AIMC Topic: Algorithms

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Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to iden...
Hospitalization Prospective Studies Aged Electronic Health Records Male Delirium Aged, 80 and over Female ROC Curve Middle Aged Models, Theoretical Risk Assessment Workflow Humans Machine Learning Algorithms
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Predicting complications of diabetes mellitus using advanced machine learning algorithms.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of complications could help with more targeted measures that would prevent or slow down their development.
Diabetes Complications Humans Deep Learning Algorithms Neural Networks, Computer Diabetes Mellitus, Type 2 Prognosis Decision Trees Risk Assessment
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Using Machine Learning to Predict Primary Care and Advance Workforce Research.

Annals of family medicine Jul 1, 2020
PURPOSE: To develop and test a machine-learning-based model to predict primary care and other specialties using Medicare claims data.
Primary Health Care United States Area Under Curve Medicare Workforce Insurance Claim Review Physicians, Primary Care Humans Algorithms Machine Learning Cross-Sectional Studies ROC Curve
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Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.

GigaScience Jul 1, 2020
BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality ...
High-Throughput Nucleotide Sequencing CpG Islands Gene Expression Regulation Reproducibility of Results Epigenesis, Genetic Neoplasms Machine Learning Epigenomics Computational Biology Algorithms DNA Methylation Prognosis Humans Neural Networks, Computer
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Artificial Intelligence Needs Clinical Intelligence to Succeed.

JACC. Heart failure Jul 1, 2020
Algorithms Intelligence Heart Failure Humans Artificial Intelligence Machine Learning
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Similar Disease Prediction With Heterogeneous Disease Information Networks.

IEEE transactions on nanobioscience Jul 1, 2020
Studying the similarity of diseases can help us to explore the pathological characteristics of complex diseases, and help provide reliable reference information for inferring the relationship between new diseases and known diseases, so as to develop ...
Semantics Disease Databases, Factual Computational Biology Models, Statistical Humans Machine Learning Algorithms
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Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients.

The bone & joint journal Jul 1, 2020
AIMS: Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clini...
Humans Registries Data Accuracy Arthroplasty, Replacement, Knee Algorithms Retrospective Studies Natural Language Processing Quality of Health Care Information Storage and Retrieval Arthroplasty, Replacement, Hip
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Machine learning in haematological malignancies.

The Lancet. Haematology Jul 1, 2020
Machine learning is a branch of computer science and statistics that generates predictive or descriptive models by learning from training data rather than by being rigidly programmed. It has attracted substantial attention for its many applications i...
Algorithms Hematologic Neoplasms Humans Neural Networks, Computer Machine Learning
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Application of deep learning and image processing analysis of photographs for amblyopia screening.

Indian journal of ophthalmology Jul 1, 2020
PURPOSE: Photo screeners and autorefractors have been used to screen children for amblyopia risk factors (ARF) but are limited by cost and efficacy. We looked for a deep learning and image processing analysis-based system to screen for ARF.
Amblyopia Child Image Processing, Computer-Assisted Mass Screening Humans Algorithms Young Adult Deep Learning
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Artificial intelligence in chemistry and drug design.

Journal of computer-aided molecular design Jul 1, 2020
Humans Artificial Intelligence Drug Discovery Big Data Databases, Chemical Machine Learning Algorithms Drug Design
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