AIMC Topic: Classification

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Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Systematic biology May 27, 2024
Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computation...
Phylogeny Deep Learning Likelihood Functions Viruses Phylogeography Bayes Theorem Classification
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Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data.

Genome biology and evolution May 2, 2024
The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and an...
High-Throughput Nucleotide Sequencing Classification Computational Biology Databases, Genetic Machine Learning
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The synergism of spatial metabolomics and morphometry improves machine learning-based renal tumour subtype classification.

Clinical and translational medicine Feb 1, 2022
Kidney Neoplasms Classification Machine Learning Metabolomics Humans
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H-Accuracy, an Alternative Metric to Assess Classification Models in Medicine.

Studies in health technology and informatics Jun 16, 2020
As widely known, regular accuracy is a misleading and shallow indicator of the performance of a predictive model, especially in real-life domains like medicine, where decisions affect health or life. In this paper we present and discuss a new accurac...
Classification Medicine Data Accuracy Humans Artificial Intelligence
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Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2020
OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We dev...
Humans Observational Studies as Topic Supervised Machine Learning Medical Informatics Electronic Health Records Data Science Classification
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Optimal Feature Selection for Big Data Classification: Firefly with Lion-Assisted Model.

Big data Apr 1, 2020
In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature...
Big Data Classification Neural Networks, Computer Algorithms
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Accurate Inference of Tree Topologies from Multiple Sequence Alignments Using Deep Learning.

Systematic biology Mar 1, 2020
Reconstructing the phylogenetic relationships between species is one of the most formidable tasks in evolutionary biology. Multiple methods exist to reconstruct phylogenetic trees, each with their own strengths and weaknesses. Both simulation and emp...
Classification Sequence Alignment Neural Networks, Computer Phylogeny Deep Learning Computer Simulation
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Imputation and characterization of uncoded self-harm in major mental illness using machine learning.

Journal of the American Medical Informatics Association : JAMIA Jan 1, 2020
OBJECTIVE: We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm incidence, and identify factors associated with coding bias.
Female Adult Male Suicidal Ideation Humans Clinical Coding Mental Disorders Electronic Health Records Machine Learning Classification Algorithms Self-Injurious Behavior Incidence Datasets as Topic
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Using convolutional neural networks to identify patient safety incident reports by type and severity.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2019
OBJECTIVE: To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports.
Patient Safety Classification Humans Risk Management Support Vector Machine Neural Networks, Computer Feasibility Studies Medical Errors
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A computational study of mental health awareness campaigns on social media.

Translational behavioral medicine Nov 25, 2019
As public discourse continues to progress online, it is important for mental health advocates, public health officials, and other curious parties and stakeholders, ranging from researchers, to those affected by the issue, to be aware of the advancing...
Delphi Technique Humans Social Media Mental Health Health Promotion Classification Machine Learning Public Health
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