Aprenentatge automàtic per predir risc cardiovascular amb dades clíniques

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Laura Igual Muñoz[en] Atherosclerosis is one of the main precursors to cardiovascular pathologies, the first defunction cause on developed countries. One of its principal diagnosis methodologies is carotid ultrasound images due to their low cost and intrusivity. Nonetheless, these produce low quality representations, which makes the diagnosis of atherosclerotic plaques a laborious task. In spite of that, other risk measurement methodologies exist. Risk tables which, taking into consideration diverse lifestyle and medical data, assign the probability of an individual to suffer a cardiovascular event. These types of tables inherit their functionality from the Framingham study, which analyzed data of United States population to create its risk function, thus being the first study to do so. However, adapting these tables to all population is not precise, as there are different epidemiological factors that can affect the values of the tables, and conducting studies to adjust them is expensive. Moreover, other limitations exist, as it has been proved that most of the future cardiovascular events end up classified on mid-range risk groups, thus not being medicated, besides an age limit to apply the tables, and not accepting missing values. This project sets out to improve the current REGICOR risk function, computed in catalan population, using machine learning prediction models and a combination of medical and ultrasound data of volunteers

Transformers in depression detection from semi-structured psychological Interviews

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2022-2023. Tutor: Jordi Vitrià i Marca, Javi Jiménez i Alberto CocaThe expansive adoption of Transformer models across the Machine Learning landscape is undeniable, and health is not an exception. This study undertakes a rigorous exploration of the efficacy of these novel architectures in discerning depression indicators from semi-structured psychological interviews. A key focus of this study is the extrapolation of the pre-training knowledge inherent in these models, and the comparison with traditional state-of-the-art Machine Learning models. In doing so, the thesis proposes a comprehensive framework designed to facilitate objective comparison. The study extends its inquiry into the differential performance of text and speech modalities, in isolation and combination, within the context of depression detection. Moreover, this research delves into the importance of topical relevance in the detection process, culminating in an evaluative discussion of crucial themes integral to accurate depression detection. Ultimately, this thesis contributes to the deepening understanding of the complex interplay between Transformer models, modality use, and topic importance in the realm of depression detection

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Peer reviewed: TrueObjectiveEarly detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.MethodData were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic “F” fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.ResultsMean logit algorithm scores were significantly different across groups in the testing sample (p &amp;lt; 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively.ConclusionFindings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.</jats:sec