Extracting biomedical relations from biomedical literature

Tese de mestrado em Bioinformática e Biologia Computacional, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, em 2018A ciência, e em especial o ramo biomédico, testemunham hoje um crescimento de conhecimento a uma taxa que clínicos, cientistas e investigadores têm dificuldade em acompanhar. Factos científicos espalhados por diferentes tipos de publicações, a riqueza de menções etiológicas, mecanismos moleculares, pontos anatómicos e outras terminologias biomédicas que não se encontram uniformes ao longo das várias publicações, para além de outros constrangimentos, encorajaram a aplicação de métodos de text mining ao processo de revisão sistemática. Este trabalho pretende testar o impacto positivo que as ferramentas de text mining juntamente com vocabulários controlados (enquanto forma de organização de conhecimento, para auxílio num posterior momento de recolha de informação) têm no processo de revisão sistemática, através de um sistema capaz de criar um modelo de classificação cujo treino é baseado num vocabulário controlado (MeSH), que pode ser aplicado a uma panóplia de literatura biomédica. Para esse propósito, este projeto divide-se em duas tarefas distintas: a criação de um sistema, constituído por uma ferramenta que pesquisa a base de dados PubMed por artigos científicos e os grava de acordo com etiquetas pré-definidas, e outra ferramenta que classifica um conjunto de artigos; e a análise dos resultados obtidos pelo sistema criado, quando aplicado a dois casos práticos diferentes. O sistema foi avaliado através de uma série de testes, com recurso a datasets cuja classificação era conhecida, permitindo a confirmação dos resultados obtidos. Posteriormente, o sistema foi testado com recurso a dois datasets independentes, manualmente curados por investigadores cuja área de investigação se relaciona com os dados. Esta forma de avaliação atingiu, por exemplo, resultados de precisão cujos valores oscilam entre os 68% e os 81%. Os resultados obtidos dão ênfase ao uso das tecnologias e ferramentas de text mining em conjunto com vocabulários controlados, como é o caso do MeSH, como forma de criação de pesquisas mais complexas e dinâmicas que permitam melhorar os resultados de problemas de classificação, como são aqueles que este trabalho retrata.Science, and the biomedical field especially, is witnessing a growth in knowledge at a rate at which clinicians and researchers struggle to keep up with. Scientific evidence spread across multiple types of scientific publications, the richness of mentions of etiology, molecular mechanisms, anatomical sites, as well as other biomedical terminology that is not uniform across different writings, among other constraints, have encouraged the application of text mining methods in the systematic reviewing process. This work aims to test the positive impact that text mining tools together with controlled vocabularies (as a way of organizing knowledge to aid, at a later time, to collect information) have on the systematic reviewing process, through a system capable of creating a classification model which training is based on a controlled vocabulary (MeSH) that can be applied to a variety of biomedical literature. For that purpose, this project was divided into two distinct tasks: the creation a system, consisting of a tool that searches the PubMed search engine for scientific articles and saves them according to pre-defined labels, and another tool that classifies a set of articles; and the analysis of the results obtained by the created system when applied to two different practical cases. The system was evaluated through a series of tests, using datasets whose classification results were previously known, allowing the confirmation of the obtained results. Afterwards, the system was tested by using two independently-created datasets which were manually curated by researchers working in the field of study. This last form of evaluation achieved, for example, precision scores as low as 68%, and as high as 81%. The results obtained emphasize the use of text mining tools, along with controlled vocabularies, such as MeSH, as a way to create more complex and comprehensive queries to improve the performance scores of classification problems, with which the theme of this work relates

Micropublications: a Semantic Model for Claims, Evidence, Arguments and Annotations in Biomedical Communications

The Micropublications semantic model for scientific claims, evidence, argumentation and annotation in biomedical publications, is a metadata model of scientific argumentation, designed to support several key requirements for exchange and value-addition of semantic metadata across the biomedical publications ecosystem. Micropublications allow formalizing the argument structure of scientific publications so that (a) their internal structure is semantically clear and computable; (b) citation networks can be easily constructed across large corpora; (c) statements can be formalized in multiple useful abstraction models; (d) statements in one work may cite statements in another, individually; (e) support, similarity and challenge of assertions can be modelled across corpora; (f) scientific assertions, particularly in review articles, may be transitively closed to supporting evidence and methods. The model supports natural language statements; data; methods and materials specifications; discussion and commentary; as well as challenge and disagreement. A detailed analysis of nine use cases is provided, along with an implementation in OWL 2 and SWRL, with several example instantiations in RDF.Comment: Version 4. Minor revision

