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Medical Errors

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Can Unified Medical Language System-based semantic representation improve automated identification of patient safety incident reports by type and severity?

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2020
OBJECTIVE: The study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity.
Humans Patient Safety Datasets as Topic Semantics Unified Medical Language System Medical Errors Support Vector Machine Natural Language Processing Risk Management
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Detecting Severe Incidents from Electronic Medical Records Using Machine Learning Methods.

Studies in health technology and informatics Jun 16, 2020
The goal of this research was to design a solution to detect non-reported incidents, especially severe incidents. To achieve this goal, we proposed a method to process electronic medical records and automatically extract clinical notes describing sev...
Medical Errors Electronic Health Records Risk Management Safety Management Machine Learning
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Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study.

PloS one Jan 1, 2020
Wrong-site surgeries can occur due to the absence of an appropriate surgical time-out. However, during a time-out, surgical participants are unable to review the patient's charts due to their aseptic hands. To improve the conditions in surgical time-...
Speech Pilot Projects Cataract Extraction Cell Phone Cataract Humans Medical Errors Electronic Health Records Deep Learning
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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.
Neural Networks, Computer Patient Safety Support Vector Machine Risk Management Humans Feasibility Studies Classification Medical Errors
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Development and Validation of a Bayesian Network Method to Detect External Beam Radiation Therapy Physician Order Errors.

International journal of radiation oncology, biology, physics Oct 1, 2019
PURPOSE: To investigate a Bayesian network (BN)-based method to detect errors in external beam radiation therapy physician orders.
Radiotherapy Planning, Computer-Assisted Datasets as Topic False Negative Reactions Humans False Positive Reactions Medical Errors ROC Curve Radiotherapy Dosage Radiotherapy Setup Errors Algorithms Neural Networks, Computer Bayes Theorem Organ Specificity Technology, Radiologic Neoplasms Radiologists Cohort Studies Dose Fractionation, Radiation Radiotherapy, Image-Guided Simulation Training
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Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer.

International journal of medical informatics Aug 1, 2019
BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to ...
Algorithms Electronic Health Records Natural Language Processing Humans Adverse Drug Reaction Reporting Systems Machine Learning Medical Errors Singapore Patient Discharge Drug-Related Side Effects and Adverse Reactions
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Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Journal of digital imaging Aug 1, 2019
Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of ...
Radiography Radiography, Thoracic Enteral Nutrition Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Radiography, Abdominal Medical Errors Humans
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Named Entity Recognition and Classification for Medical Prospectuses.

Studies in health technology and informatics Jul 4, 2019
Structuring and processing natural language is a growing challenge in the medical field. Researchers are looking for new ways to extract knowledge to create databases and applications to help doctors treat patients and minimize medical errors. A very...
Humans Language Natural Language Processing Data Mining Medical Errors
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Avoiding common pitfalls in machine learning omic data science.

Nature materials May 1, 2019
Humans Oligonucleotide Array Sequence Analysis Proteomics Machine Learning Medical Errors Models, Theoretical Algorithms Cell Biology Genetic Variation Genomics Phenotype Computational Biology Data Science
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A new deep learning model for assisted diagnosis on electrocardiogram.

Mathematical biosciences and engineering : MBE Mar 22, 2019
In order to enhance the accuracy of computer aided electrocardiogram analysis, we propose a deep learning model called CBRNN to assist diagnosis on electrocardiogram for clinical medical service. It combines two sub networks which are convolutional n...
Medical Errors Electrocardiography Algorithms Medical Informatics Signal Processing, Computer-Assisted Diagnosis, Computer-Assisted Humans Skin Models, Cardiovascular Deep Learning Neural Networks, Computer Cardiology Machine Learning
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