200 research outputs found
Entity Linking in Low-Annotation Data Settings
Recent advances in natural language processing have focused on applying and adapting large pretrained language models to specific tasks. These models, such as BERT (Devlin et al., 2019) and BART (Lewis et al., 2020a), are pretrained on massive amounts of unlabeled text across a variety of domains. The impact of these pretrained models is visible in the task of entity linking, where a mention of an entity in unstructured text is matched to the relevant entry in a knowledge base. State-of-the-art linkers, such as Wu et al. (2020) and De Cao et al. (2021), leverage pretrained models as a foundation for their systems. However, these models are also trained on large amounts of annotated data, which is crucial to their performance. Often these large datasets consist of domains that are easily annotated, such as Wikipedia or newswire text. However, tailoring NLP tools to a narrow variety of textual domains severely restricts their use in the real world.
Many other domains, such as medicine or law, do not have large amounts of entity linking annotations available. Entity linking, which serves to bridge the gap between massive unstructured amounts of text and structured repositories of knowledge, is equally crucial in these domains. Yet tools trained on newswire or Wikipedia annotations are unlikely to be well-suited for identifying medical conditions mentioned in clinical notes. As most annotation efforts focus on English, similar challenges can be noted in building systems for non-English text. There is often a relatively small amount of annotated data in these domains. With this being the case, looking to other types of domain-specific data, such as unannotated text or highly-curated structured knowledge bases, is often required. In these settings, it is crucial to translate lessons taken from tools tailored for high-annotation domains into algorithms that are suited for low-annotation domains. This requires both leveraging broader types of data and understanding the unique challenges present in each domain
L’appropriation d’un lecteur de glucose connecté à mesure flash chez les personnes vivant avec un diabète en contexte d’éducation thérapeutique
Cotutelle internationale avec le Laboratoire Éducations et Promotion de la Santé (Santé publique - UR 3412) de l'Université Sorbonne Paris Nord.L'autosurveillance glycémique est essentielle pour les personnes vivant avec un diabète afin d'évaluer leur glycémie et adapter leurs traitements ou comportements. En France, depuis 2017, le glucomètre connecté à mesure flash FreeStyle Libre est proposé aux personnes vivant avec un diabète à la condition de suivre une éducation spécifique au sein de structures coutumières de l’éducation thérapeutique et du diabète. La littérature scientifique a montré l'efficacité de l'autosurveillance avec ce système, mais il existe peu d'études sur son appropriation et son impact. Cette recherche vise à décrire et comprendre le phénomène d’appropriation du FreeStyle Libre en identifiant comment elle s’est déroulée, comment elle s’opérationnalise, selon quelles interventions, chez qui cela fonctionne, dans quels contextes, et quels sont les mécanismes en jeu. Une évaluation réaliste a été menée en se basant sur une théorie de moyenne portée. Cette recherche a été réalisée au sein de quatre terrains en région parisienne auprès de 48 personnes vivant avec un diabète et professionnels de santé. Tout d’abord, les résultats montrent qu’au cours du temps, les programmes ont évolué dans leurs modalités et contenus, dans la façon dont ils s’organisaient, mais aussi que les interventions éducatives réellement mises en œuvre diffèrent au regard des interventions qui sont censées avoir cours. Ensuite, pour expliquer l’appropriation du FreeStyle Libre, 114 chaînes de contexte-mécanismes et effets ont été construites et éclairent sur l’acceptation du FreeStyle Libre, les conditions et modalités d’utilisation et sur les effets produits grâce à celle-ci. Les chaînes de contextes-mécanismes-effets mettent en évidence des contextes plus favorables à l’appropriation (littératie numérique élevée, empowerment préexistant, engagement dans la démarche d’autogestion…) et des contextes moins favorables (trait de personnalité compulsive, littératie générale ou numérique faible, absence d’éducation et d’accompagnement…). Les mécanismes qui sont générés font appel aux connaissances, à l’absence de crainte sur la confidentialité et l’immixtion dans la vie privée, à la motivation, et aux normes personnelles. L’acceptation du FSL est forte et fait intervenir la perception que la technologie peut contribuer à la performance de l’autosurveillance glycémique et qu’elle est facile à utiliser. Ensuite, l’analyse a permis de discriminer plusieurs modalités d’utilisation suivant des