AIMC Topic: Hospitals, University
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A comparative study of attention mechanism based deep learning methods for bladder tumor segmentation.
International journal of medical informatics
Jan 5, 2023
BACKGROUND: Artificial intelligence aided tumor segmentation has been applied in various medical scenarios and showed effectiveness in helping physicians observe the potential malignant tissues. However, little research has been conducted for the cys...
Development of a system to support warfarin dose decisions using deep neural networks.
Scientific reports
Jul 20, 2021
The first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop a warfarin maintenance dose decision support system as a precise warfarin dosing platform. Data of 1...
Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan.
International journal of nursing studies
Mar 26, 2021
BACKGROUND: In hospitals, nurses are responsible for pressure injury risk assessment using several kinds of risk assessment scales. However, their predictive validity is insufficient to initiate targeted preventive strategy for each patient. The use ...
Supervised classification techniques for prediction of mortality in adult patients with sepsis.
The American journal of emergency medicine
Sep 12, 2020
BACKGROUND: Sepsis mortality is still unacceptably high and an appropriate prognostic tool may increase the accuracy for clinical decisions.
Clinical application of an intelligent oropharyngeal swab robot: implication for the COVID-19 pandemic.
The European respiratory journal
Aug 20, 2020
https://bit.ly/2BUsV55
Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.
Lung
May 18, 2020
PURPOSE: Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a predicti...
Fibrin Fibrinogen Degradation Products
Computed Tomography Angiography
Clinical Decision Rules
Creatinine
Hospitals, University
Heart Rate
Natriuretic Peptide, Brain
Body Mass Index
Oxygen
International Normalized Ratio
Pulmonary Embolism
Aged
Humans
Comorbidity
Adult
Machine Learning
Case-Control Studies
Middle Aged
Asthma
Intensive Care Units
Length of Stay
Disease Progression
Male
Female
Determination of Marital Status of Patients from Structured and Unstructured Electronic Healthcare Data.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Social Determinants of Health, including marital status, are becoming increasingly identified as key drivers of health care utilization. This paper describes a robust method to determine the marital status of patients using structured and unstructure...
[Hemoglobinuria in children hospitalized in Ouagadougou: short term inpatient care and prognosis].
The Pan African medical journal
Nov 26, 2019
INTRODUCTION: The purpose of this study was to analyze the epidemiological, diagnostic, therapeutic and evolutionary features of hemoglobinuria in children hospitalized in the Pediatric University Hospital Charles de Gaulle, Ouagadougou.
Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.
European journal of cancer (Oxford, England : 1990)
Aug 14, 2019
BACKGROUND: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer s...
Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.
European journal of cancer (Oxford, England : 1990)
Apr 10, 2019
BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For ...
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