Objectif: Explorer, adapter et développer de nouvelles méthodologies permettant de réaliser des revues systématiques et méta-analyses en chirurgie cardiaque. Méthodes: Le text mining et la citation chasing ont été utilisés pour l’optimisation de l’efficience et de la sensibilité de la recherche. Nous avons participé à l’évaluation des nouveaux outils (Risk of Bias 2.0 et Risk of bias in non-randomized studies of interventions) pour l’évaluation de la qualité des études randomisées et non randomisées et qui ont été adoptés pour nos projets futurs. Une nouvelle méthodologie graphique a été développée pour la réalisation des méta-analyses de données de survie. Résultats: Ces approches ont été utilisées pour répondre à diverses questions de recherche touchants différents aspects de la chirurgie cardiaque : 1) la rédaction des premières lignes directrices de l’Enhanced Recovery After Cardiac Surgery, 2) une revue systématique des résultats de la chirurgie valvulaire et aortique chez le transplanté cardiaque, démontrant les bons résultats de ces procédures dans une population à haut risque et l’émergence des techniques trans-cathéters dans la prise en charge de ces pathologies, 3) une méta-analyse portant sur les arythmies supra-ventriculaires chez les patients ayant eu une intervention de Fontan, concluant à un effet bénéfique de la technique du conduit extra-cardiaque et 4) une méta-analyse portant sur l’insuffisance aortique chez les patients porteurs d’assistance ventriculaire gauche, objectivant une incidence sous-estimée de cette situation clinique avec un impact significatif sur la survie de cette population de patients. Conclusion: Cette thèse aborde certaines contraintes de la littérature en chirurgie cardiaque comme la sensibilité sous optimale de la recherche systématique et les méta-analyses de données de survie, et a proposé des solutions. D’autres contraintes telles que les comparaisons multiples subsistent. Des recherches futures axées sur de nouvelles approches comme le network meta-analysis ou l’approche bayésienne pourraient offrir des solutions. / Objective: To explore, adapt and develop new methodologies for performing systematic reviews and meta-analyses in cardiac surgery. Methods: Text mining and citation chasing were used to optimize the efficiency and sensitivity of search process. We participated in the evaluation of new tools (Risk of Bias 2.0 and Risk of bias in non-randomized studies of interventions) for quality assessment of randomized and nonrandomized studies and we have adopted them for our future projects. A new graphic methodology has been developped for the performance of meta-analyses of time-to-event data. Results: These approaches have been used to answer various research questions touching different aspects of cardiac surgery: 1) writing the first guidelines of enhanced recovery after cardiac surgery, 2) a systematic review of the results of valvular surgery and aortic in cardiac transplantation, demonstrating good results of these procedures in a high-risk population and the emergence of trans-catheter techniques in the management of these pathologies, 3) a meta-analysis of supra-ventricular arrhythmias in patients who had a Fontan intervention, finding a beneficial effects of the extracardiac conduct technique and 4) a meta-analysis of aortic insufficiency in patients with left ventricular assist device, showing an under-estimated incidence of this clinical entity with a significant impact on the survival of this population of patients. Conclusion: This thesis addresses some of the short comings of the heart surgery literature such as the sensitivity of the systematic search and time-to-event data meta-anlysis and proposed novel solutions. Other issues such as the need to summarize a comprehensive and coherent set of comparisons remain. Future researchs focused on new approaches such as the network meta-analysis or the Bayesian approach can solve these issues.

<p><br></p> <p>Police use of force has become a topic of significant concern, particularly given the disparate impact on communities of color. Research has shown that police officer involved shootings, misconduct and excessive use of force complaints exhibit network effects, where officers are at greater risk of being involved in these incidents when they socialize with officers who have a history of use of force and misconduct. Given that use of force and misconduct behavior appear to be transmissible across police networks, we are attempting to address if police networks can be altered to reduce use of force and misconduct events in a limited scope.</p> <p><br></p> <p>In this work, we analyze a novel dataset from the Indianapolis Metropolitan Police Department on officer field training, subsequent use of force, and the role of network effects from field training officers. We construct a network survival model for analyzing time-to-event of use of force incidents involving new police trainees. The model includes network effects of the diffusion of risk from field training officers (FTOs) to trainees. We then introduce a network rewiring algorithm to maximize the expected time to use of force events upon completion of field training. We study several versions of the algorithm, including constraints that encourage demographic diversity of FTOs. The results show that FTO use of force history is the best predictor of trainee's time to use of force in the survival model and rewiring the network can increase the expected time (in days) of a recruit's first use of force incident by 8%. </p> <p>We then discuss the potential benefits and challenges associated with implementing such an algorithm in practice.</p> <p><br></p>

Histogram data are widely used for compressing high-frequency time-series signals due to their ability to capture distributional informa-tion. However, this compression comes at the cost of increased di-mensionality and loss of contextual details from the original features.This study addresses the challenge of effectively capturing changesin distributions over time and their contribution to failure prediction.Specifically, we focus on the task of predicting Time to Event (TTE) forturbocharger failures.In this thesis, we propose a novel approach to improve failure pre-diction by fine-tuning lower-dimensional representations of bi-variatehistograms. The goal is to optimize these representations in a waythat enhances their ability to predict component failure. Moreover, wecompare the performance of our learned representations with hand-crafted histogram features to assess the efficacy of both approaches.We evaluate the different representations using the Weibull Time ToEvent - Recurrent Neural Network (WTTE-RNN) framework, which isa popular choice for TTE prediction tasks. By conducting extensive ex-periments, we demonstrate that the fine-tuning approach yields supe-rior results compared to general lower-dimensional learned features.Notably, our approach achieves performance levels close to state-of-the-art results.This research contributes to the understanding of effective failureprediction from time series histogram data. The findings highlightthe significance of fine-tuning lower-dimensional representations forimproving predictive capabilities in real-world applications. The in-sights gained from this study can potentially impact various indus-tries, where failure prediction is crucial for proactive maintenanceand reliability enhancement.