indicateurs quantitatifs et qualitatifs de l’usage. Des effets de l’appropriation sont identifiés dans l’amélioration de la qualité de vie dans le diabète, l’amélioration de la relation interpersonnelle entre soignants et personnes soignées, dans la diminution d’une anxiété liée au diabète, dans l’adaptation des traitements et des comportements et enfin dans la connaissance de la maladie et le raisonnement des personnes. La théorie de moyenne portée finale constituée sur la base de ces résultats adresse un modèle global de l’appropriation du FreeStyle Libre. Cette étude montre qu’il existe de nombreuses variations de l’appropriation. Elle situe que l’éducation à l’utilisation du FreeStyle Libre est nécessaire pour en tirer davantage parti et identifie un manque d’intégration de la technologie connectée dans les programmes d’éducation thérapeutique, ce qui constitue un enjeu particulier pour l’avenir.Self-monitoring of blood glucose is essential for people living with diabetes to assess their blood glucose levels and adapt their treatment or behaviour. In France, since 2017, the FreeStyle Libre (FSL) flash glucose meter has been offered to people living with diabetes on the condition that they attend a specific education program within facilities accustomed to diabetes and therapeutic education. The scientific literature has shown the efficacy of self-monitoring with this system, but there are few studies on its appropriation and impact. This research aims to describe and understand the phenomenon of appropriation of FreeStyle Libre by identifying how it has been implemented, how it is operationalized, according to which interventions, in whom it works, in which contexts, and what mechanisms are at work. A realist evaluation was carried out based on a middle-range theory. This research was conducted in four settings in the Paris area involving 48 people living with diabetes and healthcare professionals. First of all, the results show that over time, the programmes have evolved in their modalities and contents, in the way they were organized, but also that the implemented educational interventions differed from those that were supposed to take place. Next, to explain the appropriation of FreeStyle Libre, 114 context-mechanism-effect chains were constructed that shed light on the acceptance of FreeStyle Libre, the conditions and modalities of its use, and the effects produced through it. The context-mechanism-effect chains highlight contexts that are more favourable to appropriation (high digital literacy, pre-existing empowerment, commitment to self-management, etc.) and less favourable contexts (compulsive personality trait, low general or digital literacy, lack of education and support, etc.). The mechanisms that are generated involve knowledge, lack of fear about confidentiality and privacy, motivation, and personal norms. Acceptance of the FSL is strong and involves the perception that the technology can contribute to the performance of self-monitoring of blood glucose and that it is easy to use. Then, the analysis allowed us to distinguish several modalities of use according to quantitative and qualitative indicators of use. The effects of appropriation are identified in the improvement of the quality of life in diabetes, the improvement of the interpersonal relationship between caregivers and cared-for persons, the reduction of anxiety related to diabetes, the adaptation of treatments and behaviours, and finally in the knowledge of the disease and the reasoning of the persons. The final middle-range theory built on these results addresses a global model of the appropriation of FreeStyle Libre.
This study shows that there are many variations of appropriation. It identifies that education in the use of FreeStyle Libre is needed to get more out of it, and identifies a lack of integration of connected technology into health education programmes, which is a particular challenge for the future
Computational acquisition of knowledge in small-data environments: a case study in the field of energetics
The UK’s defence industry is accelerating its implementation of artificial intelligence, including
expert systems and natural language processing (NLP) tools designed to supplement human
analysis. This thesis examines the limitations of NLP tools in small-data environments (common
in defence) in the defence-related energetic-materials domain. A literature review identifies
the domain-specific challenges of developing an expert system (specifically an ontology). The
absence of domain resources such as labelled datasets and, most significantly, the preprocessing
of text resources are identified as challenges. To address the latter, a novel general-purpose
preprocessing pipeline specifically tailored for the energetic-materials domain is developed. The
effectiveness of the pipeline is evaluated.
