AI Medical Compendium Topic:
Severity of Illness Index

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Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.

PloS one Jul 28, 2020
This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consi...
Severity of Illness Index Tomography, X-Ray Computed Humans Deep Learning Female Pneumonia, Viral Artificial Intelligence Radiologists Male Pandemics Coronavirus Infections Middle Aged COVID-19 Lung
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Prediction on critically ill patients: The role of "big data".

Journal of critical care Jul 23, 2020
Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simpl...
Electronic Health Records Severity of Illness Index Length of Stay Hospital Mortality Critical Care Delivery of Health Care Prognosis Critical Illness Intensive Care Units Humans Machine Learning
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Ischemia and outcome prediction by cardiac CT based machine learning.

The international journal of cardiovascular imaging Jul 4, 2020
Cardiac CT using non-enhanced coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA) has been proven to provide excellent evaluation of coronary artery disease (CAD) combining anatomical and morphological assessment of CAD for card...
Vascular Calcification Computed Tomography Angiography Coronary Artery Disease Heart Disease Risk Factors Coronary Vessels Plaque, Atherosclerotic Humans Machine Learning Prognosis Reproducibility of Results Risk Assessment Severity of Illness Index Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted
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Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

Scientific reports Jul 3, 2020
We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitali...
Humans Machine Learning Prospective Studies Infant Severity of Illness Index Cohort Studies Regression Analysis Decision Trees Intensive Care Units Female Hospitalization Male Predictive Value of Tests Positive-Pressure Respiration Bronchiolitis
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Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning.

Journal of hematology & oncology Jul 3, 2020
Due to acromegaly's insidious onset and slow progression, its diagnosis is usually delayed, thus causing severe complications and treatment difficulty. A convenient screening method is imperative. Based on our previous work, we herein developed a new...
Acromegaly Photography Diagnostic Self Evaluation Middle Aged Severity of Illness Index Diagnosis, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Models, Theoretical Algorithms Cephalometry Datasets as Topic Humans Female Face Adult Sensitivity and Specificity Male
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From CT to artificial intelligence for complex assessment of plaque-associated risk.

The international journal of cardiovascular imaging Jul 2, 2020
The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust inform...
Machine Learning Prognosis Reproducibility of Results Risk Assessment Coronary Vessels Severity of Illness Index Rupture, Spontaneous Plaque, Atherosclerotic Computed Tomography Angiography Humans Predictive Value of Tests Coronary Stenosis Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Heart Disease Risk Factors
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Objective evaluation of allergic conjunctival disease (with a focus on the application of artificial intelligence technology).

Allergology international : official journal of the Japanese Society of Allergology Jun 17, 2020
We have summarized the past efforts and results of objective measurement methods for conjunctival hyperemia classification. Severity classification using conjunctival blood vessel occupancy rate, ocular surface temperature analysis, and artificial in...
Severity of Illness Index Humans Hyperemia Slit Lamp Microscopy Artificial Intelligence Conjunctivitis, Allergic
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Defining heterogeneity of epicardial functional stenosis with low coronary flow reserve by unsupervised machine learning.

Heart and vessels Jun 6, 2020
Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the actual coronary flow, such as high resting or low hyperemic coronary flow, which should have different physiological traits and clinical implications....
Unsupervised Machine Learning Predictive Value of Tests Humans Registries Japan Spain Blood Flow Velocity Cardiac Catheterization Coronary Stenosis Fractional Flow Reserve, Myocardial Male Middle Aged Prognosis Severity of Illness Index Republic of Korea Hyperemia Diagnosis, Computer-Assisted Female Cluster Analysis Aged
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Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients.

Annals of the rheumatic diseases Jun 5, 2020
OBJECTIVES: We have previously shown that neural network technology can be used for scoring arthritis disease activity in ultrasound images from rheumatoid arthritis (RA) patients, giving scores according to the EULAR-OMERACT grading system. We have ...
Ultrasonography Arthritis, Rheumatoid Reproducibility of Results Image Interpretation, Computer-Assisted Joints Humans Clinical Trials as Topic Adult Rheumatology Neural Networks, Computer Male Female Synovitis Middle Aged Severity of Illness Index
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Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.

PloS one Jun 5, 2020
Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In thi...
Time Factors ROC Curve Renal Insufficiency, Chronic Taiwan Models, Biological Clinical Decision-Making Disease Progression Comorbidity Severity of Illness Index Datasets as Topic Time-to-Treatment Humans Machine Learning Retrospective Studies Renal Replacement Therapy
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