Search results for "Mach"
showing 10 items of 3360 documents
Invisible gastric carcinoma detected by random biopsy: long-term results after photodynamic therapy
2008
Background and study aims Gastric cancer diagnosed from routine gastric biopsies without any evidence of a visible lesion and negative repeated biopsies is an infrequent but serious clinical problem for which gastrectomy has usually been recommended, even if operative specimens do not show cancer either. We report on a series of 22 such patients undergoing long-term follow-up after attempted treatment with photodynamic therapy (PDT). Patients and methods 22 patients with invisible gastric cancer (IGC) who presented during a 10-year period (10 men, mean age 56 +/- 15 years) were prospectively included. Initial histopathological findings confirmed by second opinion included 10 well-differenti…
Decoding Musical Training from Dynamic Processing of Musical Features in the Brain
2018
AbstractPattern recognition on neural activations from naturalistic music listening has been successful at predicting neural responses of listeners from musical features, and vice versa. Inter-subject differences in the decoding accuracies have arisen partly from musical training that has widely recognized structural and functional effects on the brain. We propose and evaluate a decoding approach aimed at predicting the musicianship class of an individual listener from dynamic neural processing of musical features. Whole brain functional magnetic resonance imaging (fMRI) data was acquired from musicians and nonmusicians during listening of three musical pieces from different genres. Six mus…
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…
Phase information of time-frequency transforms as a key feature for classification of atrial fibrillation episodes
2015
[EN] Patients suffering from atrial fibrillation can be classified into different subtypes, according to the temporal pattern of the arrhythmia and its recurrence. Nowadays, clinicians cannot differentiate a priori between the different subtypes, and patient classification is done afterwards, when its clinical course is available. In this paper we present a comparison of classification performances when differentiating paroxysmal and persistent atrial fibrillation episodes by means of support vector machines. We analyze short surface electrocardiogram recordings by extracting modulus and phase features from several time-frequency transforms: short-time Fourier transform, Wigner-Ville, Choi-…
Clinical validation of a virtual environment for normalizing eating patterns in eating disorders
2013
The purpose of the present study was to examine the clinical validation of a Virtual Reality Environment (VRE) designed to normalize eating patterns in Eating Disorders (ED). The efficacy of VR in eliciting emotions, sense of presence and reality of the VRE were explored in 22 ED patients and 37 healthy eating individuals. The VRE (non-immersive) consisted of a kitchen room where participants had to eat a virtual pizza. In order to assess the sense of presence and reality produced by the VRE, participants answered seven questions with a Likert scale (0-10) during the experience, and then filled out the Reality Judgment and Presence Questionnaire (RJPQ) and ITC-Sense of Presence Inventory (I…
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…
Esophageal transmural potential difference in patients with symptomatic gastroesophageal reflux.
1980
Esophageal electrical potential difference (PD) was studied in 9 patients with symptomatic gastroesophageal reflux and in 9 healthy control subjects. None of the patients revealed gross mucosal damage by radiography or endoscopy, but all of them showed positive acid perfusion studies. In the stomach and across the lower esophageal sphincter PD profiles were remarkably similar in patients and controls. Throughout the lower esophagus however, PD values were slightly higher in patients with symptomatic reflux than in healthy volunteers. These data are in contrast to a previous investigation, in which patients with reflux-induced gross mucosal damage revealed a decreased PD in the lower esophag…