Intelligent medicine最新文献

Background

Bacterial vaginosis is a polymicrobial syndrome in which the homeostasis exerted by the Latobacillus species that protect the vaginal mucosa has been lost. This study explored the data balancing process with the intention of improving the quality of association rules. The article aimed to balance the unbalanced multiclass dataset to improve association rule creation.

Methods

A dataset with 201 observations and 58 variables was analyzed. A preconstructed dataset was used. The authors collected the data between August 2016 and October 2018 in Tabasco, Mexico. The study population comprised sexually active women ages 18 to 50 who underwent gynecological inspection at the infectious and metabolic diseases research laboratory at the Universidad Juarez Autonoma de Tabasco. To determine the best $k$-value, the random-forest algorithm was used and the balancing was performed with the synthetic minority over-sampling technique (SMOTE), random over-sampling examples (ROSE), and adaptive syntetic sampling approach for imbalanced learning (ADASYN) algorithms. The Apriori algorithm created the rules and to select rules with statistical significance, the is.redundant(), is.significant(), and is.maximal() functions and quality metric Fisher’s exact tes were used. The biological validation was carried out by the expert (bacteriologist).

Results

The ADASYN algorithm at $K=9$ the out of the bag (OOB) error was zero, this was the best $K$-values. In the balancing process the ADASYN algorithm show best the performance. From the dataset balanced with ADASYN, the apriori algorithm created the association rules and the selection with the quality metric Fisher’s exact test, and the biological validation reported 13 rules. Gram - bacteria Atopobium vaginae, Gardnerella vaginalis, Megasphaera filotipo 1, Mycoplasma hominis and Ureaplasma parvum were detected by the apriori algorithm from the balanced dataset.

Conclusion

Balancing may improve the creation of association rules to efficiently model the bacteria that cause bacterial vaginosis.

Background

Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies. Machine learning (ML) models have shown satisfactory performance in short-term mortality prediction in patients with heart disease, whereas their utility in long-term predictions is limited. This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality.

Methods

This study used publicly available data from the Collaboration Center of Health Information Application at the Ministry of Health and Welfare, Taiwan, China. The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction (AMI) between November 2003 and September 2004. We collected and analyzed mortality data up to December 2018. Medical records were used to gather demographic and clinical data, including age, gender, body mass index, percutaneous coronary intervention status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction, and non-ST-segment elevation myocardial infarction. Using the data, collected from 139 patients with AMI, from medical and demographic records as well as two recently introduced biomarkers, brachial pre-ejection period (bPEP) and brachial ejection time (bET), we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality within 14 years. A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression (LR) as the baseline method.

Results

The developed ML models achieved significantly better performance compared to the baseline LR (C-Statistic, 0.80 for random forest, 0.79 for AdaBoost, and 0.78 for XGBoost, vs. 0.77 for LR) (P_RF < 0.001, P_AdaBoost < 0.001, and P_XGBoost < 0.05). Adding bPEP and bET to our feature set significantly improved the performance of the algorithm, leading to an absolute increase in C-statistic of up to 0.03 (C-statistic, 0.83 for random forest, 0.82 for AdaBoost, and 0.80 for XGBoost, vs. 0.74 for LR) (P_RF < 0.001, P_AdaBoost < 0.001, P_XGBoost < 0.05).

Conclusion

The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases. This advancement may enable better treatment prioritization for high-risk individuals.

Neuropsychological disorders (e.g., dementia, epilepsy, brain cancer, autism, stroke, and multiple sclerosis) adversely affect the quality of life of patients and their families; moreover, in some instances, they may lead to loss of life. The primary aim was to evaluate and compare the use of machine learning in neuropsychological research in contrast to traditional approaches such as through case studies. This was achieved by referring to earlier studies on this subject. This article presented the use of support vector machines (SVMs) and convolutional neural networks (CNN) for detecting and predicting neuropsychological diseases, such as dementia and Alzheimer's disease. Challenges in using these models include data availability, quality, variability, model interpretability, and validation. Experimental findings have demonstrated the potential of these models in this field. It has been shown that SVM models are robust and efficient in processing and classifying data, particularly in neuroimaging applications, such as magnetic resonance imaging (MRI). CNNs have excelled in handling visual input; thus, they have been used in neuroimaging segregation, recognition, and classification, with applications in brain tumor segmentation, radiation therapy, robotic neurosurgery, and disease prediction. Future research will explore asymmetric differences among left- and right-handed patients, incorporate longitudinal studies, and utilize larger sample sizes. The use of machine learning models has the potential to revolutionize the diagnosis and treatment of neuropsychological diseases, allowing for early detection and intervention. This approach could offer significant advantages to healthcare, such as cost-effective diagnosis and treatment, to help save lives and preserve the quality of life of patients.

Background

Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors; however, it has some drawbacks. This study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological images.

Methods

The most suitable process was selected through multiple experiments by comparing multiple methods and features for classification. First, the U-net was applied to segment the image. Next, the nucleus was extracted from the segmented image, and the minimum spanning tree (MST) diagram structure that can capture the topological information was drawn. The third step was to extract the graph-curvature features of the histopathological image according to the MST image. Finally, by inputting the graph-curvature features into the classifier, the recognition results for benign or malignant cancer can be obtained.

