AIMC Topic: Machine Learning

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Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.

Genes
It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets...

Comparing classification techniques for identification of grasped objects.

Biomedical engineering online
BACKGROUND: This work presents a comparison and selection of different machine learning classification techniques applied in the identification of objects using data collected by an instrumented glove during a grasp process. The selected classifiers ...

Recursive neural networks in hospital bed occupancy forecasting.

BMC medical informatics and decision making
BACKGROUND: Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday ...

Predicting one-year outcome in first episode psychosis using machine learning.

PloS one
BACKGROUND: Early illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes. Our study object...

The trauma severity model: An ensemble machine learning approach to risk prediction.

Computers in biology and medicine
Statistical theory indicates that a flexible model can attain a lower generalization error than an inflexible model, provided that the setting is appropriate. This is highly relevant for mortality risk prediction with trauma patients, as researchers ...

DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC.

PloS one
Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great promise in the treatment of diseases such as Parkinson's, Huntington's, diabetes mellitus, etc. hESC ...

Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults.

PloS one
BACKGROUND: Payers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. Penalized linear regression represents a practical and incremental step forward that p...

Psychological characteristics and stress differentiate between high from low health trajectories in later life: a machine learning analysis.

Aging & mental health
: This study set out to empirically identify joint health trajectories in individuals of advanced age. Predictors of subgroup allocation were investigated to identify the impact of psychological characteristics, stress, and socio-demographic variable...