Important information is captured in medical documents. To make use of this information and intepret the semantics, technologies are required for extracting, analysing and interpreting it. As a result, rich semantics including relations among events, subjectivity or polarity of events, become available. The First Workshop on Extraction and Processing of Rich Semantics from Medical Texts, is devoted to the technologies for dealing with clinical documents for medical information gathering and application in knowledge based systems. New approaches for identifying and analysing rich semantics are presented. In this paper, we introduce the topic and summarize the workshop contributions.
Pharmaceutical companies and individual patients exploiting advances in translational medicine and informational infrastructure are joining clinical interests in recording detailed patient records. The latter comprise a broad range of clinical documents including nurse letters, discharge summaries and radiology reports describing a patients health status, diagnoses, applied procedures and observations of the health care team. The rich semantics such as facts, experiences, opinions or information that are hidden in those medical documents could when extracted automatically - support a broad range of applications including clinical decision support systems. Physicians could learn about the experiences of their colleagues, get hints to critical events in the treatment of a speci c patient or receive information for improving treatment. Studies on the e ectiveness of clinical treatment could be realised based on the text material. The workshop on Extraction and Processing of Rich Semantics from Medical Texts (RichMedSem) brings together researchers and their work in this upcoming eld. Relevant topics include on the one hand extraction methods speci cally designed for the extraction of rich semantics from medical texts. Further, the representation and storage of extracted rich semantics for further analysis is an important aspect. Semantic Web technologies are of particular interest, since they could provide the means to link this type of medical information with other data sets in a Linked Data infrastructure. What makes rich semantics, what are the challenges to be addressed and which approaches are suggested? The work presented in the RichMedSem workshop addresses these questions and answers are summarized in the following. 2
With rich semantics, we refer to concepts and their relations and characteristics described in written text. Existing methods for information extraction from clinical texts addressed the extraction of mentions of diagnoses, clinical treatments or medications mainly for the purpose of clinical coding, detection of drug interactions or contraindications. Extraction or addition of rich semantics goes beyond this. Rich semantics can include descriptions of clinical events, relations among clinical events (e.g. causality relations), but also subjectivity, polarity, emotion or even comparison, for example { A change in the health status (e.g., a patient can suddenly feel better or worse), { Critical events, unexpected situations or speci c medical conditions that impact the patient's life (e.g., tumour is malignant as such is a fact, but this medical condition is negative for the patient since it might lead to health problems or death), { The outcome or e ectiveness of a treatment (e.g., a surgery can be successfully completed), { Experiences or opinions towards a treatment or a sort of drug (e.g., a patient or a physician can describe serious adverse events after drug consumption), { The certainty of a diagnosis (e.g., a physician can be certain of some diagnosis). 3
One big challenge for extracting rich semantics from clinical texts are language peculiarities, content diversity, streaming nature of clinical documents that pose many challenges to an automatic processing, Finding the trade-o of ltering noise at the cost of losing potentially relevant information is crucial. In contrast to biomedical documents, clinical documents are often not well formulated. They can for example contain verbless clauses, writing errors, many idiosyncratic abbreviations and sentence complexity of such document ranges from few word phrases to complex sentence structures. New technologies are required for dealing with the peculiarities of clinical documents, in particular for extracting, Extraction and Processing of Rich Semantics from Medical Texts analysing or adding semantics, which can be included into corresponding knowledge based systems. The challenges of extracting rich semantics stem from the ultimate ambiguities caused by the objective nature of the medical text. The medical knowledge bases have also a deep in uence on the outcome of the semantic meanings. For instance, events such as a bleeding can be positive or negative, critical or less critical. The phrase blood pressure decreased could express a positive or negative change depending on the previous state of blood pressure. A decrease of blood pressure can be good when it was too high before. This also shows that sentiment in clinical narratives cannot always be manifested in single terms of phrases, but the context is important.Moreover, the medical semantics include a large amount of aspects, a coherent de nition and standard data schema should be established to facilitate the linking and the usage of current available ontologies in the biomedical domain. 4
The papers accepted in the RichMedSem workshop this year have focused on
the extraction, retrieval of biomedical semantics and corpus generation in the
biomedical domain, which covered the foundation of text analysis in the
biomedical domain. Shafahi et.al [
There is an increased awareness that rich semantics such as sentiments, opinions and other qualitative factors are relevant in ensuring individualized care. New research topics are coming up (e.g. sentiment analysis from clinical texts). Rich semantics can be in the future be used in clinical decision support systems, with particular support of semantic web technologies. There is still a huge potential to connect semantic web technologies with extraction and storage of rich semantics from biomedical texts. Clinical decision support can only be realised when the amounts of unstructured texts can be processed, on the one hand to establish databases with rich semantics; on the other hand to extract decision relevant information for speci c patients targeting at improving treatment.