Enquête sur l'enquête 'Les réseaux économiques souterrains en cité de transit (1981-2010)' de Jean-François Laé et Numa Murard

L’enquête « Les réseaux économiques souterrains en cité de transit » a été réalisée par Jean-François Laé, professeur émérite de l’Université Paris 8 Vincennes – Saint Denis et Numa Murard, professeur émérite de l’Université Paris Diderot. Elle a la particularité d’avoir été menée en deux fois, puisqu’elle a donné lieu à une première enquête réalisée au début des années 1980 puis à un retour sur enquête en 2010. L’origine de cette recherche remonte à l’expérience de Jean-François Laé comme travailleur social dans une cité dite de transit de la ville d’Elbeuf, en Seine-Maritime. Après sa rencontre avec Numa Murard au CERFI (Centre d’études, de recherche et de formation institutionnelle), ils décident tous deux de réaliser cette enquête, ayant obtenu des financements de la CNAF (Caisse nationale des affaires familiales) et du ministère de l’Urbanisme et du Logement. Elle donnera lieu à la rédaction d’un rapport et à la publication d’un ouvrage en 1985, L’Argent des pauvres. Trente ans plus tard, les deux chercheurs décident de revenir sur les terrains de leur première enquête, dans le cadre d’un documentaire radiophonique. Un ouvrage sera publié suite à ce retour, intitulé Deux générations dans la débine et paru en 2012. Pour l’enquête initiale comme pour le retour sur enquête, les deux chercheurs se sont immergés en ethnographes dans la vie quotidienne des habitants de la cité de transit. S’ils se sont principalement focalisés sur la vie économique des enquêtés, ils ont ouvert un ensemble de thématique allant bien au-delà de ce que laisse à penser le titre de l’enquête. Si la méthodologie est particulière, la méthode d’exposition l’est tout autant puisqu’elle ressort de ce que Jean-François Laé et Numa Murard appellent la « sociologie narrative ». Le corpus de documents fourni par les chercheurs a trait aux deux étapes de cette recherche. Il réunit notamment un carnet de terrain et le rapport publié suite à la première enquête, de même que différentes notes préparatoires, des photos et des transcriptions d’enregistrements collectés lors du retour sur enquête. S’il est parcellaire du fait de la perte de certains documents, ce corpus donne une idée précise des méthodes d’enquête des deux chercheurs et ouvre des pistes de réutilisation, notamment dans un cadre pédagogique. Deux entretiens ont été réalisés par l'équipe beQuali avec les auteurs de l'enquête : le premier avec Jean-François Laé,Numa Murard et Fabien Deshayes au CRESPPA, le deuxième avec Jean-François Laé et Numa Murard au CDSP

Improving Cross-Validation Classifier Selection Accuracy through Meta-learning

In order to choose from the large number of classification methods available for use, cross-validation error estimates are often employed. We present this cross-validation selection strategy in the framework of meta-learning and show that conceptually, meta-learning techniques could provide better classifier selections than traditional cross-validation selection. Using various simulation studies we illustrate and discuss this possibility. Through a collection of datasets resembling real-world data, we investigate whether these improvements could possibly exist in the real-world as well. Although the approach presented here currently requires significant investment when applied to practical applications, the concept of being able to outperform cross-validation selection opens the door to new classifier selection strategies.Pattern Recognition LabIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Also for k-means: more data does not imply better performance

Arguably, a desirable feature of a learner is that its performance gets better with an increasing amount of training data, at least in expectation. This issue has received renewed attention in recent years and some curious and surprising findings have been reported on. In essence, these results show that more data does actually not necessarily lead to improved performance—worse even, performance can deteriorate. Clustering, however, has not been subjected to such kind of study up to now. This paper shows that k-means clustering, a ubiquitous technique in machine learning and data mining, suffers from the same lack of so-called monotonicity and can display deterioration in expected performance with increasing training set sizes. Our main, theoretical contributions prove that 1-means clustering is monotonic, while 2-means is not even weakly monotonic, i.e., the occurrence of nonmonotonic behavior persists indefinitely, beyond any training sample size. For larger k, the question remains open.Pattern Recognition and Bioinformatic

