Practice Management

Patient Clustering for Vital Organ Failure Using ICD Code With Graph Attention.

OBJECTIVE: Heart failure, respiratory failure and kidney failure are three severe organ failures (OF) that have high mortalities and are most prevalent in intensive care units. The objective of this work is to offer insights into OF clustering from the aspects of graph neural networks and diagnosis history.

The ethics of advancing artificial intelligence in healthcare: analyzing ethical considerations for Japan's innovative AI hospital system.

Public and private investments into developing digital health technologies-including artificial intelligence (AI)-are intensifying globally. Japan is a key case study given major governmental investments, in part through a Cross-Ministerial Strategic Innovation Promotion Program (SIP) for an "Innovative AI Hospital System." Yet, there has been little critical examination of the SIP Research Plan, ...

Artificial intelligence in the neonatal intensive care unit: the time is now.

Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimen...

Tree-Structured Data Clustering-Driven Neural Network for Intra Prediction in Video Coding.

Intra prediction is a crucial part of video compression, which utilizes local information in images to eliminate spatial redundancy. As the state-of-t...

PTPRC promoted CD8+ T cell mediated tumor immunity and drug sensitivity in breast cancer: based on pan-cancer analysis and artificial intelligence modeling of immunogenic cell death-based drug sensitivity stratification.

BACKGROUND: Immunogenic cell death (ICD) is a result of immune cell infiltration (ICI)-mediated cell death, which is also a novel acknowledgment to re...

A Deep Neural Network-Based Co-Coding Method to Predict Drug-Protein Interactions by Analyzing the Feature Consistency Between Drugs and Proteins.

Exploring drug-protein interactions (DPIs) through computational methods can effectively reduce the workload and the cost of DPI identification. Previ...

Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.

The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learn...

Mitochondrial genome analysis, phylogeny and divergence time evaluation of (Aves, Strigiformes, Strigidae).

Background Prior research has shown that the European peninsulas were the main sources of colonisation of Northern Europe during the late glacial per...

Correlation analysis of deep learning methods in S-ICD screening.

BACKGROUND: Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varyi...

Evidence of a predictive coding hierarchy in the human brain listening to speech.

Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, tr...

ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction.

BACKGROUND: Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent ...

Let the algorithm speak: How to use neural networks for automatic item generation in psychological scale development.

Measurement is at the heart of scientific research. As many-perhaps most-psychological constructs cannot be directly observed, there is a steady deman...

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA-IncRNA Based on Artificial Gorilla Troops Algorithm.

MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream ...

Defining the distance between diseases using SNOMED CT embeddings.

Characterizing disease relationships is essential to biomedical research to understand disease etiology and improve clinical decision-making. Measurem...

Evaluation of AIML + HDR-A Course to Enhance Data Science Workforce Capacity for Hispanic Biomedical Researchers.

Artificial intelligence (AI) and machine learning (ML) facilitate the creation of revolutionary medical techniques. Unfortunately, biases in current A...

Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective.

The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offere...

CARES: A Corpus for classification of Spanish Radiological reports.

This paper presents a new corpus of radiology medical reports written in Spanish and labeled with ICD-10. CARES (Corpus of Anonymised Radiological Evi...

Use of neural network based on international classification ICD-10 in patients with head and neck injuries in Lublin Province, Poland, between 2006-2018, as a predictive value of the outcomes of injury sustained.

INTRODUCTION AND OBJECTIVE: Head and neck injuries are a heterogeneous group in terms of both clinical course and prognosis. For years, there have bee...

Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine t...

Efficient neural codes naturally emerge through gradient descent learning.

Human sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more fre...