The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods. This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment. Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSⓇ metathesaurus. Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it.

Early identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the acceptance of the integration of such complex models in clinical routine remains unclear. The aim of this study was to evaluate user acceptance of an already implemented machine learning-based application predicting the risk of delirium for in-patients. We applied a mixed methods design to collect opinions and concerns from health care professionals including physicians and nurses who regularly used the application. The evaluation was framed by the Technology Acceptance Model assessing perceived ease of use, perceived usefulness, actual system use and output quality of the application. Questionnaire results from 47 nurses and physicians as well as qualitative results of four expert group meetings rated the overall usefulness of the delirium prediction positively. For healthcare professionals, the visualization and presented information was understandable, the application was easy to use and the additional information for delirium management was appreciated. The application did not increase their workload, but the actual system use was still low during the pilot study. Our study provides insights into the user acceptance of a machine learning-based application supporting delirium management in hospitals. In order to improve quality and safety in healthcare, computerized decision support should predict actionable events and be highly accepted by users.

Clinical data interoperability requires shared specifications of meaning. This is the rationale for clinical data standards. Up until now, the adoption of such standards has been varied, although they are increasingly advocated in an area where proprietary specifications prevail, and semantic resources are geared to specific purposes and limited by boundaries of languages and jurisdictions. This chapter highlights the need of data standards in the context of the difficult and heterogeneous field of clinical data and the way how they are addressed by terminologies, ontologies and information models. It provides an overview of existing standards and discusses quality and implementation issues. Emphasis is also put on the eStandards methodology, which investigates needs for health data standards, supports the creation of standardised artefacts and defines actions for the implementation of standards.

The word "ontology" was introduced to information systems when only closed-world reasoning systems were available. It was "borrowed" from philosophy, but literal links to its philosophical meaning were explicitly disavowed. Since then, open-world reasoning systems based on description logics have been developed, OWL has become a standard, and philosophical issues have been raised. The result has too often been confusion. The question "What statements are ontological" receives a variety of answers. A clearer vocabulary that is better suited to today's information systems is needed. The project to base ICD-11 on a "Common Ontology" required addressing this confusion. This paper sets out to systematise the lessons of that experience and subsequent discussions. We explore the semantics of open-world and closed-world systems. For specifying knowledge bases and software, we propose "invariants" or, more fully, "the first order invariant part of the background domain knowledge base" as an alternative to the words "ontology" and "ontological." We discuss the role and limitations of OWL and description logics and how they are complementary to closed world systems such as frames and to less formal "knowledge organisation systems". We illustrate why the conventions of classifications such as ICD cannot be formulated directly in OWL, but can be linked to OWL knowledge bases by queries. We contend that while OWL and description logics are major advances for representing invariants and terminologies, they must be combined with other technologies to represent broader background knowledge faithfully. The ICD-11 architecture is one approach. We argue that such hybrid architectures can and should be developed further.

In Western languages the period character is highly ambiguous, due to its double role as sentence delimiter and abbreviation marker. This is particularly relevant in clinical free-texts characterized by numerous anomalies in spelling, punctuation, vocabulary and with a high frequency of short forms. The problem is addressed by two binary classifiers for abbreviation and sentence detection. A support vector machine exploiting a linear kernel is trained on different combinations of feature sets for each classification task. Feature relevance ranking is applied to investigate which features are important for the particular task. The methods are applied to German language texts from a medical record system, authored by specialized physicians. Two collections of 3,024 text snippets were annotated regarding the role of period characters for training and testing. Cohen's kappa resulted in 0.98. For abbreviation and sentence boundary detection we can report an unweighted micro-averaged F-measure using a 10-fold cross validation of 0.97 for the training set. For test set based evaluation we obtained an unweighted micro-averaged F-measure of 0.95 for abbreviation detection and 0.94 for sentence delineation. Language-dependent resources and rules were found to have less impact on abbreviation detection than on sentence delineation. Sentence detection is an important task, which should be performed at the beginning of a text processing pipeline. For the text genre under scrutiny we showed that support vector machines exploiting a linear kernel produce state of the art results for sentence boundary detection. The results are comparable with other sentence boundary detection methods applied to English clinical texts. We identified abbreviation detection as a supportive task for sentence delineation.

SNOMED CT is a clinical terminology that uses logical axioms to provide terms with meaning. This enforces precise agreements about the ontological nature of the entities denoted by the terms, commonly described as ontological commitment. We demonstra