AIMC Topic: 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 Support Vector Machine Semantics Unified Medical Language System Medical Errors Natural Language Processing Risk Management Datasets as Topic
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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...
Safety Management Medical Errors Machine Learning Electronic Health Records Risk Management
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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.
Humans Classification Medical Errors Support Vector Machine Risk Management Feasibility Studies Neural Networks, Computer Patient Safety
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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 ...
Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Humans Radiography, Abdominal Medical Errors Radiography Radiography, Thoracic Enteral Nutrition
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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...
Medical Errors Humans Language Natural Language Processing Data Mining
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Avoiding common pitfalls in machine learning omic data science.

Nature materials May 1, 2019
Cell Biology Genetic Variation Oligonucleotide Array Sequence Analysis Medical Errors Proteomics Models, Theoretical Machine Learning Algorithms Phenotype Data Science Genomics Computational Biology Humans
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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...
Diagnosis, Computer-Assisted Deep Learning Electrocardiography Signal Processing, Computer-Assisted Skin Neural Networks, Computer Machine Learning Algorithms Humans Medical Informatics Cardiology Models, Cardiovascular Medical Errors
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Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery.

The international journal of medical robotics + computer assisted surgery : MRCAS Feb 1, 2019
BACKGROUND: This paper aims to move the debate forward regarding the potential for artificial intelligence (AI) and autonomous robotic surgery with a particular focus on ethics, regulation and legal aspects (such as civil law, international law, tort...
Humans Artificial Intelligence Medical Errors Ethics, Medical Robotic Surgical Procedures Algorithms Computer Security United States Europe
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Using Machine Learning-Based Multianalyte Delta Checks to Detect Wrong Blood in Tube Errors.

American journal of clinical pathology Oct 24, 2018
OBJECTIVES: An unfortunate reality of laboratory medicine is that blood specimens collected from one patient occasionally get mislabeled with identifiers from a different patient, resulting in so-called "wrong blood in tube" (WBIT) errors and potenti...
Medical Errors Patient Safety Blood Specimen Collection Humans Machine Learning Algorithms
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Structuring Clinical Decision Support Rules for Drug Safety Using Natural Language Processing.

Studies in health technology and informatics Jan 1, 2018
Drug safety is an important aspect in healthcare, resulting in a number of inadvertent events, which may harm the patients. IT based Clinical Decision Support (CDS), integrated in electronic-prescription or Electronic Health Records (EHR) systems, ca...
Semantics Medical Errors Electronic Health Records Decision Support Systems, Clinical Humans Natural Language Processing Software
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