Examination of the interface between using NLP tools in data-limited environments to either
supplement or replace human analysis completely is conducted in a study examining the subjective
concept of importance. A methodology for directly comparing the ability of NLP tools
and experts to identify important points in the text is presented. Results show the participants
of the study exhibit little agreement, even on which points in the text are important. The NLP,
expert (author of the text being examined) and participants only agree on general statements.
However, as a group, the participants agreed with the expert. In data-limited environments,
the extractive-summarisation tools examined cannot effectively identify the important points
in a technical document akin to an expert.
A methodology for the classification of journal articles by the technology readiness level (TRL)
of the described technologies in a data-limited environment is proposed. Techniques to overcome
challenges with using real-world data such as class imbalances are investigated. A methodology
to evaluate the reliability of human annotations is presented. Analysis identifies a lack of
agreement and consistency in the expert evaluation of document TRL.Open Acces
S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity Recognition
Named Entity Recognition (NER) remains challenging due to the complex
entities, like nested, overlapping, and discontinuous entities. Existing
approaches, such as sequence-to-sequence (Seq2Seq) generation and span-based
classification, have shown impressive performance on various NER subtasks, but
they are difficult to scale to datasets with longer input text because of
either exposure bias issue or inefficient computation. In this paper, we
propose a novel Sequence-to-Forest generation paradigm, S2F-NER, which can
directly extract entities in sentence via a Forest decoder that decode multiple
entities in parallel rather than sequentially. Specifically, our model generate
each path of each tree in forest autoregressively, where the maximum depth of
each tree is three (which is the shortest feasible length for complex NER and
is far smaller than the decoding length of Seq2Seq). Based on this novel
paradigm, our model can elegantly mitigates the exposure bias problem and keep
the simplicity of Seq2Seq. Experimental results show that our model
significantly outperforms the baselines on three discontinuous NER datasets and
on two nested NER datasets, especially for discontinuous entity recognition
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Explainable online health information truthfulness in Consumer Health Search
Introduction: People are today increasingly relying on health information they find online to make decisions that may impact both their physical and mental wellbeing. Therefore, there is a growing need for systems that can assess the truthfulness of such health information. Most of the current literature solutions use machine learning or knowledge-based approaches treating the problem as a binary classification task, discriminating between correct information and misinformation. Such solutions present several problems with regard to user decision making, among which: (i) the binary classification task provides users with just two predetermined possibilities with respect to the truthfulness of the information, which users should take for granted; indeed, (ii) the processes by which the results were obtained are often opaque and the results themselves have little or no interpretation. Methods: To address these issues, we approach the problem as an ad hoc retrieval task rather than a classification task, with reference, in particular, to the Consumer Health Search task. To do this, a previously proposed Information Retrieval model, which considers information truthfulness as a dimension of relevance, is used to obtain a ranked list of both topically-relevant and truthful documents. The novelty of this work concerns the extension of such a model with a solution for the explainability of the results obtained, by relying on a knowledge base consisting of scientific evidence in the form of medical journal articles. Results and discussion: We evaluate the proposed solution both quantitatively, as a standard classification task, and qualitatively, through a user study to examine the “explained” ranked list of documents. The results obtained illustrate the solution's effectiveness and usefulness in making the retrieved results more interpretable by Consumer Health Searchers, both with respect to topical relevance and truthfulness
Semantic-aware Retrieval Standards based on Dirichlet Compound Model to Rank Notifications by Level of Urgency
There is a growing number of notifications generated from a wide range of sources. However, to our knowledge, there is no well-known generalizable standard for detecting the most urgent notifications. Establishing reusable standards is crucial for applications in which the recommendation (notification) is critical due to the level of urgency and sensitivity (e.g. medical domain). To tackle this problem, this thesis aims to establish Information Retrieval (IR) standards for notification (recommendation) task by taking semantic dimensions (terms, opinions, concepts and user interaction) into consideration. The technical research contributions of this thesis include but not limited to the development of a semantic IR framework based on Dirichlet Compound Model (DCM); namely FDCM, extending FDCM to the recommendation scenario (RFDCM) and proposing novel opinion-aware ranking models. Transparency, explainability and generalizability are some benefits that the use of a mathematically well-defined solution such as DCM offers. The FDCM framework is based on a robust aggregation parameter which effectively combines the semantic retrieval scores using Query Performance Predictors (QPPs). Our experimental results confirm the effectiveness of such approach in recommendation systems and semantic retrieval. One of the main findings of this thesis is that the concept-based extension (term-only + concept-only) of FDCM consistently outperformed both terms-only and concept-only baselines concerning biomedical data. Moreover, we show that semantic IR is beneficial for collaborative filtering and therefore it could help data scientists to develop hybrid and consolidated IR systems comprising content-based and collaborative filtering aspects of recommendation
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Screening prioritisation in medical systematic reviews aims to rank the set
of documents retrieved by complex Boolean queries. The goal is to prioritise
the most important documents so that subsequent review steps can be carried out
more efficiently and effectively. The current state of the art uses the final
title of the review to rank documents using BERT-based neural neural rankers.
