Results 71 to 80 of about 33,125 (301)

A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi   +17 more
wiley   +1 more source

SeXAI: Introducing Concepts into Black Boxes for Explainable Artificial Intelligence

open access: yes, 2020
The interest in Explainable Artificial Intelligence (XAI) research is dramatically grown during the last few years. The main reason is the need of having systems that beyond being effective are also able to describe how a certain output has been obtained
Ivan Donadello, Mauro Dragoni
core  

An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets

open access: yes, 2021
Xenophobia is a social and political behavior that has been present in our societies since the beginning of humanity. The feeling of hatred, fear, or resentment is present before people from different communities from ours.
Octavio Loyola-González   +2 more
core   +1 more source

Thalamo‐Lesional Connectivity Signatures of Bilateral Tonic–Clonic Seizures in Focal Cortical Dysplasia‐Related Epilepsy

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives Focal cortical dysplasia (FCD) is the most common etiology of drug‐resistant epilepsy in children. Focal to bilateral tonic–clonic seizures (FBTCS) mark a high risk of drug‐resistant epilepsy and involve thalamocortical circuitry in their generation and propagation.
Hua Xie   +8 more
wiley   +1 more source

Causability and explainability of artificial intelligence in medicine [PDF]

open access: yesWIREs Data Mining and Knowledge Discovery, 2019
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world.
Andreas Holzinger   +4 more
openaire   +2 more sources

Integrating Time‐Adjusted Imaging Instability Into Functional Outcome Prediction After Intracerebral Hemorrhage: Development and Validation of the HAGIV Score

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Early risk stratification may support clinical decision‐making in spontaneous intracerebral hemorrhage (ICH). We aimed to develop and internally validate HAGIV, a score integrating frequency of imaging markers (FIM), a time‐adjusted non‐contrast computed tomography (CT) metric of hematoma expansion, with established predictors for 90‐
Lei Song   +10 more
wiley   +1 more source

The Role of Causality in Explainable Artificial Intelligence

open access: yesWIREs Data Mining and Knowledge Discovery
ABSTRACTCausality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to
Carloni, Gianluca   +2 more
openaire   +4 more sources

Evaluation of a Novel Electric Health Record Sidecar Application to Display Rheumatoid Arthritis Clinical Outcomes During Clinic Visits: Results of a Stepped‐Wedge Cluster Randomized Pragmatic Trial

open access: yesArthritis Care &Research, EarlyView.
Objective We developed a novel electronic health record sidecar application to visualize key rheumatoid arthritis (RA) outcomes, including disease activity, physical function, and pain, via a patient‐facing graphical interface designed for use during outpatient visits (“RA PRO dashboard”).
Gabriela Schmajuk   +16 more
wiley   +1 more source

XAI.it 2021 - Preface to the Second Italian Workshop on Explainable Artificial Intelligence [PDF]

open access: yes, 2021
Artificial Intelligence systems are increasingly playing an increasingly important role in our daily lives. As their importance in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as ...
Musto C.   +3 more
core  

Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases

open access: yesBiomimetics
Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization’s ultimate goal of breast self-examination. A literature review indicates the urgency of improving
Yerken Mirasbekov   +6 more
doaj   +1 more source

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