Feature selection integrating Shapley values and mutual information in reinforcement learning: An application in the prediction of post-operative outcomes in patients with end-stage renal disease.

Background: In predicting post-operative outcomes for patients with end-stage renal disease, our study faced challenges related to class imbalance and a high-dimensional feature space. Therefore, with a focus on overcoming class imbalance and improving interpretability, we propose a novel feature selection approach using multi-agent reinforcement learning.

Methods: We proposed a multi-agent feature selection model based on a comprehensive reward function that combines classification model performance, Shapley additive explanations values, and the mutual information.

Impartial feature selection using multi-agent reinforcement learning for adverse glycemic event prediction.

We developed an attention model to predict future adverse glycemic events 30 min in advance based on the observation of past glycemic values over a 35 min period. The proposed model effectively encodes insulin administration and meal intake time using Time2Vec (T2V) for glucose prediction. The proposed impartial feature selection algorithm is designed to distribute rewards proportionally according to agent contributions.

WBC YOLO-ViT: 2 Way - 2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer.

Accurate detection and classification of white blood cells, otherwise known as leukocytes, play a critical role in diagnosing and monitoring various illnesses. However, conventional methods, such as manual classification by trained professionals, must be revised in terms of accuracy, efficiency, and potential bias. Moreover, applying deep learning techniques to detect and classify white blood cells using microscopic images is challenging owing to limited data, resolution noise, irregular shapes, and varying colors from different sources.

Glucose Transformer: Forecasting Glucose Level and Events of Hyperglycemia and Hypoglycemia.

To avoid the adverse consequences from abrupt increases in blood glucose, diabetic inpatients should be closely monitored. Using blood glucose data from type 2 diabetes patients, we propose a deep learning model-based framework to forecast blood glucose levels. We used continuous glucose monitoring (CGM) data collected from inpatients with type 2 diabetes for a week.

Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning.

Arterial hypotension is associated with incidence of postoperative complications, such as myocardial infarction or acute kidney injury. Little research has been conducted for the real-time prediction of hypotension, even though many studies have been performed to investigate the factors which affect hypotension events. This forecasting problem is quite challenging compared to diagnosis that detects high-risk patients at current.

Transiently Observed Trace Albuminuria on Urine Dipstick Test Is Associated With All-Cause Death, Cardiovascular Death, and Incident Chronic Kidney Disease: A National Health Insurance Service-National Sample Cohort in Korea.

Introduction: Albuminuria is a well-known risk factor for end-stage kidney disease, all-cause mortality, and cardiovascular mortality, even when the albumin-to-creatinine ratio is <30 mg/g. However, the association between transiently observed trace albuminuria and these major adverse outcomes has not yet been reported. This study aimed to examine the effect of transient albuminuria on these major adverse outcomes using the National Health Insurance Service data in Korea.

Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum.

Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence of CQ-resistant and artemisinin resistance parasites is now present in most areas where malaria is endemic. In this work, we developed five machine learning models to predict antimalarial bioactivities of a drug against plasmodium falciparum from the features (i.

Prediction of Postoperative Complications for Patients of End Stage Renal Disease.

End stage renal disease (ESRD) is the last stage of chronic kidney disease that requires dialysis or a kidney transplant to survive. Many studies reported a higher risk of mortality in ESRD patients compared with patients without ESRD. In this paper, we develop a model to predict postoperative complications, major cardiac event, for patients who underwent any type of surgery.

Developing an Individual Glucose Prediction Model Using Recurrent Neural Network.

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction.

Malware Detection of Hangul Word Processor Files Using Spatial Pyramid Average Pooling.

Malware detection of non-executables has recently been drawing much attention because ordinary users are vulnerable to such malware. Hangul Word Processor (HWP) is software for editing non-executable text files and is widely used in South Korea. New malware for HWP files continues to appear because of the circumstances between South Korea and North Korea.

Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension.

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation.

Development of a prediction model for hypotension after induction of anesthesia using machine learning.

Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension.

The Whole-Genome and Transcriptome of the Manila Clam (Ruditapes philippinarum).

The manila clam, Ruditapes philippinarum, is an important bivalve species in worldwide aquaculture including Korea. The aquaculture production of R. philippinarum is under threat from diverse environmental factors including viruses, microorganisms, parasites, and water conditions with subsequently declining production.

