Practice Management

An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes.

BACKGROUND AND OBJECTIVE: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective, and it requires medical coders with extensive training. This study aims to evaluate the performance of deep-learning-based systems to automatically m...

Interpretable deep learning to map diagnostic texts to ICD-10 codes.

BACKGROUND: Automatic extraction of morbid disease or conditions contained in Death Certificates is a critical process, useful for billing, epidemiological studies and comparison across countries. The fact that these clinical documents are written in regular natural language makes the automatic coding process difficult because, often, spontaneous terms diverge strongly from standard reference term...

Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China.

INTRODUCTION: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for prima...

Gene pathogenicity prediction of Mendelian diseases via the random forest algorithm.

The study of Mendelian diseases and the identification of their causative genes are of great significance in the field of genetics. The evaluation of ...

A cross-lingual approach to automatic ICD-10 coding of death certificates by exploring machine translation.

Automatic ICD-10 coding is an unresolved challenge in terms of Machine Learning tasks. Despite hospitals generating an enormous amount of clinical doc...

Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants.

It has been found that each non-coding RNA (ncRNA) can act not only through its target gene, but also interact with each other to act on biological tr...

A novel conjoint triad auto covariance (CTAC) coding method for predicting protein-protein interaction based on amino acid sequence.

Protein-protein interactions (PPIs) play a crucial role in the life-sustaining activities of organisms. Although various methods for the prediction of...

Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.

Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understand...

Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.

Direct observation of psychotherapy and providing performance-based feedback is the gold-standard approach for training psychotherapists. At present, ...

Prediction of Long Non-Coding RNAs Based on Deep Learning.

With the rapid development of high-throughput sequencing technology, a large number of transcript sequences have been discovered, and how to identify ...

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research.

As early as infancy, caregivers' facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their int...

RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.

Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implic...

NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans.

State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with ...

Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity.

Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and lan...

[An online dynamic knowledge base in multiple languages on general medicine and primary care].

INTRODUCTION: The International Classification of Primary Care, Second version (ICPC-2) aligned with the 10th Revision of the International Classifica...

Impact of robotics and human enhancement on occupation: what does it mean for rehabilitation?

People with disabilities face participation challenges in all occupations. Scientific and technological advancements impact the occupational landscap...

Sparse coding of pathology slides compared to transfer learning with deep neural networks.

BACKGROUND: Histopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. ...

Computer-Assisted Diagnostic Coding: Effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings.

Computer-assisted (diagnostic) coding (CAC) aims to improve the operational productivity and accuracy of clinical coders. The level of accuracy, espec...

Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors.

Transcription factors (TFs) are the major components of human gene regulation. In particular, they bind onto specific DNA sequences and regulate neigh...

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk.

BACKGROUND: With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been c...