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This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records. In the second stage, an extension of the NegEx negation detection algorithm is developed and employed for detecting the negated records. The third stage involves identifying the temporal status of the substance use by applying windowing and chunking methodologies. Finally, in the fourth stage, regular expressions, syntactic patterns, and keyword search techniques are used in order to extract the substance-use attributes. The proposed system achieves an F1-score of up to 0.99 for identifying substance-use-related records, 0.98 for detecting the negation status, and 0.94 for identifying temporal status. Moreover, F1-scores of up to 0.98, 0.98, 1.00, 0.92, and 0.98 are achieved for the extraction of the amount, frequency, type, quit-time, and period attributes, respectively. Natural Language Processing (NLP) and rule-based techniques are employed efficiently for extracting substance-use status and attributes, with the proposed system being able to detect substance-use status and attributes over both sentence-level and document-level data. Results show that the proposed system outperforms the compared state-of-the-art substance-use identification system on an unseen dataset, demonstrating its generalisability. [ABSTRACT FROM AUTHOR]

By now you have probably received the call for papers for the 7th UKSS International Conference to be held from 7 to 10 July 2002, at the University of York. [...]

When using current object-oriented methods in the development of computer-based information systems, problems frequently arise in the identification and specification of ‘objects’. In this paper, these problems are discussed and an approach that draws upon interpretivist systems thinking is presented that, it is argued, may alleviate some of the problems. It is proposed that a method that has been developed specifically to aid the user and analyst to appreciate a perceived problem situation might provide a useful aid in the process of identifying objects from the situation which are meaningful to the user. [An attempt has been made to use terminology that is consistent with that used in object-oriented analysis. However, at times this terminology may be at variance with some of the interpretivist ideas put forward (e.g. the term ‘user’ is adopted, although the authors argue that the notion of ‘client’ is more appropriate to emphasize the increased involvement and responsibility of the ‘user’ in the approach advocated)]. The ability to achieve this is expected to facilitate the appropriate mapping of these meaningful objects into a technical specification through object-oriented methods and to encourage user acceptance of the resulting computer-based information system. [ABSTRACT FROM AUTHOR]

Electronic Health Records (EHR) are a rich repository of valuable clinical information that exist in primary and secondary care databases. In order to utilize EHRs for medical observational research a range of algorithms for automatically identifying individuals with a specific phenotype have been developed. This review summarizes and offers a critical evaluation of the literature relating to studies conducted into the development of EHR phenotyping systems. This review describes phenotyping systems and techniques based on structured and unstructured EHR data. Articles published on PubMed and Google scholar between 2013 and 2017 have been reviewed, using search terms derived from Medical Subject Headings (MeSH). The popularity of using Natural Language Processing (NLP) techniques in extracting features from narrative text has increased. This increased attention is due to the availability of open source NLP algorithms, combined with accuracy improvement. In this review, Concept extraction is the most popular NLP technique since it has been used by more than 50% of the reviewed papers to extract features from EHR. High-throughput phenotyping systems using unsupervised machine learning techniques have gained more popularity due to their ability to efficiently and automatically extract a phenotype with minimal human effort. [ABSTRACT FROM AUTHOR]

Provides information on two conferences regarding systems theory. Systems Theory and Practice in the Knowledge Age at the University of York in England from July 7 through 10, 2002; International Conference on Systems Thinking Globally Concerned at the University of Vienna and Vienna University of Technology in Austria from November 1 through 4, 2001.