AIMC Topic: Predictive Value of Tests
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Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy.
Cardiovascular toxicology
Nov 18, 2021
Cardiotoxicity is a severe side effect for colorectal cancer (CRC) patients undergoing fluoropyrimidine-based chemotherapy. To develop and compare machine learning algorithms to predict cardiotoxicity risk among nationally representative CRC patients...
Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.
Scientific reports
Nov 18, 2021
Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data inform...
Humans
Neural Networks, Computer
User-Computer Interface
Image Processing, Computer-Assisted
Computers
Probability
Predictive Value of Tests
Machine Learning
Reproducibility of Results
Principal Component Analysis
Wavelet Analysis
Software
Colorectal Neoplasms
Computer Graphics
Algorithms
Deep Learning
Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging.
Nature communications
Nov 17, 2021
In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic...
Can we predict anti-seizure medication response in focal epilepsy using machine learning?
Clinical neurology and neurosurgery
Nov 16, 2021
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM r...
Characterizing impact of positive lymph node number in endometrial cancer using machine-learning: A better prognostic indicator than FIGO staging?
Gynecologic oncology
Nov 15, 2021
BACKGROUND: Number of involved lymph nodes (LNs) is a crucial stratification factor in staging of numerous disease sites, but has not been incorporated for endometrial cancer. We evaluated whether number of involved LNs provide improved prognostic va...
Early prediction of hemodynamic interventions in the intensive care unit using machine learning.
Critical care (London, England)
Nov 14, 2021
BACKGROUND: Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible h...
Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Nov 11, 2021
BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising acc...
Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.
Clinical nutrition (Edinburgh, Scotland)
Nov 10, 2021
BACKGROUND & AIMS: Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (...
Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.
PloS one
Nov 3, 2021
During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and com...
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