Nuclear discrepancy for single-shot batch active learning

Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Instead of selecting randomly what data to annotate, active learning strategies aim to select data so as to get a good predictive model with as little labeled samples as possible. Single-shot batch active learners select all samples to be labeled in a single step, before any labels are observed.We study single-shot active learners that minimize generalization bounds to select a representative sample, such as the maximum mean discrepancy (MMD) active learner.We prove that a related bound, the discrepancy, provides a tighter worst-case bound. We study these bounds probabilistically, which inspires us to introduce a novel bound, the nuclear discrepancy (ND). The ND bound is tighter for the expected loss under optimistic probabilistic assumptions. Our experiments show that the MMD active learner performs better than the discrepancy in terms of the mean squared error, indicating that tighter worst case bounds do not imply better active learning performance. The proposed active learner improves significantly upon the MMD and discrepancy in the realizable setting and a similar trend is observed in the agnostic setting, showing the benefits of a probabilistic approach to active learning. Our study highlights that assumptions underlying generalization bounds can be equally important as bound-tightness, when it comes to active learning performance. Code for reproducing our experimental results can be found at https://github.com/tomviering/ NuclearDiscrepancy.Pattern Recognition and Bioinformatic

The association of comorbidity with Parkinson's disease-related hospitalizations

Introduction: Unplanned hospital admissions associated with Parkinson's disease could be partly attributable to comorbidities. Methods: We studied nationwide claims databases and registries. Persons with newly diagnosed Parkinson's disease were identified based on the first Parkinson's disease-related reimbursement claim by a medical specialist. Comorbidities were classified based on the Charlson Comorbidity Index. We studied hospitalization admissions because of falls, psychiatric diseases, pneumonia and urinary tract infections, PD-related hospitalizations-not otherwise specified. The association between comorbidities and time-to-hospitalization was estimated using Cox proportional hazard modelling. To better understand pathways leading to hospitalizations, we performed multiple analyses on causes for hospitalizations. Results: We identified 18 586 people with newly diagnosed Parkinson's disease. The hazard of hospitalization was increased in persons with peptic ulcer disease (HR 2.20, p = 0.009), chronic obstructive pulmonary disease (HR 1.61, p < 0.001), stroke (HR 1.37, p = 0.002) and peripheral vascular disease (HR 1.31, p = 0.02). In the secondary analyses, the hazard of PD-related hospitalizations-not otherwise specified (HR 3.24, p = 0.02) and pneumonia-related hospitalization (HR 2.90, p = 0.03) was increased for those with comorbid peptic ulcer disease. The hazard of fall-related hospitalization (HR 1.57, p = 0.003) and pneumonia-related hospitalization (HR 2.91, p < 0.001) was increased in persons with chronic obstructive pulmonary disease. The hazard of pneumonia-related hospitalization was increased in those with stroke (HR 1.54, p = 0.03) or peripheral vascular disease (HR 1.60, p = 0.02). The population attributable risk of comorbidity was 8.4%. Conclusion: Several comorbidities increase the risk of Parkinson's disease related-hospitalization indicating a need for intervention strategies targeting these comorbid disorders.Pattern Recognition and Bioinformatic

Complexe neurologische aandoeningen in de langdurige zorg: Een verkenning van aantallen, patiëntkenmerken en indicaties

Veel patiënten met een complexe neurologische aandoening, zoals de ziekte van Parkinson, multiple sclerose of restverschijnselen van niet-aangeboren hersenletsel, doen vroeg of laat in hun ziekteproces een beroep op de langdurige zorg. Dat deze mensen een andere zorgbehoefte hebben dan de algemene verpleeghuispopulatie is bekend, maar van hun aantallen bestaan slechts schattingen. Dit onderzoek, mogelijk gemaakt door de Verenso Beurs voor wetenschappelijk onderzoek, heeft als doel te achterhalen hoeveel mensen in Nederland een indicatie voor langdurige zorg hebben én een diagnose die wijst op een ernstige aandoening van het zenuwstelsel, anders dan dementie. We combineerden grote datasets van het Centraal Bureau voor Statistiek om zo de prevalentie van een aantal complexe neurologische aandoeningen onder mensen met een indicatie voor langdurige zorg in een verpleeghuis te bepalen. Op 31-12-2015 hadden 121.749 mensen in Nederland een indicatie die toegang geeft tot langdurige zorg in een verpleeghuis. Van hen hebben 9.398 mensen een complexe neurologische aandoening. De ziekte van Parkinson en aanverwante aandoeningen komen bij 4% van de totale langdurige-zorgpopulatie voor. Patiënten met een zeldzamere aandoening zijn 10 tot 20 jaar jonger dan de gemiddelde verpleeghuispatiënt. Er zijn in de langdurige zorg meer mensen met een complexe neurologische aandoening, anders dan dementie, dan tot nu toe werd aangenomen. Door verdere academisering van de langdurige zorg en de ontwikkeling van doelgroepexpertisecentra moet het mogelijk zijn ook deze patiënten en hun naasten passende zorg te bieden.Pattern Recognition and Bioinformatic

