Identification of clinical subphenotypes of sepsis after laparoscopic surgery
Jie YangBo ZhangChaomin HuXiaocong JiangPengfei ShuiJiajie HuangYucai HongHongying NiZhongheng Zhang
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Some patients exhibit septic symptoms following laparoscopic surgery, leading to a poor prognosis. Effective clinical subphenotyping is critical for guiding tailored therapeutic strategies in these cases. By identifying predisposing factors for postoperative sepsis, clinicians can implement targeted interventions, potentially improving outcomes. This study outlines a workflow for the subphenotype methodology in the context of laparoscopic surgery, along with its practical application. This study utilized data routinely available in clinical case systems, enhancing the applicability of our findings. The data included vital signs, such as respiratory rate, and laboratory measures, such as blood sodium levels. The process of categorizing clinical routine data involved technical complexities. A correlation heatmap was used to visually depict the relationships between variables. Ordering points were used to identify the clustering structure and combined with Consensus K clustering methods to determine the optimal categorization. Our study highlighted the intricacies of identifying clinical subphenotypes following laparoscopic surgery, and could thus serve as a valuable resource for clinicians and researchers seeking to explore disease heterogeneity in clinical settings. By simplifying complex methodologies, we aimed to bridge the gap between technical expertise and clinical application, fostering an environment where professional medical knowledge is effectively utilized in subphenotyping research. This tutorial could primarily serve as a guide for beginners. A variety of clustering approaches were explored, and each step in the process contributed to a comprehensive understanding of clinical subphenotypes.Keywords:
Identification
We can categorize our environment into different scene categories (e.g., a kitchen or a highway) within a glance. Object information has been suggested to play a crucial role in this process, and some proposals even claim that recognition of a single object can be sufficient to categorize the scene around it. Here, we tested this claim by having participants categorize real-world scene photographs reduced to a single, cut-out object. We show that single objects can indeed be sufficient for correct scene categorization and that scene category information can be extracted within 50 ms of object presentation. Furthermore, we identify the exact properties that make certain objects diagnostic of scene categories using human ratings and statistical measures derived from labelled image databases. Interestingly, fast scene categorization is best explained by human ratings of estimated frequency and specificity of the presented objects for the target scene category and less so by objective database measures. Taken together, our findings support a central role of object information during fast scene categorization, showing that single objects can be indicative of a scene category if they are assumed to frequently and exclusively occur in a certain environment.
Scene statistics
Presentation (obstetrics)
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Categorization is the act of responding differently to objects or events in separate classes or categories. It is a vitally important skill that allows us to approach friends and escape foes, to find food, and avoid toxins. The scientific study of categorization has a long history. For most of this time, the focus was on the cognitive processes that mediate categorization. Within the past decade, however, considerable attention has shifted to the study of the neural basis of categorization. This chapter reviews that work. It begins with a brief overview of the basal ganglia, which are a collection of subcortical nuclei that are especially important in categorization. It then focuses on initial category learning and considers the neural basis of automatic categorization judgements.
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Chiu (1972) reported that in a categorization task, Chinese children were more likely to categorize objects based on shared relationships, whereas American children were more likely to categorize objects based on similarity. This research examines whether such findings generalize to adults and whether cultural differences would also be observed in the activation of semantic concepts. In Experiment 1, Chinese adults were equally likely to categorize based on relationships and similarity, whereas Western adults were more likely to categorize based on similarity. Analogous differences in response latencies were observed in a timed task that reflected semantic processing in Experiment 2, and to some extent in a slightly different task in Experiment 3, although differences between the two experiments suggest that the nature of the categorization task determines the extent to which cultural differences are observed. Overall, results suggest that differences in categorization styles are associated with differences in semantic activation.
Similarity (geometry)
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Comparison plays an important role in preschoolers learning knowledge and understanding others,but about the formation of preschoolers' categorization as well as the application is still in its infancy,especially that comparison in how to influence preschoolers to form the novel object categorization seems more scarce. In this paper,the findings about categorization formation have been done,and the role of comparison preschoolers' categorization has been explored; and the possilble research issues in the future were discussed to provide inspiration and help.
Concept learning
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Categorization is an interactive process between human’s cognitive activity and nature. The result of categorization forms category. During categorization, the co-action of many factors results in cognitive differences for the same category prototype. Using the theories related to category and categorization to analyze the cognitive differences can find out the reasons for the differences, which mainly contain culture, living environment, scientific development, living experience and age.
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People and other animals are very adept at categorizing stimuli even when many
features cannot be perceived. Many psychological models of categorization, on
the other hand, assume that an entire set of features is known. We present a new
model of categorization, called Categorization by Elimination, that uses as few
features as possible to make an accurate category assignment. This algorithm
demonstrates that it is possible to have a categorization process that is fast
and frugal--using fewer features than other categorization methods--yet still
highly accurate in its judgments. We show that Categorization by Elimination
does as well as human subjects on a multi-feature categorization task, judging
intention from animate motion, and that it does as well as other categorization
algorithms on data sets from machine learning. Specific predictions of the
Categorization by Elimination algorithm, such as the order of cue use during
categorization and the time-course of these decisions, still need to be tested
against human performance.
Feature (linguistics)
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Social category
Representation
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The article looks at idioms as categorization means. On the basis of linguistic analysis of semantic organization of idioms two patterns of idiomatic categorization are argued — general categorization and relevant property based categorization. Cognitive functions of idioms differ with regard to their role as categorization means, idioms can serve different categorization purposes according to two general cognitive processes — static and dynamic — including in a category or considering the given qualities as the reasons for categorization. Moreover, the purpose of categorization was investigated with defining the specificity of the phenomena and its types. The categorization purpose was conceived as different types of information e.g. behavioral expectations or interaction models with the object. The cause-effect relationship between the category and the categorization purpose was claimed.
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Categorization researchers typically present single objects to be categorized. But real-world categorization often involves object recognition within complex scenes. It is unknown how the processes of categorization stand up to visual complexity or why they fail facing it. The authors filled this research gap by blending the categorization and visual-search paradigms into a visual-search and categorization task in which participants searched for members of target categories in complex displays. Participants have enormous difficulty in this task. Despite intensive and ongoing category training, they detect targets at near-chance levels unless displays are extremely simple or target categories extremely focused. These results, discussed from the perspectives of categorization and visual search, might illuminate societally important instances of visual search (e.g., diagnostic medical screening).
Visual Search
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We call for a shift from a content‐oriented approach to a process‐oriented approach to the study of categorization in infancy. Although gains have been made in our understanding of infant categorization by evaluating the categories to which infants respond, further understanding of infant categorization, and how categorization changes with development, requires that we more directly assess infants' category formation. We argue that two directions for future research will continue to enhance our understanding of categorization in infancy (and beyond). First, contextual variations (e.g., the effects of task, stimuli, and other factors) on infants' categorization must be better understood. Second, we must more directly evaluate the kinds of information infants use when forming categories, as well as how their use of such information changes with age, task, and so forth. We argue that these two foci will provide clearer understanding of the origins and early development of categorization.
Child Development
Concept learning
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