43 research outputs found

    Linguistic profile automated characterisation in pluripotential clinical high-risk mental state (CHARMS) conditions: methodology of a multicentre observational study

    Get PDF
    Introduction: Language is usually considered the social vehicle of thought in intersubjective communications. However, the relationship between language and high- order cognition seems to evade this canonical and unidirectional description (ie, the notion of language as a simple means of thought communication). In recent years, clinical high at-risk mental state (CHARMS) criteria (evolved from the Ultra-High-Risk paradigm) and the introduction of the Clinical Staging system have been proposed to address the dynamicity of early psychopathology. At the same time, natural language processing (NLP) techniques have greatly evolved and have been successfully applied to investigate different neuropsychiatric conditions. The combination of at-risk mental state paradigm, clinical staging system and automated NLP methods, the latter applied on spoken language transcripts, could represent a useful and convenient approach to the problem of early psychopathological distress within a transdiagnostic risk paradigm. Methods and analysis: Help-seeking young people presenting psychological distress (CHARMS+/− and Clinical Stage 1a or 1b; target sample size for both groups n=90) will be assessed through several psychometric tools and multiple speech analyses during an observational period of 1-year, in the context of an Italian multicentric study. Subjects will be enrolled in different contexts: Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Section of Psychiatry, University of Genoa—IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Mental Health Department—territorial mental services (ASL 3—Genoa), Genoa, Italy; and Mental Health Department—territorial mental services (AUSL—Piacenza), Piacenza, Italy. The conversion rate to full-blown psychopathology (CS 2) will be evaluated over 2 years of clinical observation, to further confirm the predictive and discriminative value of CHARMS criteria and to verify the possibility of enriching them with several linguistic features, derived from a fine-grained automated linguistic analysis of speech. Ethics and dissemination: The methodology described in this study adheres to ethical principles as formulated in the Declaration of Helsinki and is compatible with International Conference on Harmonization (ICH)-good clinical practice. The research protocol was reviewed and approved by two different ethics committees (CER Liguria approval code: 591/2020—id.10993; Comitato Etico dell’Area Vasta Emilia Nord approval code: 2022/0071963). Participants will provide their written informed consent prior to study enrolment and parental consent will be needed in the case of participants aged less than 18 years old. Experimental results will be carefully shared through publication in peer- reviewed journals, to ensure proper data reproducibility. Trial registration number DOI:10.17605/OSF.IO/BQZTN

    Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

    Get PDF
    Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

    NL4AI 2023: Overview of the Seventh Workshop on Natural Language for Artificial Intelligence (NL4AI 2023)

    No full text
    The Natural Language for Artificial Intelligence (NL4AI) workshop serves as a platform to explore the area situated at the intersection between Natural Language Processing (NLP) and Artificial Intelligence (AI), with a special emphasis on recent activities carried out in both fields in Italy. The seventh edition of the workshop set a new record with 23 submissions, of which 18 were accepted. The submissions span a broad spectrum of topics, encompassing foundational NLP research, applied NLP, and works that bridge the realms of NLP and AI. Notably, this edition exhibited a growing international presence, featuring contributions from authors representing 9 countries. The submissions also reflect a diversity of languages (e.g., English, French, Italian) and modalities (e.g., text, vision), underscoring the workshop's commitment to inclusivity and comprehensive exploration

    UniWireless: a Distributed Open Access Network

    No full text
    In this paper we describe the UniWireless framework, a nationwide distributed Open Access testbed that involves different research units collaborating in the TWELVE national project. The Uni-Fy AAA system, used to manage the collection of involved hotspots, is also discussed. The most important aspect of the UniWireless framework is its compatibility with different authentication mechanisms; while most access networks enforce a particular authentication protocol upon their users, in the UniWireless system different mechanisms coexist, and each client can in principle use the one that it considers most suitable. Two different, independent and coexisting authentication protocols (capive portal and a SIP-based technique) have been implemented and are described in this paper. Besides its academic and scientific value for demonstrating results and supporting research activities, the UniWireless framework is actually used to grant access to nomadic users that belong to different research units in all the hotspots related to the project. Every nomadic user can access network resources from every hotspot in the testbed by his usual authentication credentials. Experience gathered from more than one year of continuative use of the system is also discussed

    Extreme Learning Machine for Active RFID Location Classification

    No full text

    RSSI-Based Fingerprint Positioning System for Indoor Wireless Network

    No full text

    CoCoGen - Complexity Contour Generator: Automatic Assessment of Linguistic Complexity Using a Sliding-Window Technique

    No full text
    We present a novel approach to the automatic assessment of text complexity based on a sliding-window technique that tracks the distribution of complexity within a text. Such distribution is captured by what we term “complexity contours” derived from a series of measurements for a given linguistic complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of linguistic complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing
    corecore