Complete mitochondrial genome of the Pacific cod, (Gadiformes, Gadidae).

The Pacific cod is a commercially important species belonging to the family Gadidae. In this study, we performed the first sequencing and assembly of the complete mitochondrial genome of . The complete mitochondrial genome is 16,567 bp long, consisting of 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and a control region.

The complete mitochondrial genome of (Perciformes, gobiidae).

is a small intertidal species belonging to the family Gobiidae. Herein, we report the first sequencing and assembly of the complete mitochondrial genome of . The complete mitochondrial genome is 16,529 bp long and has the typical vertebrate mitochondrial gene arrangement, consisting of 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and a control region.

Detecting and classifying method based on similarity matching of Android malware behavior with profile.

Mass-market mobile security threats have increased recently due to the growth of mobile technologies and the popularity of mobile devices. Accordingly, techniques have been introduced for identifying, classifying, and defending against mobile threats utilizing static, dynamic, on-device, and off-device techniques. Static techniques are easy to evade, while dynamic techniques are expensive.

Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog.

As social media has become more prevalent, its influence on business, politics, and society has become significant. Due to easy access and interaction between large numbers of users, information diffuses in an epidemic style on the web. Understanding the mechanisms of information diffusion through these new publication methods is important for political and marketing purposes.

Reliability of the percent density in digital mammography with a semi-automated thresholding method.

Purpose: The reliability of the quantitative measurement of breast density with a semi-automated thresholding method (Cumulus™) has mainly been investigated with film mammograms. This study aimed to evaluate the intrarater reproducibility of percent density (PD) by Cumulus™ with digital mammograms.

Methods: This study included 1,496 craniocaudal digital mammograms from the unaffected breast of breast cancer patients.

Chitosan-hydroxycinnamic acid conjugates: preparation, antioxidant and antimicrobial activity.

In this study, the antioxidant and antimicrobial activities of chitosan-caffeic acid, chitosan-ferulic acid, and chitosan-sinapic acid conjugates with different grafting ratios were investigated. The synthesized chitosan-hydroxycinnamic acid conjugates were verified by performing (1)H NMR and differential scanning calorimetry analysis. The antioxidant activities of the conjugates were increased compared to the unmodified chitosan, by 1.

Genetic association of the gene encoding RPGRIP1L with susceptibility to vascular dementia.

A previous genome-wide association study (GWAS) failed to discover any nucleotide sequence variant associated with susceptibility to vascular dementia (VaD) and remained a problem of false negatives produced by a low statistical power. The current study was conducted to identify such potential false negatives and to provide comprehensive evidence for the most plausible predisposing genetic factor using large-scale Korean cohorts. We identified the gene encoding retinitis pigmentosa GTPase regulator-interacting protein 1-like (RPGRIP1L) with multiple nucleotide variants associated with susceptibility to VaD by a modest significant threshold (P<10(-4)).

Heterogeneous genetic associations of nucleotide sequence variants with bone mineral density by gender.

We examined genetic associations of previously identified sequence variants with bone mineral density and their heterogeneity by gender. Large-scale cohort data were used including a total of 8,419 subjects (4,034 males and 4,385 females) from the Korean Association REsource (KARE) cohort. Bone speed of sound (SOS) values were measured at distal radius or mid-shaft tibia by quantitative ultrasound.

Heterogeneity of genetic associations of CDKAL1 and HHEX with susceptibility of type 2 diabetes mellitus by gender.

We examined the genetic associations of previously identified sequence variants with type 2 diabetes mellitus (T2DM) and its potentially genetic heterogeneity by gender in a large-scale cohort. A total of 613 T2DM patients and 8221 control subjects from the Korea Association REsource (KARE) cohort were included in the analysis of genetic association of T2DM with 33 nucleotide polymorphic markers identified by previous studies. The association analysis was further conducted with data partitioned by gender.

The histone deacetylase inhibitor trichostatin a has genotoxic effects in human lymphoblasts in vitro.

Histone deacetylase inhibitors (HDACi) are a class of putative chemotherapeutic agents for which the mechanism of toxicity has not been fully identified. To explore the possibility that HDACi are genotoxic, human TK6 lymphoblastoid cells were exposed to trichostatin A (TSA) and genetic damage was measured. TSA caused a dose-dependent increase of G1-arrested cells at 24 h that correlated with increasing levels of p21 and apoptosis.