetic resonance imaging (MRI), positron emission tomography (PET) scans, biopsies, and blood tests, have already been utilized for the diagnosis of MM. Even though these diagnostic measures are expensive and unavailable in distant areas, some of these diagnostic methods are also very painful for the patient, including biopsy and cytology of pleural fluid. Objective: In this study, we proposed a diagnostic model for early identification of MM via machine learning techniques. We explored the health records of 324 Turkish patients, which showed the symptoms related to MM. The data of patients included socio-economic, geographical, and clinical features. Methods: Different feature selection methods have been employed for the selection of significant features. To overcome the data imbalance problem, various data-level resampling techniques have been utilized to obtain efficient results. The gradient boosted decision tree (GBDT) method has been used to develop the diagnostic model. The performance of the GBDT model is also compared with traditional machine learning algorithms. Results and Conclusion: Our model's results outperformed other models, both on balance and imbalance data. The results clearly show that undersampling techniques outperformed imbalanced data without resampling based on accuracy and receiving operating characteristic (ROC) value. Conversely, it has also been observed that oversampling techniques outperformed undersampling and imbalanced data based on accuracy and ROC. All classifiers employed in this study achieved efficient results utilizing feature selection-based methods (OneR, information gain, and Relief-F), but the other two methods (gain ratio and correlation) results were not entirely promising. Finally, when the combination of Synthetic Minority Oversampling Technique (SMOTE) and OneR was applied with GBDT, it gave the most favorable results based on accuracy, F-measure, and ROC. The diagnosis model has also been deployed to assist doctors, patients, medical practitioners, and other healthcare professionals for early diagnosis and better treatment of MM.

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