Emotional analysis prediction using qualitatively representations of multiple psycho-physiological time-series signals

Treball de fi de grau en Bioinformàtica. Curs 2018-2019Tutors: Mireia Olivella, Beatriz LópezPredicció d’emocions fent servir senyals fisiològiques. Metodologia que busca transformar les seqüències temporals a representacions qualitatives, reduint la complexitat, per minar des d’un punt de vista estructural i de característiques les senyals; fent servir tècniques adoptades de la bioinformàtica i la mineria de text. La metodologia reuneix múltiples senyals simultàniament i realitza diversos procediments de classificació per a un anàlisis de consens posterior, buscant la millor predicció.Predicción de emociones usando señales fisiológicas. Metodología que busca transformar las secuencias temporales a representaciones cualitativas, reduciendo la complejidad, para minar desde un punto de vista estructural y de características, las señales; usando técnicas adoptadas de la microinformática y la minería de texto. La metodología reúne múltiples señales simultáneamente y realiza diversos procedimientos de clasificación para un análisis de consenso posterior, aspirando a la mejor predicción.Emotion prediction using physiological signals. Method pipeline that transform time-series to qualitative representations, reducing the complexity, to mine from a structural and feature-driven point of view using adopted techniques from bioinformatics and text mining tools. Method gathers diverse signals to perform multiple classification procedure simultaneously for a posterior consensus analysis, aiming the best prediction

Diagnosis analysis through graph decomposition and association rules in the context of Covid-19

Rule miners are unsupervised learning methods used to detect associations between items. These algorithms have been traditionally used in transactional datasets to synthesise significance associations between items. Extrapolating this behaviour to EHR data, the algorithms should be able to detect associations between diagnoses in a certain segment of the population, therefore suggesting relations of conditions prone to interest by the medical community. This thesis provides an evaluation of a proposal of a rule mining algorithm to detect associations of diagnostses in medical trajectory databases of patients. The approach uses the notion of redundancy to solve the main issues of output size and validity traditionally suffered by rule miners by finding only the non-redundant significant associations. The yacaree program is able to use this approach reducing at the minimum level the needing of expertise by the end user. This thesis evaluates the validity of this technique in a high demanding medical dataset with respect to other rule miner approaches. The procedure aims to state an initial proposal for mining EHR databases to detect between and within associations of diagnoses in segments of patients based on confounding factors age ans sex, with promising results. By imposing high-demanding thresholds the procedure is able to retrieve associations of diagnoses that although being evident suggest correctness of the approach. By softening the thresholds, one should be able to detect non-obvious associations prone to research. The method is tested in a database of visits during the covid-19 outbreak period to bring to light possible associations with the pandemic. Using network visualizations, the ultimatre goal is making a primal formulation of a tool that can be easily interpreted by the medical community. Two final research proposals are adressed. First the suggestion of a basic algorithm to detect morbidity groups of diagnoses. Second, the detection of directionality between diagnoses in rules to improve the visualization and suggest temporality, which turns to be very interesting from the medical perspective

A cost-benefit analysis of the COVID-19 asymptomatic mass testing strategy in the north metropolitan area of Barcelona

Background: The epidemiological situation generated by COVID-19 has highlighted the importance of applying non-pharmacological measures in the management of the epidemic. Mass screening of the asymptomatic general population has been established as a priority strategy by carrying out diagnostic tests to detect possible cases, isolate contacts, cut transmission chains and thus limit the spread of the virus. Objective: To evaluate the economic impact of mass COVID-19 screenings of an asymptomatic population during the first and second wave of the epidemic in Catalonia, Spain. Methodology: Cost-Benefit Analysis based on the estimated total costs of mass screening versus health gains and associated health costs avoided. Results: Excluding the value of monetized health, the Benefit-Cost ratio was estimated at 0.45, a low value that would seem to advise against mass screening policies. However, if monetized health is included, the ratio is close to 1.20, reversing the interpretation. In other words, the monetization of health is the critical element that tips the scales in favour of the desirability of screening. Results show that the interventions with the highest return are those that maximize the percentage of positives detected. Conclusion: Efficient management of resources for the policy of mass screening in asymptomatic populations can generate high social returns. The positivity rate critically determines its desirability. Likewise, precocity in the detection of cases will cut more transmissions in the chain of contagion and increase the economic return of these interventions. Maximizing the value of resources depends on screening strategies being accompanied by contact-tracing and specific in their focus, targeting, for example, high-risk subpopulations with the highest rate of expected positives.Postprint (published version