Practice Management

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

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Toward an Improvement of the Analysis of Neural Coding.

Machine learning and artificial intelligence have strong roots on principles of neural computation. ...

Improved ontology for eukaryotic single-exon coding sequences in biological databases.

Efficient extraction of knowledge from biological data requires the development of structured vocabu...

PlaNC-TE: a comprehensive knowledgebase of non-coding RNAs and transposable elements in plants.

Transposable elements (TEs) play an essential role in the genetic variability of eukaryotic species....

Natural language processing of clinical notes for identification of critical limb ischemia.

BACKGROUND: Critical limb ischemia (CLI) is a complication of advanced peripheral artery disease (PA...

STDP-based spiking deep convolutional neural networks for object recognition.

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neu...

Machine Learning for Precision Psychiatry: Opportunities and Challenges.

The nature of mental illness remains a conundrum. Traditional disease categories are increasingly su...

A deep learning method for lincRNA detection using auto-encoder algorithm.

BACKGROUND: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven metho...

Classification of hospital admissions into emergency and elective care: a machine learning approach.

Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healt...

Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

When encoding visual targets using various lagged versions of a pseudorandom binary sequence of lumi...

Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatiotemporal Scales RNN Model.

This letter proposes a novel predictive coding type neural network model, the predictive multiple sp...

A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts.

BACKGROUND: In recent years, a rapidly increasing number of RNA transcripts has been generated by th...

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, chan...

ECG data compression using a neural network model based on multi-objective optimization.

Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular di...

Artificial neural network coding of the child attachment interview using linguistic data.

Assessing attachment in adolescents is important due to relations between insecurity and psychopatho...

Machine Learning Approaches on Diagnostic Term Encoding With the ICD for Clinical Documentation.

This work focuses on data mining applied to the clinical documentation domain. Diagnostic terms (DTs...

Graph construction using adaptive Local Hybrid Coding scheme.

It is well known that dense coding with local bases (via Least Square coding schemes) can lead to la...

Artificial Intelligence, DNA Mimicry, and Human Health.

The molecular evolution of genomic DNA across diverse plant and animal phyla involved dynamic regist...

A scheme based on ICD-10 diagnoses and drug prescriptions to stage chronic kidney disease severity in healthcare administrative records.

BACKGROUND: Information about renal function is important for drug safety studies using administrati...

Combining sparse coding and time-domain features for heart sound classification.

OBJECTIVE: This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which...

Recurrent networks with soft-thresholding nonlinearities for lightweight coding.

A long-standing and influential hypothesis in neural information processing is that early sensory ne...

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants.

Disease and trait-associated variants represent a tiny minority of all known genetic variation, and ...

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