Demystifying machine learning for mortality prediction

Pattern Recognition and Bioinformatic

Estimating the Effect of Early Treatment Initiation in Parkinson's Disease Using Observational Data

Background: Both patients and physicians may choose to delay initiation of dopamine replacement therapy in Parkinson's disease (PD) for various reasons. We used observational data to estimate the effect of earlier treatment in PD. Observational data offer a valuable source of evidence, complementary to controlled trials. Method: We studied the Parkinson's Progression Markers Initiative cohort of patients with de novo PD to estimate the effects of duration of PD treatment during the first 2 years of follow-up, exploiting natural interindividual variation in the time to start first treatment. We estimated the Movement Disorder Society–Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III (primary outcome) and several functionally relevant outcomes at 2, 3, and 4 years after baseline. To adjust for time-varying confounding, we used marginal structural models with inverse probability of treatment weighting and the parametric g-formula. Results: We included 302 patients from the Parkinson's Progression Markers Initiative cohort. There was a small improvement in MDS-UPDRS Part III scores after 2 years of follow-up for patients who started treatment earlier, and similar, but nonstatistically significant, differences in subsequent years. We found no statistically significant differences in most secondary outcomes, including the presence of motor fluctuations, nonmotor symptoms, MDS-UPDRS Part II scores, and the Schwab and England Activities of Daily Living Scale. Conclusion: Earlier treatment initiation does not lead to worse MDS-UPDRS motor scores and may offer small improvements. These findings, based on observational data, are in line with earlier findings from clinical trials. Observational data, when combined with appropriate causal methods, are a valuable source of additional evidence to support real-world clinical decisions.Pattern Recognition and Bioinformatic

The future of artificial intelligence in intensive care: moving from predictive to actionable AI

Artificial intelligence (AI) research in the intensive care unit (ICU) mainly focuses on developing models (from linear regression to deep learning) to predict out-comes, such as mortality or sepsis [1, 2]. However, there is another important aspect of AI that is typically not framed as AI (although it may be more worthy of the name), which is the prediction of patient outcomes or events that would result from different actions, known as causal inference [3, 4]. This aspect of AI is crucial for decision-making in the ICU. To emphasize the impor- tance of causal inference, we propose to refer to any data- driven model used for causal inference tasks as ‘action- able AI’, as opposed to ‘predictive AI’, and discuss how these models could provide meaningful decision support in the ICU.Pattern Recognition and BioinformaticsBiomechanical Engineerin

Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice

This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide recommendations to improve the future landscape of research practices in this domain. To achieve this, we searched various databases including Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, bioRxiv, arXiv, and the ACM Digital Library. Studies involving models for causal inference addressing time-varying treatments in the adult ICU were reviewed. Data extraction encompassed the study settings and methodologies applied. Furthermore, we assessed reporting quality of target trial components (i.e., eligibility criteria, treatment strategies, follow-up period, outcome, and analysis plan) and main causal assumptions (i.e., conditional exchangeability, positivity, and consistency). Among the 2184 titles screened, 79 studies met the inclusion criteria. The methodologies used were G methods (61%) and reinforcement learning methods (39%). Studies considered both static (51%) and dynamic treatment regimes (49%). Only 30 (38%) of the studies reported all five target trial components, and only seven (9%) studies mentioned all three causal assumptions. To achieve actionable AI in the ICU, we advocate careful consideration of the causal question of interest, describing this research question as a target trial emulation, usage of appropriate causal inference methods, and acknowledgement (and examination of potential violations of) the causal assumptions.Pattern Recognition and Bioinformatic