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

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

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

Lipid peroxidation and antioxidant enzymes activity in controlled and uncontrolled Type 2 diabetic patients

BACKGROUND: This study was designed to compare lipid peroxidation and antioxidant enzymes activity in Type 2 diabetes patients with good or weak glycemic control. METHODS: In this case-control study, 62 Type 2 diabetic patients with glycated hemoglobin (HbA1c) between 6 and 8 were enrolled as the controlled group and 55 patients with HbA1c &gt; 8 were selected as an uncontrolled group. Patients were all referred to Iranian Diabetes Association in Tehran, Iran, from 2010 onward. Groups were chosen by convenience sampling and were matched based on age, sex and duration of disease. Demographic questionnaire, two 24-hour food recall, HbA1c, insulin, malondialdehyde (MDA), superoxide dismutase (SOD), and catalase were measured in blood samples. Data were analyzed by Food Processor II and SPSS software. RESULTS: A mean daily consumption of energy, carbohydrate, protein, and fat was not significantly different between two groups. MDA in the uncontrolled group was significantly higher than controlled group (2.03 &plusmn; 0.88 vs. 1.65 &plusmn; 1.01 nmol/ml; P = 0.030). A mean SOD was slightly higher in the uncontrolled group comparing to the control group (843.3 &plusmn; 101.9 vs. 828.0 &plusmn; 127.3 U/g Hb; P = 0.400). CONCLUSION: These data suggest that MDA as a lipid peroxidation indicator is higher in uncontrolled diabetes probably due to chronic high blood sugar followed by higher oxidative stress. &nbsp;&nbsp;</p

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

Dietary intake, growth and development of children with ADHD in a randomized clinical trial of Ritalin and Melatonin co-administration: Through circadian cycle modification or appetite enhancement?

Objective: It is postulated that ritalin may adversely affect sleep, appetite, weight and growth of some children with ADHD. Therefore, we aimed to evaluate melatonin supplementation effects on dietary intake, growth and development of children with ADHD treated with ritalin through circadian cycle modification and appetite mechanisms.Method: After obtaining consent from parents, 50 children aged 7-12 with combined form of AD/HD were randomly divided into two groups based on gender blocks: one received melatonin (3 or 6 mg based on weight) combined with ritalin (1mg/kg) and the other took placebo combined with ritalin (1mg/kg) in a double blind randomized clinical trial. Three-day food record, and standard weight and height of children were evaluated prior to the treatment and 8 weeks after the treatment. Children’s appetite and sleep were evaluated in weeks 0, 2, 4 and 8. Hypotheses were then analyzed using SPSS17.Results: Paired sample t-test showed significant changes in sleep latency (23.15±15.25 vs. 17.96±11.66; p=0.047) and total sleep disturbance score (48.84±13.42 vs. 41.30±9.67; p=0.000) before and after melatonin administration, respectively. However, appetite and food intake did not change significantly during the study. Sleep duration and appetite were significantly correlated in melatonin group (Pearson r=0.971, p=0.029). Mean height (138.28±16.24 vs. 141.35±16.78; P=0.000) and weight (36.73±17.82 vs. 38.97±17.93; P=0.005) were significantly increased in melatonin treated children before and after the trial.Conclusion: Administration of melatonin along with ritalin improves height and weight growth of children. These effects may be attributed to circadian cycle modification, increasing sleep duration and the consequent more growth hormone release during sleep

Erythrocyte membrane fatty acid profile & serum cytokine levels in patients with non-alcoholic fatty liver disease

Background & objectives: Fatty acids may affect the expression of genes, and this process is influenced by sex hormones. Cytokines are involved in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), so this study was aimed to assess the association of erythrocyte membrane fatty acids with three cytokines and markers of hepatic injury in NAFLD patients and to explore whether these associations were the same in both sexes. Methods: In this cross-sectional study, 62 consecutive patients (32 men and 30 women) with NAFLD during the study period. Tumour necrosis factor-α (TNF-α), interleukin 6 (IL-6), monocyte chemoattractant protein-1 (MCP-1), aspartate aminotransferase and alanine aminotransferase were measured in a fasting serum sample, and Fibroscan was conducted for each individual. Gas chromatography was used to measure erythrocyte membrane fatty acids. Univariate and multiple linear regressions were used to analyze data. Results: In men, IL-6 had a significant (P <0.05) positive association with total ω-3 polyunsaturated fatty acids (PUFAs). In women, TNF-α had a significant positive association with total ω-3 (P <0.05) and ω-6 (P <0.01) PUFAs, IL-6 had a significant (P <0.05) positive association with total monounsaturated fatty acids and MCP-1 had a significant positive association with total trans-fatty acids (P <0.05). No significant associations were observed between erythrocyte membrane fatty acids and liver enzymes or Fibroscan report in both sexes. In this study, women were significantly older than men [51 (42.75-55) vs 35.5 (29-52), P <0.01], so the associations were adjusted for age and other confounders. Interpretation & conclusions: Erythrocyte membrane fatty acid profile was not associated with serum liver enzymes or Fibroscan reports in NAFLD patients, but it had significant associations with serum TNF-α, IL-6 and MCP-1 and these associations were probably sex dependent