Results

During the experiment, we used various methods for comparison. In the image segmentation stage, U-net, watershed algorithm, and Otsu threshold segmentation methods were used. We found that the U-net method, combined with multiple indicators, was the most suitable for segmentation of histopathological images. In the feature extraction stage, in addition to extracting graph-edge and graph-curvature features, several basic image features were extracted, including the red, green and blue feature, gray-level co-occurrence matrix feature, histogram of oriented gradient feature, and local binary pattern feature. In the classifier design stage, we experimented with various methods, such as support vector machine (SVM), random forest, artificial neural network, K nearest neighbors, VGG-16, and inception-V3. Through comparison and analysis, it was found that classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into the SVM classifier.

Conclusion

This study created a unique feature, the graph-curvature feature, based on the MST to represent and analyze histopathological images. This graph-based feature could be used to identify benign and malignant cells in histopathological images and assist pathologists in diagnosis.

Background

Parastomal hernia is one of the potential complications after enterostomy. There is currently no early risk assessment tool for parastomal hernia.

Methods

The current investigation was conducted using retrospective studies. A total of 302 cases were used develop and internally to validate a nomogram prediction model, and 67 cases were used for external validation. Independent risk factors for parastomal hernia after permanent sigmoid colostomy were assessed via univariate analysis and binary logistic regression analysis. The nomogram prediction model was established based on independent risk factors.

Results

Body mass index, serum albumin, age, sex, and stoma diameter were independent risk factors for parastomal hernia. The areas under the receiver operating characteristic curves were 0.909 in the development group and 0.801 in the validation group. The Hosmer-Lemeshow test (P > 0.05) and calibration curves indicated good consistency between actual observations and predicted probabilities.

Conclusions

A nomogram prediction model was constructed and validated based on risk factors for parastomal hernia. The nomogram could be generalized to patients undergoing surgery for stoma by specialized surgeons to provide relevant references for stoma patients.

Objective

Speech recognition technology is widely used as a mature technical approach in many fields. In the study of depression recognition, speech signals are commonly used due to their convenience and ease of acquisition. Though speech recognition is popular in the research field of depression recognition, it has been little studied in somatisation disorder recognition. The reason for this is the lack of a publicly accessible database of relevant speech and benchmark studies. To this end, we introduced our somatisation disorder speech database and gave benchmark results.

Methods

By collecting speech samples of somatisation disorder patients, in cooperation with the Shenzhen University General Hospital, we introduced our somatisation disorder speech database, the Shenzhen Somatisation Speech Corpus (SSSC). Moreover, a benchmark for SSSC using classic acoustic features and a machine learning model was proposed in our work.

Results

To obtain a more scientific benchmark, we compared and analysed the performance of different acoustic features, i. e., the full ComPare feature set, or only Mel frequency cepstral coefficients (MFCCs), fundamental frequency (F0), and frequency and bandwidth of the formants (F1-F3). By comparison, the best result of our benchmark was the 76.0% unweighted average recall achieved by a support vector machine with formants F1–F3.

Conclusion

The proposal of SSSC may bridge a research gap in somatisation disorder, providing researchers with a publicly accessible speech database. In addition, the results of the benchmark could show the scientific validity and feasibility of computer audition for speech recognition in somatization disorders.

Objective

The incidence of cardiovascular diseases (CVD) is rising rapidly worldwide. Some forms of CVD, such as stroke and heart attack, are more common among patients with certain conditions. Atherosclerosis development is a major factor underlying cardiovascular events, such as heart attack and stroke, and its early detection may prevent such events. Ultrasound imaging of carotid arteries is a useful method for diagnosis of atherosclerotic plaques; however, an automated method to classify atherosclerotic plaques for evaluation of early-stage CVD is needed. Here, we propose an automated method for classification of high-risk atherosclerotic plaque ultrasound images.

Methods

Five deep learning (DL) models (VGG16, ResNet-50, GoogLeNet, XceptionNet, and SqueezeNet) were used for automated classification and the results compared with those of a machine learning (ML)-based technique, involving extraction of 23 texture features from ultrasound images and classification using a Support Vector Machine classifier. To enhance model interpretability, output gradient-weighted convolutional activation maps (GradCAMs) were generated and overlayed on original images.

Results

A series of indices, including accuracy, sensitivity, specificity, F1-score, Cohen-kappa index, and area under the curve values, were calculated to evaluate model performance. GradCAM output images allowed visualization of the most significant ultrasound image regions. The GoogLeNet model yielded the highest accuracy (98.20%).

Conclusion

ML models may be also suitable for applications requiring low computational resource. Further, DL models could be more completely automated than ML models.

Background

Brain stroke is a serious health issue that requires timely and accurate prediction for effective treatment and prevention. This study described a hybrid system that used the best feature selection method and classifier to predict brain stroke.

Methods

The Stroke Prediction Dataset from Kaggle was used for this study. Synthetic minority over-sampling technique (SMOTE) analysis was used to accomplish class balancing. Accuracy, sensitivity, specificity, precision, and the F-Measure were the main performance parameters considered for investigation. To determine the best combination for predicting brain stroke, the performance of five classifiers, Naïve Bayes (NB), support vector machine (SVM), random forest (RF), adaptive boosting (Adaboost), and extreme gradient boosting (XGBoost), was compared along with three feature selection techniques, mutual information (MI), Pearson correlation (PC), and feature importance (FI). The performance parameters were assessed using k-fold cross-validation.

Results

The hybrid system proposed in this study identified a reduced set of features that were able to effectively predict brain stroke. FI provided a feature reduction ratio of 36.3%. The most successful hybrid system for predicting brain stroke used FI as the feature selection technique and RF as the classifier, achieving an accuracy of 97.17%.

Conclusion

The proposed system predicted brain stroke with high accuracy. These findings could be used to inform the early detection and prevention of brain stroke, allowing healthcare professionals to provide timely and targeted care to at-risk patients.