Publications associées à Classification of patients with cardiac amyloidosis using machine learning models on Italian electronic clinical health records

The role of computational methods in cardiovascular medicine: a narrative review

Alfio Quarteroni, Luca Dede', Christian Vergara, Stefano Pagani

Background and Objective: Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data wi ...

Ame Publishing Company2024

A Tunable Self-Offloading Module for Plantar Pressure Regulation in Diabetic Patients

Paolo Germano, Christian Köchli, Yves Perriard, Yoan René Cyrille Civet, Kenny Jeanmonod, Sofia Lydia Ntella, Bhawnath Tiwari

Plantar pressure plays a crucial role in the pathogenesis of foot ulcers among patients with diabetes and peripheral polyneuropathy. Pressure relief is a key requirement for both the prevention and treatment of plantar ulcers. Conventional medical practice ...

2024

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects

Mats Julius Stensrud

Sometimes treatment effects are absent in a subgroup of the population. For example, penicillin has no effect on severe symptoms in individuals infected by resistant Staphylococcus aureus, and codeine has no effect on pain in individuals with certain polym ...

Lippincott Williams & Wilkins2024

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Arnout Jan J Devos

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

EPFL2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

Immersive VR for upper-extremity rehabilitation in patients with neurological disorders: a scoping review

Silvestro Micera, Elena Losanno

Background Neurological disorders, such as stroke and chronic pain syndromes, profoundly impact independence and quality of life, especially when affecting upper extremity (UE) function. While conventional physical therapy has shown effectiveness in provid ...

BioMed Central2024

Method of preparation of amyloidogenic protein aggregates and uses thereof

Hilal Lashuel, Galina Limorenko

The invention relates to methods for the preparation of method of preparation of Tau aggregates, including fibrils, fibrillar species, soluble and insoluble oligomeric species, new Tau aggregates and uses thereof. The invention further relates to the uses ...

2024

Robust machine learning for neuroscientific inference

Steffen Schneider

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

EPFL2024

Iron-carbohydrate complexes treating iron anaemia: Understanding the nano-structure and interactions with proteins through orthogonal characterisation

Antonia Neels, Tiberiu Totu, Marianne Liebi

Intravenous (IV) iron-carbohydrate complexes are widely used nanoparticles (NPs) to treat iron deficiency anaemia, often associated with medical conditions such as chronic kidney disease, heart failure and various inflammatory conditions. Even though a ple ...

Elsevier2024

Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis

Michele De Palma, Nahal Mansouri

Aims: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk o ...

2023

Rapid low-cost hyperspectral imaging system for quantitative assessment of infantile hemangioma

Viktor Vorobev

Hemangioma, the predominant benign tumor occurring in infancy, exhibits a wide range of prognoses and associated outcomes. The accurate determination of prognosis through noninvasive imaging modalities holds essential importance in enabling effective perso ...

Wiley-V C H Verlag Gmbh2023

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study

Jonathan Dönz, Mary-Anne Hartley

BackgroundInterstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prom ...

BMC2023

Plasma biomarkers for Alzheimer's disease: a field-test in a memory clinic

Aurélien Lathuilière

Background The key Alzheimer's disease (AD) biomarkers are traditionally measured with techniques/exams that are either expensive (amyloid-positron emission tomography (PET) and tau-PET), invasive (cerebrospinal fluid A beta 42 and p-tau 181), or poorly sp ...

BMJ PUBLISHING GROUP2023

Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy

Olivier Michielin, Monia Guidi, Alexandre Wicky, Nadia Terranova, Daniel Abler, Federico Amato

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomark ...

2023

Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry

The increasing implementation of programs supported by machine learning in medical contexts will affect psychiatry. It is crucial to accompany this development with careful ethical considerations informed by empirical research involving experts from the fi ...

2023

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Christian Robert Abbet

Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...

EPFL2023

ERS International Congress 2022: highlights from the Respiratory Clinical Care and Physiology Assembly

Sophie Jacqueline Andrée Betka

It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of the laboratory-based science, clinical trials and qualitative research that were presented during the 2 ...

EUROPEAN RESPIRATORY SOC JOURNALS LTD2023

Monitoring of Protein and pH Levels in Human Biofluids using FD-SOI Silicon Nanowire Arrays

Luca Capua

Medical research and technological advancements are heading towards tailored healthcare approaches that prioritize individual needs, allowing for more accurate diagnoses, more effective treatments, and better patient outcomes overall. One such approach is ...

EPFL2023

Recombinant Full-Length TDP-43 Oligomers Retain Their Ability to Bind RNAs, Are Not Toxic, and Do Not Seed TDP-43 Aggregation in Vitro

Hilal Lashuel, Lixin Yang, Yllza Jasiqi

TAR DNA-binding protein with 43 kD (TDP-43) is a partially disordered protein that misfolds and accumulates in the brains of patients affected by several neurodegenerative diseases. TDP-43 oligomers have been reported to form due to aberrant misfolding or ...

Amer Chemical Soc2023

Deconstructing and Reconstructing the Complexity of Tau pathology

Galina Limorenko

This thesis consists of four Chapters unified by a singular theme â how do we develop disease models that faithfully reproduce the pathology seen in patients suffering from neurodegenerative disorders associated with the Tau protein, such as Alzheimerâ ...

EPFL2023