AIMC Topic: Severity of Illness Index
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Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading cause...
Association of AI quantified COVID-19 chest CT and patient outcome.
International journal of computer assisted radiology and surgery
Jan 23, 2021
PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to d...
Comparison of MPL-ANN and PLS-DA models for predicting the severity of patients with acute pancreatitis: An exploratory study.
The American journal of emergency medicine
Jan 22, 2021
OBJECTIVE: Acute pancreatitis (AP) is a common inflammatory disorder that may develop into severe AP (SAP), resulting in life-threatening complications and even death. The purpose of this study was to explore two different machine learning models of ...
Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods.
Computer methods and programs in biomedicine
Jan 22, 2021
BACKGROUND AND OBJECTIVE: The new type of Coronavirus (2019-nCov) epidemic spread rapidly, causing more than 250 thousand deaths worldwide. The virus, which first appeared as a sign of pneumonia, was later called the SARS-COV-2 with Severe Acute Resp...
Deep Neural Network Accurately Predicts Prognosis of Ulcerative Colitis Using Endoscopic Images.
Gastroenterology
Jan 21, 2021
A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.
PloS one
Jan 19, 2021
INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important f...
Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.
Academic radiology
Jan 18, 2021
RATIONALE AND OBJECTIVES: Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intell...
A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.
Computers in biology and medicine
Jan 18, 2021
COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (W...
Machine learning-based multimodal prediction of language outcomes in chronic aphasia.
Human brain mapping
Dec 30, 2020
Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following this line of work, the current study uses machine learning approaches to predict aphasia severity and ...
Multimodal Imaging
Chronic Disease
Humans
Stroke
Female
Cerebrovascular Circulation
Severity of Illness Index
Aged
Outcome Assessment, Health Care
Diffusion Tensor Imaging
Aged, 80 and over
Neuroimaging
Functional Neuroimaging
Language Tests
Adult
Support Vector Machine
Aphasia
Male
Magnetic Resonance Imaging
Middle Aged
Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification.
Scientific reports
Dec 16, 2020
To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 inf...
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