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Incorporating the feedback of expert stakeholders in ontology development is important to ensure content is appropriate, comprehensive, meets community needs and is interoperable with other ontologies and clas...
Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and ...
Accurate and precise information about the therapeutic uses (indications) of a drug is essential for applications in drug repurposing and precision medicine. Leading online drug resources such as DrugCentral a...
Population-based cancer registries constitute an important information source in cancer epidemiology. Studies collating and comparing data across regional and national boundaries have proved important for depl...
The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. ...
Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, therefore, has lim...
The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare disease...
Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extrac...
Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. ...
Up to 35% of nurses’ working time is spent on care documentation. We describe the evaluation of a system aimed at assisting nurses in documenting patient care and potentially reducing the documentation workloa...
Knowledge graphs can represent the contents of biomedical literature and databases as subject-predicate-object triples, thereby enabling comprehensive analyses that identify e.g. relationships between diseases...
A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information...
Textual corpora are extremely important for various NLP applications as they provide information necessary for creating, setting and testing those applications and the corresponding tools. They are also crucia...
Sharing sensitive data across organizational boundaries is often significantly limited by legal and ethical restrictions. Regulations such as the EU General Data Protection Rules (GDPR) impose strict requireme...
Health 2.0 allows patients and caregivers to conveniently seek medical information and advice via e-portals and online discussion forums, especially regarding potential drug side effects. Although online healt...
Most tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as emergent modelling guideli...
Scientific activity for 3D bioprinting has increased over the past years focusing mainly on fully functional biological constructs to overcome issues related to organ transplants. This research performs a scie...
Duration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. DUP estimation requires knowledge about wh...
Ontologies are widely used across biology and biomedicine for the annotation of databases. Ontology development is often a manual, time-consuming, and expensive process. Automatic or semi-automatic identificat...
Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases. Extensive HMI studies have generated large amounts of data. We propose that...
Knee injury and Osteoarthritis Outcome Score (KOOS) is an instrument used to quantify patients’ perceptions about their knee condition and associated problems. It is administered as a 42-item closed-ended ques...
This article is part of a Supplement: Volume 10 Supplement 1
With the improvements to text mining technology and the availability of large unstructured Electronic Healthcare Records (EHR) datasets, it is now possible to extract structured information from raw text conta...
This article is part of a Supplement: Volume 10 Supplement 1
Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and ...
This article is part of a Supplement: Volume 10 Supplement 1
Significant amounts of health data are stored as free-text within clinical reports, letters, discharge summaries and notes. Busy clinicians have limited time to read such large amounts of free-text and are at ...
This article is part of a Supplement: Volume 10 Supplement 1
Free text in electronic health records (EHR) may contain additional phenotypic information beyond structured (coded) information. For major health events – heart attack and death – there is a lack of studies e...
This article is part of a Supplement: Volume 10 Supplement 1
There is an increasing amount of unstructured medical data that can be analysed for different purposes. However, information extraction from free text data may be particularly inefficient in the presence of sp...
This article is part of a Supplement: Volume 10 Supplement 1
Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmit...
Although the mouse is widely used to model human lung development, function, and disease, our understanding of the molecular mechanisms involved in alveolarization of the peripheral lung is incomplete. Recentl...
Cell tracking experiments, based on time-lapse microscopy, have become an important tool in biomedical research. The goal is the reconstruction of cell migration patterns, shape and state changes, and, compreh...
Infectious diseases claim millions of lives especially in the developing countries each year. Identification of causative pathogens accurately and rapidly plays a key role in the success of treatment. To suppo...
Information in Electronic Health Records is largely stored as unstructured free text. Natural language processing (NLP), or Medical Language Processing (MLP) in medicine, aims at extracting structured informat...
Microbial genetics has formed a foundation for understanding many aspects of biology. Systematic annotation that supports computational data mining should reveal further insights for microbes, microbiomes, and...
Currently, almost all morphological data are published as unstructured free text descriptions. This not only brings about terminological problems regarding semantic transparency, which hampers their re-use by ...
To improve the outcomes of biological pathway analysis, a better way of integrating pathway data is needed. Ontologies can be used to organize data from disparate sources, and we leverage the Pathway Ontology ...
We introduce TranScriptML, a semantic representation schema for prescription regimens allowing various properties of prescriptions (e.g. dose, frequency, route) to be specified separately and applied (manually or...
The vigilant observation of medical devices during post-market surveillance (PMS) for identifying safety-relevant incidents is a non-trivial task. A wide range of sources has to be monitored in order to integr...
The ability to express the same meaning in different ways is a well-known property of natural language. This amazing property is the source of major difficulties in natural language processing. Given the const...
Most electronic medical records still contain large amounts of free-text data. Semantic evaluation of such data requires the data to be encoded with sufficient classifications or transformed into a knowledge-b...
Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many soc...
Information technology has transformed the way healthcare is conducted. There is a deluge of patient data dispersed in different systems that are commonly not interoperable. As a result, access to patient data...
With the emergence of high-throughput technologies, Big Data and eScience, the use of online data repositories and the establishment of new data standards that require data to be computer-parsable become incre...
A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older ...
VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. ...
Conclusive association entities (CAEs) in a biomedical article a are those biomedical entities (e.g., genes, diseases, and chemicals) that are specifically involved in the associations concluded in a. Identificat...
Structured electronic health records are a rich resource for identifying novel correlations, such as co-morbidities and adverse drug reactions. For drug development and better understanding of biomedical pheno...
Even though several high-quality clinical terminologies, such as SNOMED CT and LOINC, are readily available, uptake in clinical systems has been slow and many continue to capture information in plain text or u...
Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the pre...
In this paper, we discuss the design and development of a formal ontology to describe misinformation about vaccines. Vaccine misinformation is one of the drivers leading to vaccine hesitancy in patients. While...
While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has f...
Entropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, curren...
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Journal of Biomedical Semantics