A Recommender System to Help Discovering Cohorts in Rare Diseases

Almeida, João Rafael; Monteiro, Eriksson; Silva, Luís A. Bastião; Sierra, Alejandro Pazos; Oliveira, José Luís

doi:10.1109/cbms49503.2020.00012

Cited by 12 publications

(6 citation statements)

References 16 publications

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“…In a previous work, we used a recommender system to enable researchers to discover cohorts of interest [2]. Although these systems provide good results, they are not suitable for verifying studies' feasibility.…”

Section: Results and Conclusionmentioning

confidence: 99%

Discovery of Biomedical Databases Through Semantic Questioning

Pereira

Almeida

Lopes

et al. 2022

Studies in Health Technology and Informatics

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Many clinical studies are greatly dependent on an efficient identification of relevant datasets. This selection can be performed in existing health data catalogues, by searching for available metadata. The search process can be optimised through questioning-answering interfaces, to help researchers explore the available data present. However, when searching the distinct catalogues the lack of metadata harmonisation imposes a few bottlenecks. This paper presents a methodology to allow semantic search over several biomedical database catalogues, by extracting the information using a shared domain knowledge. The resulting pipeline allows the converted data to be published as FAIR endpoints, and it provides an end-user interface that accepts natural language questions.

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Section: Results and Conclusionmentioning

confidence: 99%

Discovery of Biomedical Databases Through Semantic Questioning

Pereira

Almeida

Lopes

et al. 2022

Studies in Health Technology and Informatics

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“…The authors evaluated their model using precision, recall, and F-measure. Context-based filtering combined with a collaborative filtering recommender was developed to discover cohorts of interests, as discussed in [83]. Almeida et al utilized collaborative filtering to detect similar users' profiles and context-based filtering to generate better suggestions and recommendations.…”

Section: A Context-aware Recommendation Systems In E-healthmentioning

confidence: 99%

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Nawara

Kashef

2021

IEEE Access

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An essential goal of recommendation systems is to provide users with accurate and personalized recommendations that meet their preferences. With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS). Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information. Context-aware recommenders define the context as any information that characterizes the situations of items and users at a particular interaction. They are essential for some contexts where prediction can be more precise in generating specific personalized recommendations. This paper provides a comprehensive review of context-based recommendation systems in IoT environments, namely IoT-CARS, and sheds light on their requirements, characteristics, and applications. We characterize context-aware recommenders in terms of the different IoT contexts and how these contexts are modeled. We also highlight the used metrics to evaluate the performance of various context-based recommenders.

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“…When using a recommendation system, recommendations are based on rules that can be more or less adaptive to new situations [ 13 ]. While history-based learning, when using these tools, can lead to better results over time, it is still not the best approach for many cases.…”

Section: Introductionmentioning

confidence: 99%

Semantic Data Visualisation for Biomedical Database Catalogues

et al. 2022

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Biomedical databases often have restricted access policies and governance rules. Thus, an adequate description of their content is essential for researchers who wish to use them for medical research. A strategy for publishing information without disclosing patient-level data is through database fingerprinting and aggregate characterisations. However, this information is still presented in a format that makes it challenging to search, analyse, and decide on the best databases for a domain of study. Several strategies allow one to visualise and compare the characteristics of multiple biomedical databases. Our study focused on a European platform for sharing and disseminating biomedical data. We use semantic data visualisation techniques to assist in comparing descriptive metadata from several databases. The great advantage lies in streamlining the database selection process, ensuring that sensitive details are not shared. To address this goal, we have considered two levels of data visualisation, one characterising a single database and the other involving multiple databases in network-level visualisations. This study revealed the impact of the proposed visualisations and some open challenges in representing semantically annotated biomedical datasets. Identifying future directions in this scope was one of the outcomes of this work.

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A Recommender System to Help Discovering Cohorts in Rare Diseases

Cited by 12 publications

References 16 publications

Discovery of Biomedical Databases Through Semantic Questioning

Discovery of Biomedical Databases Through Semantic Questioning

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Semantic Data Visualisation for Biomedical Database Catalogues

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