JASIST special issue on biomedical information retrieval

Information Retrieval (IR) and text analytics in the biomedical and healthcare domain has been attracting an abundant amount of research in the past decades. Similar to other domains, the main purpose used to be accurate retrieval of documents. However, in recent years, with the increased access to Electronic Health Records (EHRs), the interests and tasks are expanding (Hersh 2002). Retrieval of biomedical literature always had its unique methods due to the rich knowledge bases, such as the Unified Medical Language System (UMLS), Medical Subject Headings (MeSH), and the Systematized Nomenclature of Medicine (SNOMED) that enable the indexing of documents into concepts, for various purposes, such as retrieval (Hersh & Greens, 1989; Hersh & Hickam, 1992, 1993; Moskovitch et al., 2004; Lin & Demner-Fushman, 2006) and more (Moskovitch et al., 2006; Moskovitch & Shahar 2009; Ruch, 2006). The use of these domain-specific terminologies and vocabularies boosted the development of a large number of domainoriented retrieval methods. An important related thread of research has been the application of Natural Language Processing techniques to extract named entity concepts from data (Ruch, 2006), as well as other important pieces of information. In addition, to evaluate methods in biomedical text analytics and retrieval, several critical test data collections are now available, including TREC (Roberts et al., 2016) and more. In recent years, with the increased access to patients’ data in the form of EHRs, there are new problems and challenges related to the analysis and extraction from clinical notes and discharge summaries, particularly because the notes typically are telegraphic and include abbreviations, meta-data, semistructured text such as tables and billing codes, and use new lines to signal the end of a sentence. Another important retrieval task in the biomedical domain is from images that require image processing and retrieval. In addition to the data accumulated at hospital systems, and to the traditional clinical literature published in scientific journals and conferences and indexed in PubMed, there is an increase in healthrelated discussions in various relevant online forums and social medical sites. These forums span over multiple topics in the medical domain, in which patients share and discuss their experiences and questions. Consequently, the users of medical information retrieval may vary in their clinical knowledge, from physicians, medical students and related experts, to patients, or their relatives. They may also vary in their information needs, from scientific literature for experts to patients’ discussions on the internet. All these characteristics bring many challenges and opportunities to the scientific community. Finally, in the biomedical domain, in addition to improving the access to information through better and more efficient retrieval methodologies, there is the potential to improve the quality of care for patients. Thus, in this special issue, we encouraged participation from researchers in all fields related to medical information research, including mainstream information retrieval, but also natural language processing, multilingual text processing, and medical image analysis and retrieval.