Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

Background And Objectives: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combining chest X-ray (CXR) and electronic health record (EHR) data to screen patients with abnormal N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in emergency departments.

Methods: Using the open-source dataset MIMIC-IV and MIMICCXR, the study population consisted of 1,432 patients and 1,833 pairs of CXRs and EHRs.

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan.

We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves.

Few-shot transfer learning for personalized atrial fibrillation detection using patient-based siamese network with single-lead ECG records.

The proliferation of wearable devices has allowed the collection of electrocardiogram (ECG) recordings daily to monitor heart rhythm and rate. For example, 24-hour Holter monitors, cardiac patches, and smartwatches are widely used for ECG gathering and application. An automatic atrial fibrillation (AF) detector is required for timely ECG interpretation.

Learned Practical Guidelines for Evaluating Conditional Entropy and Mutual Information in Discovering Major Factors of Response-vs.-Covariate Dynamics.

We reformulate and reframe a series of increasingly complex parametric statistical topics into a framework of response-vs.-covariate (Re-Co) dynamics that is described without any explicit functional structures. Then we resolve these topics' data analysis tasks by discovering major factors underlying such Re-Co dynamics by only making use of data's categorical nature.

Unraveling Hidden Major Factors by Breaking Heterogeneity into Homogeneous Parts within Many-System Problems.

For a large ensemble of complex systems, a Many-System Problem (MSP) studies how heterogeneity constrains and hides structural mechanisms, and how to uncover and reveal hidden major factors from homogeneous parts. All member systems in an MSP share common governing principles of dynamics, but differ in idiosyncratic characteristics. A typical dynamic is found underlying response features with respect to covariate features of quantitative or qualitative data types.

Gender-Specific Impact of Metabolic Obesity Phenotypes on the Risk of Hashimoto's Thyroiditis: A Retrospective Data Analysis Using a Health Check-Up Database.

Background: Hashimoto's thyroiditis (HT) is recognized as the most common autoimmune thyroid disease, often accompanied by the diffuse enlargement of thyroid with abundant blood flow and elevated level of thyroid autoantibodies. As obesity had a positive association with the risk of HT. Thus, this retrospective study was established to further explore the gender relationship between metabolic obesity phenotypes and the risk of Hashimoto's thyroiditis (HT).

Categorical Nature of Major Factor Selection via Information Theoretic Measurements.

Without assuming any functional or distributional structure, we select collections of major factors embedded within response-versus-covariate (Re-Co) dynamics via selection criteria [C1: confirmable] and [C2: irrepaceable], which are based on information theoretic measurements. The two criteria are constructed based on the computing paradigm called Categorical Exploratory Data Analysis (CEDA) and linked to Wiener-Granger causality. All the information theoretical measurements, including conditional mutual information and entropy, are evaluated through the contingency table platform, which primarily rests on the categorical nature within all involved features of any data types: quantitative or qualitative.

Mimicking Complexity of Structured Data Matrix's Information Content: Categorical Exploratory Data Analysis.

We develop Categorical Exploratory Data Analysis (CEDA) with mimicking to explore and exhibit the complexity of information content that is contained within any data matrix: categorical, discrete, or continuous. Such complexity is shown through visible and explainable serial multiscale structural dependency with heterogeneity. CEDA is developed upon all features' categorical nature via histogram and it is guided by all features' associative patterns (order-2 dependence) in a mutual conditional entropy matrix.

Radiomic analysis of magnetic resonance imaging predicts brain metastases velocity and clinical outcome after upfront radiosurgery.

Background: Brain metastasis velocity (BMV) predicts outcomes after initial distant brain failure (DBF) following upfront stereotactic radiosurgery (SRS). We developed an integrated model of clinical predictors and pre-SRS MRI-derived radiomic scores (R-scores) to identify high-BMV (BMV-H) patients upon initial identification of brain metastases (BMs).

Methods: In total, 256 patients with BMs treated with upfront SRS alone were retrospectively included.

Use of a cytogenetic whole-genome comparison to resolve phylogenetic relationships among three species: implications for mammalian systematics and conservation biology.

The objective was to apply a novel modification of a genome-wide, comparative cytogenetic technique (comparative genomic hybridization, comparative genomic hybridization (CGH)), to study species belonging to the myrmecophagous (ant/termite eating) mammalian orders/superorders (Pholidota, Tubulidentata, Carnivora, and Xenarthra), as a model for other applications in mammalian systematics and conservation biology. In this study, CGH was applied to high-quality metaphase spreads of pangolin (Pholidota), using probes of sloth and canine (Xenarthra and Carnivora, respectively) genomic DNA labeled with different fluorophores, thereby facilitating analysis of the visible color spectrum on pangolin karyotypes. Our results posited that pholidotes are closer to carnivores than to xenarthrans, which confirmed the current consensus that myrmecophagy in these mammalian lineages was more likely because of homoplasy (convergent evolution) than being an ancestral character.

Spike count reliability and the Poisson hypothesis.

The variability of cortical activity in response to repeated presentations of a stimulus has been an area of controversy in the ongoing debate regarding the evidence for fine temporal structure in nervous system activity. We present a new statistical technique for assessing the significance of observed variability in the neural spike counts with respect to a minimal Poisson hypothesis, which avoids the conventional but troubling assumption that the spiking process is identically distributed across trials. We apply the method to recordings of inferotemporal cortical neurons of primates presented with complex visual stimuli.