Künstliche Intelligenz für die Medizin

Article for general public (2019)

Innovative developments in Artificial Intelligence (AI), Data Science and Computer Engineering have led to far-reaching consequences for many areas of economic and social life, including medicine and ... [more ▼]

Innovative developments in Artificial Intelligence (AI), Data Science and Computer Engineering have led to far-reaching consequences for many areas of economic and social life, including medicine and healthcare. This article presents some scientific innovations, but at the same time pleads for the inclusion of important factors such as explainability, ethics, comprehensibility and others. [less ▲]

Looking into the Past: Evaluating the Effect of Time Gaps in a Personalized Sentiment Model

in ACM/SIGAPP Symposium On Applied Computing, Limassol 8-12 April 2019 (2019, April)

This paper concerns personalized sentiment analysis, which aims at improving the prediction of the sentiment expressed in a piece of text by considering individualities. Mostly, this is done by relating ... [more ▼]

This paper concerns personalized sentiment analysis, which aims at improving the prediction of the sentiment expressed in a piece of text by considering individualities. Mostly, this is done by relating to a person’s past expressions (or opinions), however the time gaps between the messages are not considered in the existing works. We argue that the opinion at a specific time point is affected more by recent opinions that contain related content than the earlier or unrelated ones, thus a sentiment model ought to include such information in the analysis. By using a recurrent neural network with an attention layer as a basic model, we introduce three cases to integrate time gaps in the model. Evaluated on Twitter data with frequent users, we have found that the performance is improved the most by including the time information in the Hawkes process, and it is also more effective to add the time information in the attention layer than at the input. [less ▲]

The LuNa Open Toolbox for the Luxembourgish Language

in Perner, Petra (Ed.) Advances in Data Mining, Applications and Theoretical Aspects, Poster Proceedings 2019 (2019)

Despite some recent work, the ongoing research for the processing of Luxembourgish is still largely in its infancy. While a rich variety of linguistic processing tools exist, especially for English, these ... [more ▼]

Despite some recent work, the ongoing research for the processing of Luxembourgish is still largely in its infancy. While a rich variety of linguistic processing tools exist, especially for English, these software tools offer little scope for the Luxembourgish language. LuNa (a Tool for Luxembourgish National Corpus) is an Open Toolbox that allows researchers to annotate a text corpus written in Luxembourgish language and to build/query an annotated corpus. The aim of the paper is to demonstrate the components of the system and its usage for Machine Learning applications like Topic Modelling and Sentiment Detection. Overall, LuNa bases on a XML-database to store the data and to define the XML scheme, it offers a Graphical User Interface (GUI) for a linguistic data preparation such as tokenization, Part-Of-Speech tagging, and morphological analysis -- just to name a few. [less ▲]

A Temporal Warehouse for Modern Luxembourgish Text Collections

Presentation (2019)

A Bilingual Study for Personalized Sentiment Model PERSEUS

Scientific Conference (2018, September 10)

This paper investigates the significance of analyzing language preferences in personalized sentiment analysis. Motivated by the considerable amount of text generated by multilingual speakers on social ... [more ▼]

This paper investigates the significance of analyzing language preferences in personalized sentiment analysis. Motivated by the considerable amount of text generated by multilingual speakers on social platforms, we focus on constructing a single model that is able to analyze sentiments in a multilingual environment. In particular, Twitter texts are used in this research where the choice of language can be switched at a message-, sentence-, word- or topic-level. To represent and analyze the text, we extract concepts and main topics from the text and apply a recurrent neural network with attention mechanism in order to learn the relation between the lexical choices and the opinions of each sentiment holder. The personalized sentiment model PERSEUS is applied as the central structure of this research. Moreover, a language index is added to each concept to enable multilingual analysis, which provides a solution for analyzing code-switching in the text as well. In this work, English and German are chosen for a pilot study, and an artificial corpus is created to evaluate the situation with multilingual speakers. [less ▲]

Mind and Language. AI in an Example of Similar Patterns of Luxembourgish Language

in International Conference on Artificial Intelligence Humanities, Book of Abstracts (2018, August 16)

