AIMC Topic: Acute Disease

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Prehospital triage of acute aortic syndrome using a machine learning algorithm.

The British journal of surgery Feb 11, 2020
BACKGROUND: Acute aortic syndrome (AAS) comprises a complex and potentially fatal group of conditions requiring emergency specialist management. The aim of this study was to build a prediction algorithm to assist prehospital triage of AAS.
Female Reproducibility of Results Aged Male Machine Learning Algorithms Prospective Studies Prognosis Triage Acute Disease Humans Syndrome Aortic Diseases Clinical Decision Rules Middle Aged Hospital Mortality Logistic Models Emergency Medical Services
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Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière's disease.

Acta oto-laryngologica Jan 31, 2020
Fluctuating hearing loss is characteristic of Ménière's disease (MD) during acute episodes. However, no reliable audiometric hallmarks are available for counselling the hearing recovery possibility. To find parameters for predicting MD hearing outco...
Prospective Studies Prognosis Acute Disease Evoked Potentials, Auditory Hearing Loss, Sensorineural Meniere Disease Hair Cells, Auditory, Outer Humans Machine Learning
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Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage.

Neuroradiology Dec 11, 2019
PURPOSE: To analyze the implementation of deep learning software for the detection and worklist prioritization of acute intracranial hemorrhage on non-contrast head CT (NCCT) in various clinical settings at an academic medical center.
Software Prospective Studies Tomography, X-Ray Computed Female Humans Acute Disease Intracranial Hemorrhages Sensitivity and Specificity Male Deep Learning Predictive Value of Tests
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Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.

Proceedings of the National Academy of Sciences of the United States of America Oct 21, 2019
Computed tomography (CT) of the head is used worldwide to diagnose neurologic emergencies. However, expertise is required to interpret these scans, and even highly trained experts may miss subtle life-threatening findings. For head CT, a unique chall...
Humans Neural Networks, Computer Algorithms Tomography, X-Ray Computed Deep Learning Acute Disease Intracranial Hemorrhages
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Machine Learning-Enabled Automated Determination of Acute Ischemic Core From Computed Tomography Angiography.

Stroke Sep 24, 2019
Background and Purpose- The availability of and expertise to interpret advanced neuroimaging recommended in the guideline-based endovascular stroke therapy (EST) evaluation are limited. Here, we develop and validate an automated machine learning-base...
Stroke Brain Ischemia Neuroimaging Computed Tomography Angiography Diagnosis, Computer-Assisted Deep Learning Acute Disease Registries Female Aged Male Databases, Factual Middle Aged Prospective Studies Electronic Health Records Humans Neural Networks, Computer
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Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke.

International journal of computer assisted radiology and surgery Sep 3, 2019
PURPOSE: Acute ischemic stroke is one of the primary causes of death worldwide. Recent studies have shown that the assessment of collateral status could aid in improving the treatment for patients with acute ischemic stroke. We present a 3D deep regr...
Follow-Up Studies Magnetic Resonance Imaging Acute Disease Brain Ischemia Humans Neural Networks, Computer Retrospective Studies Imaging, Three-Dimensional
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A Diagnostic Prediction Model of Acute Symptomatic Portal Vein Thrombosis.

Annals of vascular surgery Jul 25, 2019
BACKGROUND: The aim of this study was to develop a diagnostic prediction model to improve identification of acute symptomatic portal vein thrombosis (PVT).
Venous Thrombosis Predictive Value of Tests Acute Disease Portal Vein Middle Aged Humans Diagnosis, Computer-Assisted China Decision Support Techniques Support Vector Machine Retrospective Studies Reproducibility of Results Female Risk Factors Adult Male
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Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis.

BMC gastroenterology Jul 4, 2019
BACKGROUND: Multiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF.
Humans Machine Learning Prognosis Retrospective Studies Predictive Value of Tests Acute Disease Risk Assessment Female APACHE Severity of Illness Index Adult Pancreatitis Logistic Models Male Multiple Organ Failure Middle Aged
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Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

Journal of clinical pathology Jun 29, 2019
AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagn...
Image Interpretation, Computer-Assisted Diagnosis, Differential Leukemia Pattern Recognition, Automated Predictive Value of Tests Humans Blood Specimen Collection Acute Disease Machine Learning Cell Lineage Leukocytes Reproducibility of Results Staining and Labeling
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Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model.

Journal of medical systems Jun 13, 2019
The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical data. In order to achieve the regional medical and public health data analysis through ...
Support Vector Machine Chronic Disease Acute Disease Humans Risk Assessment Stroke Bayes Theorem Artificial Intelligence Severity of Illness Index Heart Failure Prognosis Diagnosis, Computer-Assisted Data Mining Logistic Models Renal Insufficiency
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