Prognostic prediction of sepsis patient using transformer with skip connected token for tabular data.

Sepsis is known as a common syndrome in intensive care units (ICU), and severe sepsis and septic shock are among the leading causes of death worldwide. The purpose of this study is to develop a deep learning model that supports clinicians in efficiently managing sepsis patients in the ICU by predicting mortality, ICU length of stay (>14 days), and hospital length of stay (>30 days). The proposed model was developed using 591 retrospective data with 16 tabular data related to a sequential organ failure assessment (SOFA) score. To analyze tabular data, we designed the modified architecture of the transformer that has achieved extraordinary success in the field of languages and computer vision tasks in recent years. The main idea of the proposed model is to use a skip-connected token, which combines both local (feature-wise token) and global (classification token) information as the output of a transformer encoder. The proposed model was compared with four machine learning models (ElasticNet, Extreme Gradient Boosting [XGBoost]), and Random Forest) and three deep learning models (Multi-Layer Perceptron [MLP], transformer, and Feature-Tokenizer transformer [FT-Transformer]) and achieved the best performance (mortality, area under the receiver operating characteristic (AUROC) 0.8047; ICU length of stay, AUROC 0.8314; hospital length of stay, AUROC 0.7342). We anticipate that the proposed model architecture will provide a promising approach to predict the various clinical endpoints using tabular data such as electronic health and medical records.

Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation.

Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter ablation (RFCA) should be decided after fully considering its prognosis. However, a robust prediction model reflecting the complex interactions between the features affecting prognosis remains to be developed. In this paper, we propose a deep learning model for predicting the late recurrence after RFCA in patients with AF. Aiming to predict the late recurrence (LR) of AF within 1 year after pulmonary vein isolation, we designed a multimodal model based on the multilayer perceptron architecture. For quantitative evaluation, we conducted 4-fold cross-validation on data from 177 AF patients including 47 LR patients. The proposed model (area under the receiver operating characteristic curve-AUROC, 0.766) outperformed the acute patient physiologic and laboratory evaluation (APPLE) score (AUROC, 0.605), CHA2DS2-VASc score (AUROC, 0.595), linear regression (AUROC, 0.541), logistic regression (AUROC, 0.546), extreme gradient boosting (AUROC, 0.608), and support vector machine (AUROC, 0.638). The proposed model exhibited better performance than clinical indicators (APPLE and CHA2DS2-VASc score) and machine learning techniques (linear regression, logistic regression, extreme gradient boosting, and support vector machine). The model will support clinical decision-making for selecting good responders to the RFCA intervention.

Conformational changes of Aß (1-42) monomers in different solvents.

Amyloid proteins are known to be the main cause of numerous degenerative and neurodegenerative diseases. In general, amyloids are misfolded from monomers and they tend to have ß-strand formations. These misfolded monomers are then transformed into oligomers, fibrils, and plaques. It is important to understand the forming mechanism of amyloids in order to prevent degenerative diseases to occur. Aß protein is a highly noticeable protein which causes Alzheimer's disease. It is reported that solvents affect the forming mechanism of Aß amyloids. In this research, Aß1-42 was analyzed using an all-atom MD simulation with the consideration of effects induced by two disparate solvents: water and DMSO. As a result, two different conformation changes of Aß1-42 were exhibited in each solvent. It was found that salt-bridge of Asp23 and Lys28 in Aß1-42 was the key for amyloid folding based on the various analysis including hydrogen bond, electrostatic interaction energy and salt-bridge distance. Since this salt-bridge region plays a crucial role in initiating the misfolding of Aß1-42, this research may shed a light for studies related in amyloid folding and misfolding.