AIMC Topic: Multivariate Analysis
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A Hybrid Water Balance Machine Learning Model to Estimate Inter-Annual Rainfall-Runoff.
Sensors (Basel, Switzerland)
Apr 23, 2022
Watershed climatic diversity poses a hard problem when it comes to finding suitable models to estimate inter-annual rainfall runoff (IARR). In this work, a hybrid model (dubbed MR-CART) is proposed, based on a combination of MR (multiple regression) ...
Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems.
Biointerphases
Mar 28, 2022
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormo...
Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards.
PloS one
Jan 21, 2022
Completing the genotype-to-phenotype map requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause prob...
Survival analysis with semi-supervised predictive clustering trees.
Computers in biology and medicine
Nov 3, 2021
Many clinical studies follow patients over time and record the time until the occurrence of an event of interest (e.g., recovery, death, …). When patients drop out of the study or when their event did not happen before the study ended, the collected ...
A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.
Scientific reports
Oct 27, 2021
The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction mo...
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.
BMJ (Clinical research ed.)
Oct 20, 2021
OBJECTIVE: To assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties.
Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database.
Computational and mathematical methods in medicine
Oct 13, 2021
In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begi...
Identifying individuals with recent COVID-19 through voice classification using deep learning.
Scientific reports
Sep 27, 2021
Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration ...
Multivariate random forest prediction of poverty and malnutrition prevalence.
PloS one
Sep 8, 2021
Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies' programming. However, state of the art models ofte...
Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling.
Molecules (Basel, Switzerland)
Aug 23, 2021
Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, an...
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