AIMC Topic: Classification

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Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might sol...
Humans Neural Networks, Computer Data Mining Clinical Trials as Topic Classification Logistic Models Natural Language Processing Patient Selection Machine Learning Deep Learning
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ML-Net: multi-label classification of biomedical texts with deep neural networks.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods,...
Benchmarking Data Mining Classification Computational Biology Natural Language Processing Neural Networks, Computer Machine Learning Deep Learning
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Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks.

Systematic biology Nov 1, 2019
Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has ...
Phylogeny Insecta Classification Neural Networks, Computer Reproducibility of Results Animals
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Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors.

Journal of medical entomology Sep 3, 2019
Vector-borne Chagas disease is endemic to the Americas and imposes significant economic and social burdens on public health. In a previous contribution, we presented an automated identification system that was able to discriminate among 12 Mexican an...
Insect Vectors Triatominae Brazil Deep Learning Mexico Classification Chagas Disease Animals
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Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits.

Systematic biology Sep 1, 2019
Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. Thes...
Phenotype Animals Models, Statistical Biological Evolution Biological Ontologies Classification Anatomy
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Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Accurately estimating performance accuracy of machine learning classifiers is of fundamental importance in biomedical research with potentially societal consequences upon the deployment of bestperforming tools in everyday life. Although classificatio...
Models, Statistical Humans Classification Machine Learning Computer Simulation Algorithms Computational Biology
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Expert-level sleep scoring with deep neural networks.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2018
OBJECTIVES: Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the com...
Humans Classification Models, Biological Neural Networks, Computer Sleep Polysomnography Machine Learning Datasets as Topic Electroencephalography Sleep Stages Sleep Apnea Syndromes
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Machine learning for psychiatric patient triaging: an investigation of cascading classifiers.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2018
OBJECTIVE: Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification ...
Classification Medical Records Triage Machine Learning Patient Acuity Algorithms Pattern Recognition, Automated Mental Disorders Decision Support Techniques Reproducibility of Results Humans
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SPRENO: a BioC module for identifying organism terms in figure captions.

Database : the journal of biological databases and curation Jan 1, 2018
Recent advances in biological research reveal that the majority of the experiments strive for comprehensive exploration of the biological system rather than targeting specific biological entities. The qualitative and quantitative findings of the inve...
Drosophila melanogaster Animals Genes, Viral Neural Networks, Computer Sindbis Virus Genes, Insect Databases, Factual Data Mining Classification
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Improving the explainability of Random Forest classifier - user centered approach.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2018
Machine Learning (ML) methods are now influencing major decisions about patient care, new medical methods, drug development and their use and importance are rapidly increasing in all areas. However, these ML methods are inherently complex and often d...
User-Computer Interface Humans Supervised Machine Learning Computational Biology Algorithms Models, Statistical Classification Databases, Factual
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