MyMedR

Displaying all 4 publications

Abstract:

Sort:

Introducing SNOMED CT to cardiology, from Malaysia

Sulaiman IM, Sheikh Ahmad MK, Bouzekri K, Ismail D

Eur Heart J, 2015 Jul 7;36(26):1636-9.
PMID: 26366446
Matched MeSH terms: Systematized Nomenclature of Medicine*
From a Categorical Structure to a Concept Model: The International Classification of Health Interventions (ICHI)

Rodrigues JM, Kim S, Aljunid S, Lee JJ, Ten Napel H, Trombert B

Stud Health Technol Inform, 2017;245:1334.
PMID: 29295415

The International Classification of Health Interventions (ICHI) alpha2 2016 Section 1 Interventions on Body Systems and Functions is based on ISO 1828 international standard named categorial Structure (CAST). This is not sufficient to represent the meaning of ICD9-CM Volume 3 labels. We propose to modify it by using the SNOMED CT concept model.

Matched MeSH terms: Systematized Nomenclature of Medicine*
Fulltext Classification of forensic autopsy reports through conceptual graph-based document representation model

Mujtaba G, Shuib L, Raj RG, Rajandram R, Shaikh K, Al-Garadi MA

J Biomed Inform, 2018 06;82:88-105.
PMID: 29738820 DOI: 10.1016/j.jbi.2018.04.013

Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD.

Matched MeSH terms: Systematized Nomenclature of Medicine*
Chronic Disease Registries - Trends and Challenges

Schüz J, Fored M

Methods Inf Med, 2017 Aug 11;56(4):328-329.
PMID: 28726979 DOI: 10.3414/ME17-14-0004

BACKGROUND: This accompanying editorial is an introduction to the focus theme of "chronic disease registries - trends and challenges".
METHODS: A call for papers was announced on the website of Methods of Information in Medicine in April 2016 with submission deadline in September 2016. A peer review process was established to select the papers for the focus theme, managed by two guest editors.
RESULTS: Three papers were selected to be included in the focus theme. Topics range from contributions to patient care through implementation of clinical decision support functionality in clinical registries; analysing similar-purposed acute coronary syndrome registries of two countries and their registry-to-SNOMED CT maps; and data extraction for speciality population registries from electronic health record data rather than manual abstraction.
CONCLUSIONS: The focus theme gives insight into new developments related to disease registration. This applies to technical challenges such as data linkage and data as well as data structure abstraction, but also the utilisation for clinical decision making.

Matched MeSH terms: Systematized Nomenclature of Medicine