Psychological, cognitive factors and contextual influences in pain and pain-related suffering as revealed by a combined qualitative and quantitative assessment approach

in PLoS ONE (2018)

Previous psychophysiological research suggests that pain measurement needs to go beyond the assessment of Pain Intensity and Unpleasantness by adding the evaluation of Pain-Related Suffering. Based on ... [more ▼]

Previous psychophysiological research suggests that pain measurement needs to go beyond the assessment of Pain Intensity and Unpleasantness by adding the evaluation of Pain-Related Suffering. Based on this three-dimensional approach, we attempted to elucidate who is more likely to suffer by identifying reasons that may lead individuals to report Pain and Pain-Related Suffering more than others. A sample of 24 healthy participants (age range 18±33) underwent four different sessions involving the evaluation of experimentally induced phasic and tonic pain. We applied two decision tree models to identify variables (selected from psychological questionnaires regarding pain and descriptors from post-session interviews) that provided a qualitative characterization of the degrees of Pain Intensity, Unpleasantness and Suffering and assessed the respective impact of contextual influences. The overall classification accuracy of the decision trees was 75% for Intensity, 77% for Unpleasantness and 78% for Pain-Related Suffering. The reporting of suffering was predominantly associated with fear of pain and active cognitive coping strategies, pain intensity with bodily competence conveying strength and resistance and unpleasantness with the degree of fear of pain and catastrophizing. These results indicate that the appraisal of the three pain dimensions was largely determined by stable psychological constructs. They also suggest that individuals manifesting higher active coping strategies may suffer less despite enhanced pain and those who fear pain may suffer even under low pain. The second decision tree model revealed that suffering did not depend on pain alone, but that the complex rating-related decision making can be shifted by situational factors (context, emotional and cognitive). The impact of coping and fear of pain on individual Pain-Related Suffering may highlight the importance of improving cognitive coping strategies in clinical settings. [less ▲]

PERSEUS: A Personalization Framework for Sentiment Categorization with Recurrent Neural Network

in International Conference on Agents and Artificial Intelligence , Funchal 16-18 January 2018 (2018, January)

This paper introduces the personalization framework PERSEUS in order to investigate the impact of individuality in sentiment categorization by looking into the past. The existence of diversity between ... [more ▼]

This paper introduces the personalization framework PERSEUS in order to investigate the impact of individuality in sentiment categorization by looking into the past. The existence of diversity between individuals and certain consistency in each individual is the cornerstone of the framework. We focus on relations between documents for user-sensitive predictions. Individual’s lexical choices act as indicators for individuality, thus we use a concept-based system which utilizes neural networks to embed concepts and associated topics in text. Furthermore, a recurrent neural network is used to memorize the history of user’s opinions, to discover user-topic dependence, and to detect implicit relations between users. PERSEUS also offers a solution for data sparsity. At the first stage, we show the benefit of inquiring a user-specified system. Improvements in performance experimented on a combined Twitter dataset are shown over generalized models. PERSEUS can be used in addition to such generalized systems to enhance the understanding of user’s opinions. [less ▲]

Embedding of the Personalized Sentiment Engine PERSEUS in an Artificial Companion

in International Conference on Companion Technology, Ulm 11-13 September 2017 (2017, September)

The term Artificial Companion has originally been introduced by Y. Wilks [1] as “...an intelligent and helpful cognitive agent, which appears to know its owner and their habits, chats to them and diverts ... [more ▼]

The term Artificial Companion has originally been introduced by Y. Wilks [1] as “...an intelligent and helpful cognitive agent, which appears to know its owner and their habits, chats to them and diverts them, assists them with simple tasks. . . ”. To serve the users’ interests by considering a personal knowledge is, furthermore, demanded. The following position paper takes this request as motivation for the embedding of the PERSEUS system, which is a personalized sentiment framework based on a Deep Learning approach. We discuss how such an embedding with a group of users should be realized and why the utilization of PERSEUS is beneficial. [less ▲]

Q&A with Data Scientists: Christopher Schommer

in Operational Database Management Systems (2017)

Interviews with Data Scientists; see: http://www.odbms.org/2017/01/qa-with-data-scientists-christopher-schommer/