Accurate time prediction of patients' critical events is crucial in urgent scenarios where timely decision-making is important. Though many studies have proposed automatic prediction methods using Electronic Healt...
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ISBN:
(数字)9783031395390
ISBN:
(纸本)9783031395383;9783031395390
Accurate time prediction of patients' critical events is crucial in urgent scenarios where timely decision-making is important. Though many studies have proposed automatic prediction methods using Electronic Health Records (EHR), their coarse-grained time resolutions limit their practical usage in urgent environments such as the emergency department (ED) and intensive care unit (ICU). Therefore, in this study, we propose an hourly prediction method based on self-supervised predictive coding and multi-modal fusion for two critical tasks: mortality and vasopressor need prediction. Through extensive experiments, we prove significant performance gains from both multi-modal fusion and selfsupervised predictive regularization, most notably in far-future prediction, which becomes especially important in practice. Our uni-modal/bi-modal/bi-modal selfsupervision scored 0.846/0.877/0.897 (0.824/0.855/0.886) and 0.817/0.820/0.858 (0.807/0.81/0.855) with mortality (far-future mortality) and with vasopressor need (far-future vasopressor need) prediction data in AUROC, respectively.
The co-occurrence of psychotic and autism spectrum disorder (ASD) symptoms represents an important clinical challenge. Here we consider this problem in the context of a computational psychiatry approach that has been ...
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The co-occurrence of psychotic and autism spectrum disorder (ASD) symptoms represents an important clinical challenge. Here we consider this problem in the context of a computational psychiatry approach that has been applied to both conditions-predictive coding. Some symptoms of schizophrenia have been explained in terms of a failure of top-down predictions or an enhanced weighting of bottom-up prediction errors. Likewise, autism has been explained in terms of similar perturbations. We suggest that this theoretical overlap may explain overlapping symptomatology. Experimental evidence highlights meaningful distinctions and consistencies between these disorders. We hypothesize individuals with ASD may experience some degree of delusions without the presence of any additional impairment, but that hallucinations are likely indicative of a distinct process.
If beliefs and desires affect perception at least in certain specified ways then cognitive penetration occurs. Whether it occurs is a matter of controversy. Recently, some proponents of the predictive coding account o...
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If beliefs and desires affect perception at least in certain specified ways then cognitive penetration occurs. Whether it occurs is a matter of controversy. Recently, some proponents of the predictive coding account of perception have claimed that the account entails that cognitive penetrations occurs. I argue that the relationship between the predictive coding account and cognitive penetration is dependent on both the specific form of the predictive coding account and the specific form of cognitive penetration. In so doing, I spell out different forms of each and the relationship that holds between them. Thus, mere acceptance of the predictive coding approach to perception does not determine whether one should think that cognitive penetration exists. Moreover, given that there are such different conceptions of both predictive coding and cognitive penetration, researchers should cease talking of either without making clear which form they refer to, if they aspire to make true generalisations. (C) 2016 The Author. Published by Elsevier Inc.
predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient huma...
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ISBN:
(纸本)9781467390057
predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review. Previously, the sole front-end input used to create a predictive model was the exemplar documents (training data) chosen by subject-matter experts. Many predictive coding tools require users to rely on static preprocessing parameters and a single machine learning algorithm to develop the predictive model. Little research has been published discussing the impact preprocessing parameters and learning algorithms have on the effectiveness of the technology. A deeper dive into the generation of a predictive model shows that the settings and algorithm can have a strong effect on the accuracy and efficacy of a predictive coding tool. Understanding how these input parameters affect the output will empower legal teams with the information they need to implement predictive coding as efficiently and effectively as possible. This paper outlines different preprocessing parameters and algorithms as applied to multiple real-world data sets to understand the influence of various approaches.
In this paper we present a method for multi-lead ECG signal compression using predictive coding combined with Set Partitioning In Hierarchical Trees (SPllIT). We utilize linear prediction between the beats to exploit ...
