Practice Management

Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

OBJECTIVE: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH).

QUATgo: Protein quaternary structural attributes predicted by two-stage machine learning approaches with heterogeneous feature encoding.

Many proteins exist in natures as oligomers with various quaternary structural attributes rather than as single chains. Predicting these attributes is an essential task in computational biology for the advancement of proteomics. However, the existing methods do not consider the integration of heterogeneous coding and the accuracy of subunit categories with limited data. To this end, we proposed a ...

Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification.

Effective management of chronic constrictive pulmonary conditions lies in proper and timely administration of medication. As a series of studies indic...

A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes.

OBJECTIVE: This study aims to develop and evaluate effective methods that can normalize diagnosis and procedure terms written by physicians to standar...

DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.

Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to pr...

Molecular and DNA Artificial Neural Networks via Fractional Coding.

This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work ha...

Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth.

Reconstructing a "forma mentis", a mindset, and its changes, means capturing how individuals perceive topics, trends and experiences over time. To thi...

Coding with transient trajectories in recurrent neural networks.

Following a stimulus, the neural response typically strongly varies in time and across neurons before settling to a steady-state. While classical popu...

Medi-Care AI: Predicting medications from billing codes via robust recurrent neural networks.

In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic cl...

Using artificial intelligence to analyse and teach communication in healthcare.

Communication is a core component of effective healthcare that impacts many patient and doctor outcomes, yet is complex and challenging to both analys...

Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.

One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processi...

Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes.

PURPOSE: Identification of patients for epidemiologic research through administrative coding has important limitations. We investigated the feasibilit...

Long Short-Term Memory Recurrent Neural Networks for Multiple Diseases Risk Prediction by Leveraging Longitudinal Medical Records.

Individuals suffer from chronic diseases without being identified in time, which brings lots of burden of disease to the society. This paper presents ...

Identifying fetal yawns based on temporal dynamics of mouth openings: A preterm neonate model using support vector machines (SVMs).

Fetal yawning is of interest because of its clinical, developmental and theoretical implications. However, the methodological challenges of identifyin...

Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity.

BACKGROUND AND OBJECTIVE: This work deals with clinical text mining, a field of Natural Language Processing applied to biomedical informatics. The aim...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are appl...

A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition.

Recent research on hand detection and gesture recognition has attracted increasing interest due to its broad range of potential applications, such as ...

Is coding a relevant metaphor for building AI?

Brette contends that the neural coding metaphor is an invalid basis for theories of what the brain does. Here, we argue that it is an insufficient gui...

Ranking of non-coding pathogenic variants and putative essential regions of the human genome.

A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding ge...

Complete mitochondrial genome of (Lepidoptera: Uraniidae).

belongs to the family Uraniidae in the superfamily Geometroidea (Lepidoptera). We sequenced 15,346-bp long complete mitochondrial genome (mitogenome)...