Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data

IntroductionPreterm birth is a leading cause of infant mortality and morbidity. Despite the improvement in the overall mortality in premature infants, the intact survival of these infants remains a significant challenge. Screening the physical growth of infants is fundamental to potentially reducing the escalation of this disorder. Recently, machine learning models have been used to predict the growth restrictions of infants; however, they frequently rely on conventional risk factors and cross-sectional data and do not leverage the longitudinal database associated with medical data from laboratory tests.MethodsThis study aimed to present an automated interpretable ML-based approach for the prediction and classification of short-term growth outcomes in preterm infants. We prepared four datasets based on weight and length including weight baseline, length baseline, weight follow-up, and length follow-up. The CHA Bundang Medical Center Neonatal Intensive Care Unit dataset was classified using two well-known supervised machine learning algorithms, namely support vector machine (SVM) and logistic regression (LR). A five-fold cross-validation, and several performance measures, including accuracy, precision, recall and F1-score were used to compare classifier performances. We further illustrated the models’ trustworthiness using calibration and cumulative curves. The visualized global interpretations using Shapley additive explanation (SHAP) is provided for analyzing variables’ contribution to final prediction.ResultsBased on the experimental results with area under the curve, the discrimination ability of the SVM algorithm was found to better than that of the LR model on three of the four datasets with 81%, 76% and 72% in weight follow-up, length baseline and length follow-up dataset respectively. The LR classifier achieved a better ROC score only on the weight baseline dataset with 83%. The global interpretability results revealed that pregnancy-induced hypertension, gestational age, twin birth, birth weight, antenatal corticosteroid use, premature rupture of membranes, sex, and birth length were consistently ranked as important variables in both the baseline and follow-up datasets.DiscussionThe application of machine learning models to the early detection and automated classification of short-term growth outcomes in preterm infants achieved high accuracy and may provide an efficient framework for clinical decision systems enabling more effective monitoring and facilitating timely intervention

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis

인공지능에 의한 돼지 소장상피세포의 장벽기능 식별

학위논문(석사)--서울대학교 대학원 :농업생명과학대학 농생명공학부,2020. 2. Cheol-Heui Yun.Given the importance of monitoring intestinal permeability and the significant healthcare cost associated with the gut barrier disruption, automatic detection of barrier disruption pattern in porcine intestinal epithelial cells (IPEC-J2) using automatically computer-aided detection models based on a deep convolutional neural network for early detection and interpretation in measuring intestinal permeability is an area of active research and adequate experimental models are required to further understand the grade of localization and disruption of IPEC-J2 tight junction proteins. In the present study, a deep learning-based ensemble model to build a classifier to automatically analyze and extract features from input images in order to accurately assess the grade of localization and disruption of tight junction proteins (TJ) in IPEC-J2 have been proposed. Different data augmentation techniques including horizontal and vertical flips, rotating, zooming, contrast adjustment and brightness enhancement with different parameters are employed to increase the dataset size and tackle the over-fitting problem. At first, the experiments began with evaluating the performance of 8 state-of-the-art deep CNN architectures namely, VGG-Net, InceptionV3, MobileNet, DenseNet, Xception, NAS-Net, InceptionResNetV2 and ResNet models on IPEC-J2 cell image classification. Transfer learning is a common strategy in training deep CNN models. Using this strategy, the weights that are already learned on a cross-domain dataset to initialize weights of deep CNN models can be transferred in this research. The final results showed that the deep CNN ensemble of InceptionV3 and DenseNet201 achieved the best result with an accurate detection rate of 99.22% than the individual InceptionV3 architecture (95.03%) and the individual DenseNet201 architecture (91.11%). The second-best ensemble architecture was the ensemble of InceptionV3 and MobileNet with an accuracy of 97.78% than the individual InceptionV3 architecture (95.03%) and the individual MobileNet architecture (95.82%.) Collectively, employing CNN models could be considered as an automatic visual inspection system for the recognition, grading of expression, localization and disruption of tight junction proteins in epithelial cells with less misdiagnosis (false positive or false negative) and error rate, and also reduce the heavy workload of manual diagnosis.Ι. Review of Literature 1 1. Convolutional neural network 1 1.1 Characterization and design of generalized convolutional neural network 1 1.1.1 Convolution layer 1 1.1.2 Rectified linear unit function 2 1.1.3 Pooling layer 3 1.1.4 Fully connected layer 3 1.2 Feature extraction using transfer learning 5 1.2.1 InceptionV3: 5 1.2.2 Xception: 5 1.2.3 MobileNet: 6 1.2.4 NAS-Net: 6 1.2.5 ResNet50: 6 1.2.6 DenseNet: 7 1.2.7 VGG-Net: 7 1.2.8 InceptionResNetV2: 7 2. Intestinal barrier and pathways of permeability 8 3. Tight junction proteins 11 3.1 Characterization of intestinal tight junction proteins 11 3.2 Zonula occluden family 11 3.3 Occludin family 12 3.4 Claudin family 12 4. Experimental evaluation of intestinal barrier function 13 4.1 Limitation for the evaluation of intestinal permeability 13 4.2 Future direction for the evaluation of the intestinal permeability 17 5. The beneficial effect of deep convolutional neural network 18 П. Introduction 19 Ш. Materials and methods 21 1. Methodology 21 2. Motivation and Contribution 22 2.1 The contribution of the proposed ensemble model 22 2.2 Two-path ensemble architecture for IPEC-J2 cell image classification 23 ΙV. Experiment 26 1. Dataset description 26 2. Data pre-processing 34 2.1 Resizing: 34 2.2 Z-score image normalization: 34 2.3 Image normalization: 34 3. Data augmentation 34 4. Metrics for performance evaluation 36 5. Experimental Setup 36 V. Results 37 1. Deep features extraction based on transfer learning 37 2. Deep feature extraction based on deep learning-based ensemble models 39 VΙ. Discussion 43 VΠ. Literature cited 45 VШ. Acknowledgement 58 ΙХ. Appendix 60Maste

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

IntroductionDepression is a prevalent disorder worldwide, with potentially severe implications. It contributes significantly to an increased risk of diseases associated with multiple risk factors. Early accurate diagnosis of depressive symptoms is a critical first step toward management, intervention, and prevention. Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. Despite the challenges to better understanding the association between nutritional risk factors and the occurrence of depression, assessing the interplay of these markers through supervised machine learning remains to be fully explored.MethodsThis study aimed to determine the ability of machine learning-based decision support methods to identify the presence of depression using publicly available health data from the Korean National Health and Nutrition Examination Survey. Two exploration techniques, namely, uniform manifold approximation and projection and Pearson correlation, were performed for explanatory analysis among datasets. A grid search optimization with cross-validation was performed to fine-tune the models for classifying depression with the highest accuracy. Several performance measures, including accuracy, precision, recall, F1 score, confusion matrix, areas under the precision-recall and receiver operating characteristic curves, and calibration plot, were used to compare classifier performances. We further investigated the importance of the features provided: visualized interpretation using ELI5, partial dependence plots, and local interpretable using model-agnostic explanations and Shapley additive explanation for the prediction at both the population and individual levels.ResultsThe best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. The explainable results revealed a complementary observation of the relative changes in feature values, and, thus, the importance of emergent depression risks could be identified.DiscussionThe strength of our approach is the large sample size used for training with a fine-tuned model. The machine learning-based analysis showed that the hyper-tuned model has empirically higher accuracy in classifying patients with depressive disorder, as evidenced by the set of interpretable experiments, and can be an effective solution for disease control

Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms

Background and ObjectiveIdentifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. MethodsWe included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. ResultsOf the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. ConclusionsTree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.N