The purpose of the study was to develop best practice guidelines to prevent permanent hearing loss associated with the management of multi-drug resistant tuberculosis (MDR-TB) through raised awareness and monitoring. The Human Immunodeficiency Virus (HIV) and MDR-TB are global public health problems requiring urgent scale-up of treatment services. Irreversible sensorineural hearing loss (SNHL) is one of the adverse drug reactions of the current World Health Organization (WHO) recommended MDR-TB chemotherapy fuelling another public health problem, that disabling hearing loss, which is the second highest contributor of Years Lived with Disability (YLD) according to the World Health Report (2003). Expansion of MDR-TB treatment threatens to increase incidence of SNHL unless there is urgent implementation of intervention towards preservation of hearing for patients on treatment. This empirical study determined and documented the incidence of SNHL in HIV positive and HIV negative patients on MDR-TB treatment, the risk factors for SNHL, from the time treatment initiation to SNHL. Based on the findings, developed and improved the understanding of best practice guidelines for monitoring and prevention of MDR-TB treatment-related SNHL. The empirical study recruited a cohort of 173 patients with normal hearing status, after diagnosis with MDR-TB and enrolled on MDR-TB therapy over thirteen month period. Patients in the cohort received monthly hearing sensitivity testing during the intensive MDR-TB therapy when injectable aminoglycoside antibiotics are part of the treatment regimen. The three study endpoints included completion of the eight-month intensive treatment phase without developing hearing loss, development incident hearing loss or loss to follow up. Data was analysed using STATA statistical software and summarised using frequencies, means, proportions, and rates. The study documented incidence of SNHL, time to hearing loss and risk factors for hearing loss. Recommendations to prevent and monitor hearing loss are made based on the the study findings. / Health Studies / D. Litt. et Phil. (Health Studies)

Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. Such an abrupt shift of policy has left open the challenge of understanding how those two kinds of features influence the predictive capabilities of models. This thesis aims at assessing the effect of combining static and dynamic features on the most commonly used models in survival analysis. In doing so, we compare the error measurements of such models with dataset composed of purely dynamic features or a combination of static and dynamic ones. Empirical measurements have shown that models respond differently to the addition of static features to the analysis, with more complex, sequential models like the LSTM struggling to deal with the added data complexity (with a 12% increase in error), while non sequential models see reductions of up to 14.7% in error. The thesis also includes a clusterization task aimed at aiding the interpretation of survival analysis outcomes. / Överlevnadsanalys är ett delområde inom statistiken där målet är att analysera och modellera data där utfallet är tiden fram till dess att en händelse av intresse inträffar. På grund av analysens inneboende tidsmässiga karaktär har användningen av mer nyligen utvecklade sekventiella modeller (RNN och LSTM) kombinerats med användningen av dynamiska tidsmässiga egenskaper, i motsats till den tidigare förlitningen på statiska sådana. En sådan drastisk förändring av ansatsen har lämnat öppet för utmaningen att förstå hur dessa två typer av egenskaper påverkar modellernas förutsägande förmåga. Syftet med denna uppsats är att bedöma effekten av att kombinera statiska och dynamiska egenskaper på de vanligaste modellerna för överlevnadsanalys. I detta syfte jämför vi felmätningar av sådana modeller med dataset som består av rent dynamiska egenskaper eller en kombination av statiska och dynamiska egenskaper. Empiriska mätningar har visat att modellerna reagerar olika på tillägget av statiska egenskaper till analysen, där mer komplexa, sekventiella modeller som LSTM kämpar för att hantera den ökade datakomplexiteten (med en ökning av felet med 12 %), medan icke-sekventiella modeller ser en minskning av felet med upp till 14,7 %. Uppsatsen innehåller också en klusteruppgift som syftar till att underlätta tolkningen av resultaten av överlevnadsanalyser. / L’analisi della sopravvivenza è una branca della statistica il cui obiettivo è l’analisi e la modellazione di dati il cui risultato è il tempo che intercorre fino al verificarsi di un evento di interesse. A causa dell’intrinseca natura temporale dell’analisi, l’impiego di modelli sequenziali di più recente sviluppo (RNN e LSTM) è stato abbinato all’uso di attributi temporali dinamici, a differenza dell’uso più diffuso in passato di attributi statici. Questo brusco cambiamento ha lasciato aperta la sfida di capire come questi due tipi di attributi influenzino le capacità predittive dei modelli. Questa tesi si propone di valutare l’effetto della combinazione di attributi statici e dinamici sui modelli più comunemente utilizzati nell’analisi della sopravvivenza. A tal fine, confrontiamo le misure di errore di tali modelli con set di dati composti da attributi puramente dinamici o da una combinazione di statici e dinamici. I risultati empirici hanno mostrato che i modelli rispondono in modo diverso all’aggiunta di attrbiuti statici, con i modelli sequenziali più complessi, come l’LSTM, che faticano a gestire la complessità dei dati aggiunti (con un aumento dell’errore del 12%), mentre i modelli non sequenziali registrano riduzioni dell’errore fino al 14,7%. La tesi comprende anche una clusterizzazione volta a facilitare l’interpretazione dei risultati dell’analisi di sopravvivenza.