TrialMatch: A Transformer Architecture to Match Patients to Clinical Trials

Around 80% of clinical trials fail to meet the patient recruitment requirements, which not only hinders the market growth but also delays patients’ access to new and effec- tive treatments. A possible approach is to use Electronic Health Records (EHRs) to help match patients to clinical trials. Past attempts at achieving this exact goal took place, but due to a lack of data, they were unsuccessful. In 2021 Text REtrieval Conference (TREC) introduced the Clinical Trials Track, where participants were challenged with retrieving relevant clinical trials given the patient’s descriptions simulating admission notes. Utilizing the track results as a baseline, we tackled the challenge, for this, we re- sort to Information Retrieval (IR), implementing a pipeline for document ranking where we explore the different retrieval methods, how to filter the clinical trials based on the criteria, and reranking with Transformer based models. To tackle the problem, we ex- plored models pre-trained on the biomedical domain, how to deal with long queries and documents through query expansion and passage selection, and how to distinguish an eligible clinical trial from an excluded clinical trial, using techniques such as Named Entity Recognition (NER) and Clinical Assertion. Our results let to the finding that the current state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) bi-encoders outperform the cross-encoders in the mentioned task, whilst proving that sparse retrieval methods are capable of obtaining competitive outcomes, and to finalize we showed that the use of the demographic information available can be used to improve the final result.Cerca de 80% dos ensaios clínicos não satisfazem os requisitos de recrutamento de paci- entes, o que não só dificulta o crescimento do mercado como também impede o acesso dos pacientes a novos e eficazes tratamentos. Uma abordagem possível é utilizar os Pron- tuários Eletrônicos para ajudar a combinar doentes a ensaios clínicos. Tentativas passadas para alcançar este exato objetivo tiveram lugar, mas devido à falta de dados, não foram bem sucedidos. Em 2021, a TREC introduziu a Clinical Trials Track, onde os participantes foram desafiados com a recuperação ensaios clínicos relevantes, dadas as descrições dos pacientes simulando notas de admissão. Utilizando os resultados da track como base, en- frentámos o desafio, para isso recorremos à Recuperação de Informação, implementando uma pipeline para a classificação de documentos onde exploramos os diferentes métodos de recuperação, como filtrar os ensaios clínicos com base nos critérios, e reclassificação com modelos baseados no Transformer. Para enfrentar o problema, explorámos modelos pré-treinados no domínio biomédico, como lidar com longas descrições e documentos, e como distinguir um ensaio clínico elegível de um ensaio clínico excluído, utilizando técnicas como Reconhecimento de Entidade Mencionada e Asserção Clínica. Os nossos re- sultados permitem concluir que os actuais bi-encoders de última geração BERT superam os cross-encoders BERT na tarefa mencionada, provamos que os métodos de recuperação esparsos são capazes de obter resultados competitivos, e para finalizar mostramos que a utilização da informação demográfica disponível pode ser utilizada para melhorar o resultado fina

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Scalable Generation of Type Embeddings Using the ABox

Structured knowledge bases gain their expressive power from both the ABox and TBox. While the ABox is rich in data, the TBox contains the ontological assertions that are often necessary for logical inference. The crucial links between the ABox and the TBox are served by is-a statements (formally a part of the ABox) that connect instances to types, also referred to as classes or concepts. Latent space embedding algorithms, such as RDF2Vec and TransE, have been used to great effect to model instances in the ABox. Such algorithms work well on large-scale knowledge bases like DBpedia and Geonames, as they are robust to noise and are low-dimensional and real-valued. In this paper, we investigate a supervised algorithm for deriving type embeddings in the same latent space as a given set of entity embeddings. We show that our algorithm generalizes to hundreds of types, and via incremental execution, achieves near-linear scaling on graphs with millions of instances and facts. We also present a theoretical foundation for our proposed model, and the means of validating the model. The empirical utility of the embeddings is illustrated on five partitions of the English DBpedia ABox. We use visualization and clustering to show that our embeddings are in good agreement with the manually curated TBox. We also use the embeddings to perform a soft clustering on 4 million DBpedia instances in terms of the 415 types explicitly participating in is-a relationships in the DBpedia ABox. Lastly, we present a set of results obtained by using the embeddings to recommend types for untyped instances. Our method is shown to outperform another feature-agnostic baseline while achieving 15x speedup without any growth in memory usage

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Few-shot entity linking of food names

Entity linking (EL), the task of automatically matching mentions in text to concepts in a target knowledge base, remains under-explored when it comes to the food domain, despite its many potential applications, e.g., finding the nutritional value of ingredients in databases. In this paper, we describe the creation of new resources supporting the development of EL methods applied to the food domain: the E.Care Knowledge Base (E.Care KB) which contains 664 food concepts and the E.Care dataset, a corpus of 468 cooking recipes where ingredient names have been manually linked to corresponding concepts in the E.Care KB. We developed and evaluated different methods for EL, namely, deep learning-based approaches underpinned by Siamese networks trained under a few-shot learning setting, traditional machine learning-based approaches underpinned by support vector machines (SVMs) and unsupervised approaches based on string matching algorithms. Combining the strengths of each of these approaches, we built a hybrid model for food EL that balances the trade-offs between performance and inference speed. Specifically, our hybrid model obtains 89.40% accuracy and links mentions at an average speed of 0.24 seconds per mention, whereas our best deep learning-based model, SVM model and unsupervised model obtain accuracies of 86.99%, 87.19% and 87.43% at inference speeds of 0.007, 0.66 and 0.02 seconds per mention, respectively