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

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

Symptoms Identification of ICD-11 Based on Clinical NLP Mobile Apps for Diagnosing the Disease (ICD-11)

Introduction: There are still many people in Indonesia who are not aware of the importance of information related to the early symptoms that must be experienced when they become patients. Not infrequently, this lack of information disclosure results in misdiagnosis and even leads to unexpected death. Anamnesis is a process where the doctor or medical record nurse gives several questions about the clinical pathway in the form of a narrative to facilitate early identification of the disease, and the results of this history-taking process are stored in the Electronic Medical Record (EMR). EMR narratives often cannot be processed by computers if language literacy is not standardized or ambiguous, so the need to overcome this problem requires the use of technology to minimize misdiagnosis and facilitate the identification process by developing digitization in the form of mobile applications that are integrated with Natural Language Processing technology and ICD-11 in the symptom identification process. This study aims to identify ICD-11 symptoms based on clinical NLP mobile application to diagnose the disease (ICD-11). Methods: The applications of Natural language processing includes literature study, Voice Recognition, Tokenization, Stemming, The process of Stopwords Removal, Named Entity Recognition, Data Translation, Access ICD Data, and Mobile User Interfaces. Results: Named Entity Recognition (NER) is used to identify symptoms of digestive system diseases, with an accuracy rate of 74.3%. In stemming and stopwords processing, the NLP accuracy rates are 95.9% and 97.2%, respectively. Conclusions: This research focuses on the application mobile and development of the Named Entity Recognition (NER) model. The importance of the NLP process in the development of information, especially for word processing, aims as a device that simplifies speech recognition systems to be more helpful