However, the final title is only formulated at the end of the review process,
which makes this approach impractical as it relies on ex post facto
information. At the time of screening, only a rough working title is available,
with which the BERT-based ranker achieves is significantly worse than the final
title. In this paper, we explore alternative sources of queries for screening
prioritisation, such as the Boolean query used to retrieve the set of documents
to be screened, and queries generated by instruction-based generative large
language models such as ChatGPT and Alpaca. Our best approach is not only
practical based on the information available at screening time, but is similar
in effectiveness with the final title.Comment: Preprints for Accepted paper in SIGIR-AP-202
Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)
Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this they’re trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes.
The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question.
Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited:
• A multidisciplinary approach to the transformation of health and social systems
• Success factors for sustainable P5 ecosystems
• AI and robotics in transformed health ecosystems
• Transformed health ecosystems challenges for security, privacy and trust
• Modelling digital health systems
• Ethical challenges of personalized digital health
• Knowledge representation and management of transformed health ecosystems
Table of Contents:
04 Editorial: Managing healthcare transformation towards P5
medicine
Bernd Blobel and Dipak Kalra
06 Transformation of Health and Social Care Systems—An
Interdisciplinary Approach Toward a Foundational
Architecture
Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez
26 Transformed Health Ecosystems—Challenges for Security,
Privacy, and Trust
Pekka Ruotsalainen and Bernd Blobel
36 Success Factors for Scaling Up the Adoption of Digital
Therapeutics Towards the Realization of P5 Medicine
Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov,
Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra
49 EU-Funded Telemedicine Projects – Assessment of, and
Lessons Learned From, in the Light of the SARS-CoV-2
Pandemic
Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri,
Claudia Bighin, Bernd Blobel and Mauro Giacomini
60 A Review of Artificial Intelligence and Robotics in
Transformed Health Ecosystems
Kerstin Denecke and Claude R. Baudoin
73 Modeling digital health systems to foster interoperability
Frank Oemig and Bernd Blobel
89 Challenges and solutions for transforming health ecosystems
in low- and middle-income countries through artificial
intelligence
Diego M. LĂłpez, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin
111 Linguistic and ontological challenges of multiple domains
contributing to transformed health ecosystems
Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel
and Stefan Schulz
126 The ethical challenges of personalized digital health
Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides
and Michael Rigb
Enabling Cross-lingual Information Retrieval for African Languages
Language diversity in NLP is critical in enabling the development of tools for a wide range of users. However, there are limited resources for building such tools for many languages, particularly those spoken in Africa. For search, most existing datasets feature few to no African languages, directly impacting researchers’ ability to build and improve information access capabilities in those languages.
Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages automatically created from Wikipedia. The dataset comprises 6 million queries in English and 23 million relevance judgments automatically extracted from Wikipedia inter-language links. We extract 13,050 test queries with relevant judgments across 15 languages, covering a significantly broader range of African languages than other existing information retrieval test collections.
In addition to providing a much-needed resource for researchers, we also release BM25, dense retrieval, and sparse-dense hybrid baselines to establish a starting point for the development of future systems. We hope that our efforts will stimulate further research in information retrieval for African languages and lead to the creation of more effective tools for the benefit of users
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