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Personalised treatment is usually needed for hospitalised patients afflicted by secondary illnesses that demand daily medication. Even though clinical guidelines were designed to consider those circumstances exist, current decision-support features fail to assimilate detailed relevant patient information. This creates opportunities for the development of systems capable of performing a real-time evaluation of such data against existing knowledge and providing recommendations during clinical treatments. Herein, we describe a proposal for a new feature to be integrated with electronic health record (EHR) systems which can enrich the health treatment process through the automatic extraction of information from patient medical notes and the aggregation of this novel information in clinical protocols. The purpose of this work is to exploit the historical component of the patient trajectory to improve the performance of clinical decision support systems.

The World Health Organization reports that half of all mental illnesses begin by the age of 14. Most of these cases go undetected and untreated. The expanding use of social media has the potential to leverage the early identification of mental health diseases. As data gathered via social media are already digital, they have the ability to power up faster automatic analysis. In this article we evaluate the impact that psycholinguistic patterns can have on a standard machine learning approach for classifying depressed users based on their writings in an online public forum. We combine psycholinguistic features in a rule-based estimator and we evaluate their impact on this classification problem, along with three other standard classifiers. Our results on the Reddit Self-reported Depression Diagnosis dataset outperform some previously reported works on the same dataset. They stand for the importance of extracting psychologically motivated features when processing social media texts with the purpose of studying mental health.

Question answering can be described as retrieving relevant information for questions expressed in natural language, possibly also generating a natural language answer. This paper presents a pipeline for document and passage retrieval for biomedical question answering built around a new variant of the DeepRank network model in which the recursive layer is replaced by a self-attention layer combined with a weighting mechanism. This adaptation halves the total number of parameters and makes the network more suited for identifying the relevant passages in each document. The overall retrieval system was evaluated on the BioASQ tasks 6 and 7, achieving similar retrieval performance when compared to more complex network architectures.

Understanding of biological processes, associated to disease or pharmacological action for example, requires the analysis of large amounts of interconnected information. Protein interaction networks form part of this puzzle, and extracting this information from the scientific literature is an important but challenging task.

Tackling the ever-growing amount of specialized literature in the life sciences domain is a paramount challenge. Various scientific workflows depend on using domain knowledge from resources that summarize, in structured form, validated information extracted from scientific publications. Manual curation of these data is a demanding task, and latest strategies use computerized solutions to aid in the analysis, extraction and storage of relevant concepts and their respective attributes and relationships. The outcome of these complex document curation workflows provides valuable insights into the overwhelming amount of biomedical information being produced. Yet, the majority of automated and interactive annotation tools are not open, limiting access to knowledge and reducing the potential scope of the manually curated information. In this manuscript, we propose an interoperable semantic layer to unify document curation results and enable their proper exploration through multiple interfaces geared towards bioinformatics developers and general life sciences researchers. This enables a unique scenario where results from computational annotation tools are harmonized and further integrated into rich semantic knowledge bases, providing a solid foundation for discovering knowledge.

The scientific literature is one of the main sources of information for researchers. However, due to the rapid increase of the number of scientific articles, satisfying a specific information need has become a very demanding task, and researchers often have to scan through a large number of publications in search of a specific nugget of information. In this work we propose the use of supervised machine learning techniques to retrieve and rank sentences describing different types of biomolecular events. The objective is to classify and rank sentences that match any general query according to the likelihood of mentioning events involving one or more biomolecular entities. These ranked results should provide a condensed, or summarized, view of the knowledge present in the literature and related to the user’s information need.

The fast increasing amount of articles published in the biomedical field is creating difficulties in the way this wealth of information can be efficiently exploited by researchers. As a way of overcoming these limitations and potentiating a more efficient use of the literature, we propose an approach for structuring the results of a literature search based on the latent semantic information extracted from a corpus. Moreover, we show how the results of the Latent Semantic Analysis method can be adapted so as to evidence differences between results of different searches. We also propose different visualization techniques that can be applied to explore these results. Used in combination, these techniques could empower users with tools for literature guided knowledge exploration and discovery.