65 resultados para Commonsense reasoning
Resumo:
Clinical epidemiology is the most currently used name for a comparatively new branch of medicine covering a certain number of activities related to the practice of clinical medicine, but using epidemiological techniques and methods. Clinical epidemiology has only just begun to be known in Europe, whereas units are being increasingly developed and expanded in North America, particularly within the clinical departments of hospitals. The methods it offers are valid for both practicing physicians and hospital doctors (or those being trained in hospitals) and serve the purpose of promoting a better quality medical service, especially where a more adequate evaluation of the effectiveness of diagnostic methods, therapy and prognosis in medicine is concerned. Clinical epidemiology proposes a methodology of medical reasoning and of decision-making, as well as techniques intended to facilitate the indispensable task of keeping up with advances in medical knowledge.
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There is an increasing awareness that the articulation of forensic science and criminal investigation is critical to the resolution of crimes. However, models and methods to support an effective collaboration between these partners are still poorly expressed or even lacking. Three propositions are borrowed from crime intelligence methods in order to bridge this gap: (a) the general intelligence process, (b) the analyses of investigative problems along principal perspectives: entities and their relationships, time and space, quantitative aspects and (c) visualisation methods as a mode of expression of a problem in these dimensions. Indeed, in a collaborative framework, different kinds of visualisations integrating forensic case data can play a central role for supporting decisions. Among them, link-charts are scrutinised for their abilities to structure and ease the analysis of a case by describing how relevant entities are connected. However, designing an informative chart that does not bias the reasoning process is not straightforward. Using visualisation as a catalyser for a collaborative approach integrating forensic data thus calls for better specifications.
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OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.
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The case of a immunocompromised HIV patient with fever and lymphadenopathy discussed in an anatomo-pathological round. This complex clinical case was used as an opportunity to discuss the broad differential diagnosis of fever in an immunocompromized individual with multiples lymphadenopathies. Clinical reasoning leading to the probable diagnosis based on clinical, biological and radiological informations is not only a difficult task for the speaker but also a rich source of learning opportunities for our medical community.
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The paper follows on from earlier work [Taroni F and Aitken CGG. Probabilistic reasoning in the law, Part 1: assessment of probabilities and explanation of the value of DNA evidence. Science & Justice 1998; 38: 165-177]. Different explanations of the value of DNA evidence were presented to students from two schools of forensic science and to members of fifteen laboratories all around the world. The responses were divided into two groups; those which came from a school or laboratory identified as Bayesian and those which came from a school or laboratory identified as non-Bayesian. The paper analyses these responses using a likelihood approach. This approach is more consistent with a Bayesian analysis than one based on a frequentist approach, as was reported by Taroni F and Aitken CGG. [Probabilistic reasoning in the law, Part 1: assessment of probabilities and explanation of the value of DNA evidence] in Science & Justice 1998.
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PURPOSE: Patients diagnosed with a specific neoplasm tend to have a subsequent excess risk of the same neoplasm. The age incidence of a second neoplasm at the same site is approximately constant with age, and consequently the relative risk is greater at younger age. It is unclear whether such a line of reasoning can be extended from a specific neoplasm to the incidence of all neoplasms in subjects diagnosed with a defined neoplasm. METHODS: We considered the age-specific incidence of all non-hormone-related epithelial neoplasms after a first primary colorectal cancer (n = 9542) in the Vaud Cancer Registry data set. RESULTS: In subjects with a previous colorectal cancer, the incidence rate of all other epithelial non-hormone-related cancers was stable around 800 per 100,000 between age 30 and 60 years, and rose only about twofold to reach 1685 at age 70 to 79 years and 1826 per 100,000 at age 80 years or older. After excluding synchronous cancers, the rise was only about 1.5-fold, that is, from about 700 to 1000. In the general population, the incidence rate of all epithelial non-hormone-related cancers was 29 per 100,000 at age 30 to 39 years, and rose 30-fold to 883 per 100,000 at age 70 to 79 years. Excluding colorectal cancers, the rise of all non-hormone-related cancers was from 360 per 100,000 at age 40 to 49 years to 940 at age 70 to 79 years after colorectal cancer, and from 90 to 636 per 100,000 in the general population (i.e., 2.6- vs. 7.1-fold). CONCLUSIONS: The rise of incidence with age of all epithelial non-hormone-related second cancers after colorectal cancer is much smaller than in the general population. This can possibly be related to the occurrence of a single mutational event in a population of susceptible individuals, although alternative models are plausible within the complexity of the process of carcinogenesis.
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Taking as a starting point the seeming inconsistency of late-medieval romances notoriously 'run wild' (verwildert), this article is concerned with the description of an abstract form of narrative coherence that is based on the notion of the diagrammatic. In a first section, this concept is illustrated in a simplified manner by an analysis of Boccaccio's Decameron based on two levels of spatial structure: that of the autograph Berlin manuscript (Codex Hamilton 90) and that of the recipient's mental visualisation of the relations between the frame and the tales of the work. It is argued that the connectivity of the work as a whole depends on the perception of those two spatial representations of the plot. A second section develops this concept in a more theoretical fashion, drawing on Charles Sanders Peirce's notion of diagrammatic reasoning as a way of perceiving relations through mental and material topological representations. Correspondingly, a view of narrative is proposed that does not depend on the traditional perspective of temporal sequence but emphasizes the spatial structure of literary narrative. It is argued that these conditions form the primary ontological mode of narrative, whereas the temporal development of a story is an aesthetic illusion that has been specifically stimulated by the narrative conventions of approximately the past three centuries and must thus be considered a secondary effect. To conclude, an interpretation in miniature of an aspect of Heinrich von Neustadt's Apollonius von Tyrland that seems to have 'run wild' is undertaken from a diagrammatic perspective.
