Practice Management

Latest AI and machine learning research in practice management for healthcare professionals.

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Examining the Coding Strength of Object Identity and Nonidentity Features in Human Occipito-Temporal Cortex and Convolutional Neural Networks.

A visual object is characterized by multiple visual features, including its identity, position and s...

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs...

What Is in a Plan? Using Natural Language Processing to Read 461 California City General Plans.

Land-use control is local and highly varied. State agencies struggle to assess plan contents. Simila...

Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning.

Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been sh...

Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach.

Pathology reports represent a primary source of information for cancer registries. Hospitals routine...

GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification.

Exponential growth of biomedical literature and clinical data demands more robust yet precise comput...

Controllable stroke-based sketch synthesis from a self-organized latent space.

Learning to synthesize free-hand sketches controllably according to specified categories and sketchi...

Sparse deep predictive coding captures contour integration capabilities of the early visual system.

Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurren...

Nonhuman rationality: a predictive coding perspective.

How can we rethink 'rationality' in the wake of animal and artificial intelligence studies? Can nonh...

A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling.

BACKGROUND: Secondary use of Electronic Health Records (EHRs) has mostly focused on health condition...

Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition.

In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work b...

NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image.

Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis...

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records.

The International Statistical Classification of Disease and Related Health Problems (ICD) is an inte...

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.

Understanding the genetic regulatory code governing gene expression is an important challenge in mol...

Pre-training phenotyping classifiers.

Recent transformer-based pre-trained language models have become a de facto standard for many text c...

Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network.

This work is aimed to study experimental and theoretical approaches for searching effective local tr...

The Relationship between Sparseness and Energy Consumption of Neural Networks.

About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes ...

Impact of chronic intermittent hypoxia on the long non-coding RNA and mRNA expression profiles in myocardial infarction.

Chronic intermittent hypoxia (CIH) is the primary feature of obstructive sleep apnoea (OSA), a cruci...

Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.

We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferativ...

Deep learning predicts short non-coding RNA functions from only raw sequence data.

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many bi...

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