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ISBN:
(纸本)9781509003631
In this paper we present a method for multi-lead ECG signal compression using predictive coding combined with Set Partitioning In Hierarchical Trees (SPllIT). We utilize linear prediction between the beats to exploit the high correlation among those beats. This method can optimize the redundancy between adjacent samples and adjacent beats. predictive coding is the next step after beat reordering step. The purpose of using predictive coding is to minimize amplitude variance of 2D ECG array so the compression error can be minimize. The experiments from selected records from MIT-UllI arrhythmia database shows that the proposed method is more efficient for ECG signal compression compared with original SPllIT and relatively have lower distortion with the same compression ratios compared to the other wavelet transformation techniques.
As a phenomenon in dynamical systems allowing autonomous switching between stable behaviors, chaotic itinerancy has gained interest in neurorobotics research. In this study, we draw a connection between this phenomeno...
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ISBN:
(纸本)9783030863623;9783030863616
As a phenomenon in dynamical systems allowing autonomous switching between stable behaviors, chaotic itinerancy has gained interest in neurorobotics research. In this study, we draw a connection between this phenomenon and the predictive coding theory by showing how a recurrent neural network implementing predictive coding can generate neural trajectories similar to chaotic itinerancy in the presence of input noise. We propose two scenarios generating random and past-independent attractor switching trajectories using our model.
The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted s...
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ISBN:
(纸本)9783319700908;9783319700892
The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder.
In today's legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analyze large quantities of data. When...
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ISBN:
(纸本)9781538650356
In today's legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analyze large quantities of data. When documents are staged for review - where they are typically assessed for relevancy or privilege - the process can require companies to dedicate an extraordinary level of resources, both with respect to human resources, but also with respect to the use of technology-based techniques to intelligently sift through data. Companies regularly spend millions of dollars producing 'responsive' electronically-stored documents for these types of matters. For several years, attorneys have been using a variety of tools to conduct this exercise, and most recently, they are accepting the use of machine learning techniques like text classification (referred to as predictive coding in the legal industry) to efficiently cull massive volumes of data to identify responsive documents for use in these matters. In recent years, a group of AI and Machine Learning researchers have been actively researching Explainable AI. In an explainable AI system, actions or decisions are human understandable. In typical legal 'document review' scenarios, a document can be identified as responsive, as long as one or more of the text snippets (small passages of text) in a document are deemed responsive. In these scenarios, if predictive coding can be used to locate these responsive snippets, then attorneys could easily evaluate the model's document classification decision. When deployed with defined and explainable results, predictive coding can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. The authors of this paper propose the concept of explainable predictive coding and simple expla
In this paper the feasibility of implementing Cellular Neural Networks (CNN) for image predictive coding is investigated. Various CNN structures as predictors are proposed. The performances are compared to the existin...
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ISBN:
(纸本)9783319086729;9783319086712
In this paper the feasibility of implementing Cellular Neural Networks (CNN) for image predictive coding is investigated. Various CNN structures as predictors are proposed. The performances are compared to the existing predictive coding methods. Thanks to their massive parallel nature, CNN have been proven well suitable for image predictive coding application.
In view of the information security questions, the information hiding technology already becomes the hot spot in the research field. On the basis of the predictive coding, a reversible information hiding algorithm is ...
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ISBN:
(纸本)9780769536798
In view of the information security questions, the information hiding technology already becomes the hot spot in the research field. On the basis of the predictive coding, a reversible information hiding algorithm is proposed. In the algorithm, the carrier image is divided into embedding separations and non-embedding separations of secret information to restrain the error diffusion which possibly happens when decoding. This makes the original carrier image can be restored absolutely when extracting the secret information. As a result of the experiments on the carrier image of different sizes, the algorithm has achieved large information hiding capacity of 0.953 bits/Byte and PSNR of 49.184dB. And the validity of this algorithm is proved.
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