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The widespread use of digital imaging devices for surveillance (CCTV) and entertainment (e.g., mobile phones, compact cameras) has increased the number of images recorded and opportunities to consider the images as traces or documentation of criminal activity. The forensic science literature focuses almost exclusively on technical issues and evidence assessment [1]. Earlier steps in the investigation phase have been neglected and must be considered. This article is the first comprehensive description of a methodology to event reconstruction using images. This formal methodology was conceptualised from practical experiences and applied to different contexts and case studies to test and refine it. Based on this practical analysis, we propose a systematic approach that includes a preliminary analysis followed by four main steps. These steps form a sequence for which the results from each step rely on the previous step. However, the methodology is not linear, but it is a cyclic, iterative progression for obtaining knowledge about an event. The preliminary analysis is a pre-evaluation phase, wherein potential relevance of images is assessed. In the first step, images are detected and collected as pertinent trace material; the second step involves organising and assessing their quality and informative potential. The third step includes reconstruction using clues about space, time and actions. Finally, in the fourth step, the images are evaluated and selected as evidence. These steps are described and illustrated using practical examples. The paper outlines how images elicit information about persons, objects, space, time and actions throughout the investigation process to reconstruct an event step by step. We emphasise the hypothetico-deductive reasoning framework, which demonstrates the contribution of images to generating, refining or eliminating propositions or hypotheses. This methodology provides a sound basis for extending image use as evidence and, more generally, as clues in investigation and crime reconstruction processes.
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Voting Advice Applications (VAAs) render a valuable platform for tackling one of democracy's central challenges: low voter turnout. Studies indicate that lack of information and cost-benefit considerations cause voters to abstain from voting. VAAs are online voting assistance tools which match own political preferences with those of candidates and parties in elections. By assisting voters in their decision-making process prior to casting their votes, VAAs not only rebut rational choice reasoning against voting but also narrow existing information gaps. In this paper we examine the impact of VAAs on participation and voter turnout. Specifically, we present results on how the Swiss VAA smartvote affected voter turnout in the 2007 federal elections. Our analyses suggest that smartvote does have a mobilizing capacity, especially among young voters who are usually underrepresented at polls. Moreover, the study demonstrates how VAAs such as smartvote do affect citizen's propensity to deal with politics in general.
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The case of a patient with severe pulmonary hypertension whose etiology has remained unknown until an autopsy was performed is discussed in a symposium of pathological anatomy. This case helped to address the diagnostic and therapeutic management of pulmonary hypertension. The broad differential diagnosis of this disease requires a diagnostic strategy to be developped. Clinical reasoning leading to a probable diagnosis based on clinical biological and radiological information is not only a difficult task for the speaker but also a rich source of learning opportunities for our medical community.
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The present study investigates the short- and long-term outcomes of a computer-assisted cognitive remediation (CACR) program in adolescents with psychosis or at high risk. 32 adolescents participated in a blinded 8-week randomized controlled trial of CACR treatment compared to computer games (CG). Clinical and neuropsychological evaluations were undertaken at baseline, at the end of the program and at 6-month. At the end of the program (n = 28), results indicated that visuospatial abilities (Repeatable Battery for the Assessment of Neuropsychological Status, RBANS; P = .005) improved signifi cantly more in the CACR group compared to the CG group. Furthermore, other cognitive functions (RBANS), psychotic symptoms (Positive and Negative Symptom Scale) and psychosocial functioning (Social and Occupational Functioning Assessment Scale) improved signifi cantly, but at similar rates, in the two groups. At long term (n = 22), cognitive abilities did not demonstrated any amelioration in the control group while, in the CACR group, signifi cant long-term improvements in inhibition (Stroop; P = .040) and reasoning (Block Design Test; P = .005) were observed. In addition, symptom severity (Clinical Global Improvement) decreased signifi cantly in the control group (P = .046) and marginally in the CACR group (P = .088). To sum up, CACR can be successfully administered in this population. CACR proved to be effective over and above CG for the most intensively trained cognitive ability. Finally, on the long-term, enhanced reasoning and inhibition abilities, which are necessary to execute higher-order goals or to adapt behavior to the ever-changing environment, were observed in adolescents benefi ting from a CACR.
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Most theories of perception assume a rigid relationship between objects of the physical world and the corresponding mental representations. We show by a priori reasoning that this assumption is not fulfilled. We claim instead that all object-representation correspondences have to be learned. However, we cannot learn to perceive all objects that there are in the world. We arrive at these conclusions by a combinatory analysis of a fictive stimulus world and the way to cope with its complexity, which is perceptual learning. We show that successful perceptual learning requires changes in the representational states of the brain that are not derived directly from the constitution of the physical world. The mind constitutes itself through perceptual learning.
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The Wechsler Intelligence Scale for Children-fourth edition (i.e. WISC-IV) recognizes a four-factor scoring structure in addition to the Full Scale IQ (FSIQ) score: Verbal Comprehension (VCI), Perceptual Reasoning (PRI), Working Memory (WMI), and Processing Speed (PSI) indices. However, several authors suggested that models based on the Cattell-Horn-Carroll (CHC) theory with 5 or 6 factors provided a better fit to the data than does the current four-factor solution. By comparing the current four-factor structure to CHC-based models, this research aimed to investigate the factorial structure and the constructs underlying the WISC-IV subtest scores with French-speaking Swiss children (N = 249). To deal with this goal, confirmatory factor analyses (CFAs) were conducted. Results showed that a CHC-based model with five factors better fitted the French-Swiss data than did the current WISC-IV scoring structure. All together, these results support the hypothesis of the appropriateness of the CHC model with French-speaking children.
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Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.