AIMC Topic: ROC Curve
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Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes.
Scientific reports
Jul 31, 2024
Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overestimated results, while those studies conducted in real-life remain scarce. To avoid image selection ...
SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.
Scientific reports
Jul 31, 2024
Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission ris...
Modelling the rapid detection of Carbapenemase-resistant Klebsiella pneumoniae based on machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.
Diagnostic microbiology and infectious disease
Jul 30, 2024
In this study, 80 carbapenem-resistant Klebsiella pneumoniae (CR-KP) and 160 carbapenem-susceptible Klebsiella pneumoniae (CS-KP) strains detected in the clinic were selected and their matrix-assisted laser desorption/ionization time-of-flight mass s...
Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.
Computer methods and programs in biomedicine
Jul 29, 2024
BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combinin...
Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer.
BMC cancer
Jul 29, 2024
PURPOSE: A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis...
Development and validation of a machine learning framework for improved resource allocation in the emergency department.
The American journal of emergency medicine
Jul 27, 2024
OBJECTIVE: The Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted resource utilization as a means to triage [1], Mistriage, which includes both undertriage and overtr...
Diagnostic Performance of Machine Learning-based Models in Neonatal Sepsis: A Systematic Review.
The Pediatric infectious disease journal
Jul 26, 2024
BACKGROUND: Timely diagnosis of neonatal sepsis is challenging. We aimed to systematically evaluate the diagnostic performance of sophisticated machine learning (ML) techniques for the prediction of neonatal sepsis.
Prediction of leukemia peptides using convolutional neural network and protein compositions.
BMC cancer
Jul 26, 2024
Leukemia is a type of blood cell cancer that is in the bone marrow's blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemia...
Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer.
BMC medical imaging
Jul 26, 2024
BACKGROUND: The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC).
Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis.
The American journal of gastroenterology
Jul 25, 2024
INTRODUCTION: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.
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