Search results for "machine learning."
showing 10 items of 1455 documents
Subgrouping factors influencing migraine intensity in women: A semi-automatic methodology based on machine learning and information geometry
2019
[EN] Background Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling techniques. Objective The aim of this study was to analyze critical features that permit the differentiation of subgroups of patients with migraine according to the intensity and frequency of attacks by using machine learning algorithms. Methods Sixty-seven women with migraine participated. Clinical features of migraine, related disability (Migraine Disability Assessment Scale), anxiety/depressive levels (Hospital Anxiety and Depression Scale), anxiety state/trait levels (S…
On the complementarity of holistic and analytic approaches to performance assessment scoring.
2019
BACKGROUND A holistic approach to performance assessment recognizes the theoretical complexity of multifaceted critical thinking (CT), a key objective of higher education. However, issues related to reliability, interpretation, and use arise with this approach. AIMS AND METHOD Therefore, we take an analytic approach to scoring students' written responses on a performance assessment. We focus on the complementarity of holistic and analytic approaches and on whether theoretically developed analytical scoring rubrics can produce sub-scores that may measure the 'whole' performance in a holistic assessment. SAMPLE We use data from the Wind Turbines performance assessment, developed in the iPAL p…
Whole-body MRI radiomics model to predict relapsed/refractory Hodgkin Lymphoma: A preliminary study.
2022
Purpose A strong prognostic score that enables a stratification of newly diagnosed Hodgkin Lymphoma (HL) to identify patients at high risk of refractory/relapsed disease is still needed. Our aim was to investigate the potential value of a radiomics analysis pipeline from whole-body MRI (WB-MRI) exams for clinical outcome prediction in patients with Hodgkin Lymphoma (HL). Materials and methods Index lesions from baseline WB-MRIs of 40 patients (22 females; mean age 31.7 ± 11.4 years) with newly diagnosed HL treated by ABVD chemotherapy regimen were manually segmented on T1-weighted, STIR, and DWI images for texture analysis feature extraction. A machine learning approach based on the Extra T…
How to assess the risks associated with the usage of a medical device based on predictive modeling: the case of an anemia control model certified as …
2021
Background The successful application of Machine Learning (ML) to many clinical problems can lead to its implementation as medical devices (MD), being important to assess the associated risks. Methods An anemia control model (ACM), certified as MD may face adverse events as the result of wrong predictions that are translated into suggestions of doses of erythropoietic stimulating agents to dialysis patients. Risks are assessed as the combination of severity and probability of a given hazard. While severities are typically assessed by clinicians, probabilities are tightly related to the performance of the predictive model. Results A post-marketing dataset formed by all adult patients registe…
Achievement of treatment targets predicts progression of vascular complications in type 1 diabetes.
2021
Abstract Background and aim To study the association between achievement of guideline-defined treatment targets on HbA1c, low-density lipoproteins (LDL-C), and blood pressure with the progression of diabetic complications in patients with type 1 diabetes (T1D). Methods The study included 355 patients at baseline and 114 patients with follow-up data after 3–5 years. Outcome variables were the progression of diabetic kidney disease, retinopathy, or cardiovascular disease (CVD). We used logistic regression and other machine learning algorithms (MLA) to model the association of achievement of treatment targets and probability of progression of complications. Results Achievement of the target bl…
miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sici…
2022
Human ageing can be characterized by a profile of circulating microRNAs (miRNAs), which are potentially predictors of biological age. They can be used as a biomarker of risk for age-related inflammatory outcomes, and senescent endothelial cells (ECs) have emerged as a possible source of circulating miRNAs. In this paper, a panel of four circulating miRNAs including miR-146a-5p, miR-126-3p, miR-21-5p, and miR-181a-5p, involved in several pathways related to inflammation, and ECs senescence that seem to be characteristic of the healthy ageing phenotype. The circulating levels of these miRNAs were determined in 78 healthy subjects aged between 22 to 111 years. Contextually, extracellular miR-1…
Methods matter: Testing competing models for designing short-scale Big-Five assessments
2015
Abstract Many psychological instruments are psychometrically inadequate because derived person-parameters are unfounded and models will be rejected using established psychometric criteria. One strategy towards improving the psychometric properties is to shorten instruments. We present and compare the following procedures for the abbreviation of self-report assessments on the Trait Self-Description Inventory in a sample of 14,347 participants: (a) Maximizing reliability/main loadings, (b) Minimizing modification indices/cross loadings, (c) the PURIFY Algorithm in Tetrad, (d) Ant Colony Optimization, and (e) a genetic algorithm. Ant Colony Optimization was superior to all other methods in imp…
Worker safety in agriculture 4.0: A new approach for mapping operator's vibration risk through Machine Learning activity recognition
2022
While being a fundamental driver of competitiveness in agroindustry, technological innovation has also introduced new critical elements related, for example, to the sustainability of the production processes as well as to the safety of workers. In such regard, the advent of the 4th industrial revolution (Agriculture 4.0) based on digitalization, is an unprecedented opportunity of rethinking the role of innovation in a new human-centric perspective. In particular, the establishment of an interconnected work environment and the augmentation of the operator’s physical, sensorial, and cognitive capabilities, are two technologies which can be effectively employed for substantially improving the …
Learning from good examples
1995
The usual information in inductive inference for the purposes of learning an unknown recursive function f is the set of all input /output examples (n,f(n)), n ∈ ℕ. In contrast to this approach we show that it is considerably more powerful to work with finite sets of “good” examples even when these good examples are required to be effectively computable. The influence of the underlying numberings, with respect to which the learning problem has to be solved, to the capabilities of inference from good examples is also investigated. It turns out that nonstandard numberings can be much more powerful than Godel numberings.
Multi-sensor Fusion through Adaptive Bayesian Networks
2011
Common sensory devices for measuring environmental data are typically heterogeneous, and present strict energy constraints; moreover, they are likely affected by noise, and their behavior may vary across time. Bayesian Networks constitute a suitable tool for pre-processing such data before performing more refined artificial reasoning; the approach proposed here aims at obtaining the best trade-off between performance and cost, by adapting the operating mode of the underlying sensory devices. Moreover, self-configuration of the nodes providing the evidence to the Bayesian network is carried out by means of an on-line multi-objective optimization.