The use of wearable/portable digital sensors in Huntington’s disease: a systematic review

In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington’s disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD

DoR Communicator - February 2014

https://digitalcommons.fiu.edu/research_newsletter/1005/thumbnail.jp

Washington University Record, September 11, 2008

https://digitalcommons.wustl.edu/record/2150/thumbnail.jp

Technology and dementia: the future is now

Background: Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. Objectives: To summarise key areas of technology development in dementia and identify future directions and implications. Method: Members of the US Alzheimer’s Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. Results: The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. Conclusions: The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and pre- vention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia

In the pursuit of a semantic similarity metric based on UMLS annotations for articles in PubMed Central

Motivation Although full-text articles are provided by the publishers in electronic formats, it remains a challenge to find related work beyond the title and abstract context. Identifying related articles based on their abstract is indeed a good starting point; this process is straightforward and does not consume as many resources as full-text based similarity would require. However, further analyses may require in-depth understanding of the full content. Two articles with highly related abstracts can be substantially different regarding the full content. How similarity differs when considering title-and-abstract versus full-text and which semantic similarity metric provides better results when dealing with full-text articles are the main issues addressed in this manuscript. Methods We have benchmarked three similarity metrics – BM25, PMRA, and Cosine, in order to determine which one performs best when using concept-based annotations on full-text documents. We also evaluated variations in similarity values based on title-and-abstract against those relying on full-text. Our test dataset comprises the Genomics track article collection from the 2005 Text Retrieval Conference. Initially, we used an entity recognition software to semantically annotate titles and abstracts as well as full-text with concepts defined in the Unified Medical Language System (UMLS®). For each article, we created a document profile, i.e., a set of identified concepts, term frequency, and inverse document frequency; we then applied various similarity metrics to those document profiles. We considered correlation, precision, recall, and F1 in order to determine which similarity metric performs best with concept-based annotations. For those full-text articles available in PubMed Central Open Access (PMC-OA), we also performed dispersion analyses in order to understand how similarity varies when considering full-text articles. Results We have found that the PubMed Related Articles similarity metric is the most suitable for full-text articles annotated with UMLS concepts. For similarity values above 0.8, all metrics exhibited an F1 around 0.2 and a recall around 0.1; BM25 showed the highest precision close to 1; in all cases the concept-based metrics performed better than the word-stem-based one. Our experiments show that similarity values vary when considering only title-and-abstract versus full-text similarity. Therefore, analyses based on full-text become useful when a given research requires going beyond title and abstract, particularly regarding connectivity across articles. Availability Visualization available at ljgarcia.github.io/semsim.benchmark/, data available at http://dx.doi.org/10.5281/zenodo.13323.The authors acknowledge the support from the members of Temporal Knowledge Bases Group at Universitat Jaume I. Funding: LJGC and AGC are both self-funded, RB is funded by the “Ministerio de Economía y Competitividad” with contract number TIN2011-24147

Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data size. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pre-training of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. We propose Med-BERT, which adapts the BERT framework for pre-training contextualized embedding models on structured diagnosis data from 28,490,650 patients EHR dataset. Fine-tuning experiments are conducted on two disease-prediction tasks: (1) prediction of heart failure in patients with diabetes and (2) prediction of pancreatic cancer from two clinical databases. Med-BERT substantially improves prediction accuracy, boosting the area under receiver operating characteristics curve (AUC) by 2.02-7.12%. In particular, pre-trained Med-BERT substantially improves the performance of tasks with very small fine-tuning training sets (300-500 samples) boosting the AUC by more than 20% or equivalent to the AUC of 10 times larger training set. We believe that Med-BERT will benefit disease-prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare.Comment: L.R., X.Y., and Z.X. share first authorship of this wor

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined