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Sea ice at the North Pole is vital to global climate dynamics. However, accurately forecasting sea ice poses a significant challenge due to the intricate interaction among multiple variables. Leveraging the capability to integrate multiple inputs and powerful performances seamlessly, many studies have turned to neural networks for sea ice forecasting. This paper introduces a novel deep architecture named Unicorn, designed to forecast weekly sea ice. Our model integrates multiple time series images within its architecture to enhance its forecasting performance. Moreover, we incorporate a bottleneck layer within the U-Net architecture, serving as neural ordinary differential equations with convolutional operations, to capture the spatiotemporal dynamics of latent variables. Through real data analysis with datasets spanning from 1998 to 2021, our proposed model demonstrates significant improvements over state-of-the-art models in the sea ice concentration forecasting task. It achieves an average MAE improvement of 12% compared to benchmark models. Additionally, our method outperforms existing approaches in sea ice extent forecasting, achieving a classification performance improvement of approximately 18%. These experimental results show the superiority of our proposed model. A preprint version of this work is available at this url https://arxiv.org/abs/2405.03929 .

Sea ice at the North Pole is vital to global climate dynamics. However, accurately forecasting sea ice poses a significant challenge due to the intricate interaction among multiple variables. Leveraging the capability to integrate multiple inputs and powerful performances seamlessly, many studies have turned to neural networks for sea ice forecasting. This paper introduces a novel deep architecture named Unicorn, designed to forecast weekly sea ice. Our model integrates multiple time series images within its architecture to enhance its forecasting performance. Moreover, we incorporate a bottleneck layer within the U-Net architecture, serving as neural ordinary differential equations with convolution operations, to capture the spatiotemporal dynamics of latent variables. Through real data analysis with datasets spanning from 1998 to 2021, our proposed model demonstrates significant improvements over state-of-the-art models in the sea ice concentration forecasting task. It achieves an average MAE improvement of 12% compared to benchmark models. Additionally, our method outperforms existing approaches in sea ice extent forecasting, achieving a classification performance improvement of approximately 18%. These experimental results show the superiority of our proposed model.

Restless legs syndrome (RLS) is a relatively common neurosensory disorder that causes an irresistible urge for leg movement. RLS causes sleep disturbances and reduced quality of life, but accurate diagnosis remains challenging owing to the reliance on subjective reporting. This study aimed to propose a predictive machine learning model based on digital phenotypes for RLS diagnosis. Self-reported lifestyle data were integrated via a smartphone application with objective biometric data from wearable devices to obtain 85 features processed based on circadian rhythms. Prediction models used these features to distinguish between the non-RLS (International Restless Legs Study Group Severity Rating Scale [IRLS] score ≤ 10) and RLS symptom groups (10 < IRLS ≤ 20) and between the non-RLS and severe RLS symptom groups (IRLS > 20). The RF model showed the highest performance in predicting the RLS symptom group and XGB model in the severe RLS symptom group. For the RLS symptom group, when using only wearable device data, the AUC, accuracy, precision, recall, and F1 scores were 0.78, 0.70, 0.66, 0.84, and 0.74, respectively, while these scores combining wearable device and application data were 0.86, 0.76, 0.68, 1.00, and 0.81, respectively. For the severe RLS symptom group, when using only wearable device data, XGB achieved AUC, accuracy, precision, recall, and F1 scores of 0.66, 0.84, 0.89, 0.93, and 0.91, respectively, while these scores combining wearable device and application data were 0.70, 0.80, 0.88, 0.90, and 0.89, respectively. Diverse digital phenotypes clinically associated with RLS were processed based on circadian rhythms to demonstrate the potential of digital phenotyping for RLS prediction. Thus, our study establishes early detection and personalized management of RLS. Trial Registration: Clinical Research Information Service (CRIS) KCT0009175 (Registration data: Feb-15-2024) ( https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=M&pageSize=10&page=undefined&seq=26133&status=5&seq_group=26133 ).

Pesticide poisoning remains a significant public health issue, characterized by high morbidity and mortality, particularly among patients presenting to the emergency department. This study aimed to develop a 14-day in-hospital mortality prediction model for patients with acute pesticide poisoning using early clinical and laboratory data. This retrospective cohort study included 1056 patients who visited Soonchunhyang University Cheonan Hospital between January 2015 and December 2020. The cohort was randomly divided into train (n = 739) and test (n = 317) sets using stratification by pesticide type and outcome. Candidate predictors were selected based on univariate Cox regression, LASSO regularization, random forest feature importance, and clinical relevance derived from established prognostic scoring systems. Logistic regression models were constructed using six distinct feature sets. The best-performing model combined LASSO-selected and clinically curated features (AUC 0.926 [0.890–0.957]), while the final model—selected for interpretability—used only LASSO-selected features (AUC 0.923 [0.884–0.955]; balanced accuracy 0.835; sensitivity 0.843; specificity 0.857; F1.5 score 0.714 at threshold 0.450). SHapley Additive exPlanations (SHAP) analysis identified paraquat ingestion, Glasgow Coma Scale, bicarbonate level, base excess, and alcohol history as major mortality predictors. The proposed model outperformed the APACHE II score (AUC 0.835 [0.781–0.888]) and may serve as a valuable tool for early risk stratification and clinical decision making in pesticide-poisoned patients.