Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve ...
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Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve a large-scale classification problem, as there are thousands of possible codes. In this working notes paper, we present our system and the results of the CLEF 2018 eHealth Evaluation Task 1 on Multilingual Information Extraction-ICD10 coding. This benchmark addresses information extraction in written text with focus on several languages, specifically Hungarian, Italian and French. The goal is to automatically assign ICD10 codes to diagnostic terms of death certificates. The problem can be cast in different ways, for example as a multilabel classification task or as sequence-to-sequence prediction. Our proposal follows this last approach, with promising results, well above the average results for the task. It only relies on the material provided by the task organizers, allowing the application of the same system to all datasets.
Indoor location at room level plays a key role for providing useful services for Ambient Assisted Living (AAL) applications. Wi-Fi fingerprinting indoor location methods are extensively used due to the widespread avai...
ISBN:
(数字)9781728129341
ISBN:
(纸本)9781728129358
Indoor location at room level plays a key role for providing useful services for Ambient Assisted Living (AAL) applications. Wi-Fi fingerprinting indoor location methods are extensively used due to the widespread availability of Wi-Fi infrastructures. A main drawback of Wi-Fi fingerprinting methods is the temporal cost involved in creating the radio maps. Crowdsourcing strategies have been presented as a way to minimize the cost of radio map creation. In this work, we present an extensive study of the issues involved when using crowdsourcing strategies for that purpose. Results provided by extensive experiments performed in a real scenario by three users during two weeks are presented. The main conclusions are: i) crowdsourcing data improves accuracy location in most studied cases; ii) accuracy of Wi-Fi fingerprinting methods decay along time; iii) device diversity is an important issue even when using the same device model.
This paper presents a computational model of social attitude for virtual agents. In our work, the agent acts as a virtual recruiter and interacts with a user during job inter-view training. Training sessions have a pr...
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ISBN:
(纸本)9781634391313
This paper presents a computational model of social attitude for virtual agents. In our work, the agent acts as a virtual recruiter and interacts with a user during job inter-view training. Training sessions have a predefined level of difficulty, which is used along with the perceived user's anxiety at each speaking turn to compute the objectives of the recruiter, namely to challenge or comfort the user. Given an objective, the recruiter chooses how to conduct the interview (i.e. the complexity of its questions), and which social attitudes to express toward the user. Social attitudes are defined along 2 dimensions, dominance and liking. Our model computes both the verbal and nonverbal behaviors of the virtual agent to express a given social attitude. A study on the perception of the attitude of the virtual recruiter endowed with our model has been conducted. We show how the different verbal and non-verbal behaviors defined to either challenge or comfort human interviewees enable the virtual recruiter to successfully convey social attitudes.
The increasing complexity of smart phones makes them more susceptible to accidental failures. However, there is still little understanding on the dependability behavior of modern smart phones. In this paper, we propos...
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The increasing complexity of smart phones makes them more susceptible to accidental failures. However, there is still little understanding on the dependability behavior of modern smart phones. In this paper, we propos...
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ISBN:
(纸本)9781479901807
The increasing complexity of smart phones makes them more susceptible to accidental failures. However, there is still little understanding on the dependability behavior of modern smart phones. In this paper, we propose the design and implementation of a logger to collect relevant failure data from iOS devices, such as iPhone and iPad. The preliminary use of the logger on real-world devices shows that it is able to collect meaningful failure data and to provide interesting insight on the dependability behavior of iOS.
Developing interactive applications according to quality criteria can be done within the environment of a methodology centred on the user and by means of the agile but ordered realisation of its components. The analys...
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A key aspect for the development of CSCW systems is the previous study of the social organization of the members that participate in the collaborative process. Organizations have static and dynamic aspects that are re...
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We describe a method to automatically discover translation collocations from a bilingual corpus and how these improve a machine translation system. The process of inference of collocations is iterative: An alignment i...
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ISBN:
(纸本)9781586034528
We describe a method to automatically discover translation collocations from a bilingual corpus and how these improve a machine translation system. The process of inference of collocations is iterative: An alignment is used to derive an initial set of collocations, these are used in turn to improve the alignment and this new alignment is used to generate new collocations. This process is repeated until no more collocations are found. The final alignment and the set of collocations are used to train a translation model. We use a model that is based on finite state transducers and word clusters and has been modified to work with collocations in addition to single words. We present experiments in which we show that automatic collocations improve translation quality without prior linguistic information.
To reduce speech recognition error rate we can use better statistical language models. These models can be improved by grouping words into word equivalence classes. Clustering algorithms can be used to automatically d...
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To reduce speech recognition error rate we can use better statistical language models. These models can be improved by grouping words into word equivalence classes. Clustering algorithms can be used to automatically do this word grouping. We present an incremental clustering algorithm and two iterative clustering algorithms. Also, we compare them with previous algorithms. The experimental results show that the two iterative algorithms perform as well as previous ones. It should be pointed out that one of them, that uses the leaving one out technique, has the ability to automatically determine the optimum number of classes. These iterative algorithms are used by the incremental one. On the other hand, the proposed incremental algorithm achieves the best results of the compared algorithms, its behavior is the most regular with the variation of the number of classes and can automatically determine the optimum number of classes.
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