Literature DB >> 8847119

A new drug classification for computer systems: the ATC extension code.

G C Miller1, H Britt.   

Abstract

During the testing of the Read Clinical Codes in general practice medical records in Australia, it became apparent that the pharmaceutical section of the codes was not applicable in a country with different brand names, pack sizes and forms. For pharmacoepidemiological studies, structured classification of both morbidity and pharmaceuticals is required for meaningful analysis. The search for a suitable pharmaceutical classification proved fruitless. While the Australian Government has recently adopted the Anatomical Therapeutic Chemical (ATC) Classification as the national standard, this only classifies drugs to the generic level. None of the extended coding systems used in hospital pharmacies, by community pharmacists, or by Government are hierarchically structured. The extension code we have developed, is an analytical algorithm comprising independent fields for: dosage; strength; manufacturer and brand; and pack size. The codes within each field are also structured in a hierarchical manner. The result is an extension code of 21 digits, each digit or group of digits having a meaning. The structure of this classification will allow analysis of any aspect of the drug prescribed. This system is designed for computerised entry of text and transparent coding of the data--not for manual coding on paper nor manual code entry to the computer.

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Year:  1995        PMID: 8847119     DOI: 10.1016/0020-7101(95)01135-2

Source DB:  PubMed          Journal:  Int J Biomed Comput        ISSN: 0020-7101


  16 in total

1.  Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study.

Authors:  Véronique Pizzoglio; Ismaïl Ahmed; Pascal Auriche; Pascale Tuber-Bitter; Françoise Haramburu; Carmen Kreft-Jaïs; Ghada Miremont-Salamé
Journal:  Eur J Clin Pharmacol       Date:  2011-12-06       Impact factor: 2.953

2.  Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics.

Authors:  Robert Hoehndorf; Michel Dumontier; Georgios V Gkoutos
Journal:  Bioinformatics       Date:  2012-06-17       Impact factor: 6.937

3.  Trends in spontaneous adverse drug reaction reports to the French pharmacovigilance system (1986-2001).

Authors:  Frantz Thiessard; Emmanuel Roux; Ghada Miremont-Salamé; Annie Fourrier-Réglat; Françoise Haramburu; Pascale Tubert-Bitter; Bernard Bégaud
Journal:  Drug Saf       Date:  2005       Impact factor: 5.606

4.  Early detection of pharmacovigilance signals with automated methods based on false discovery rates: a comparative study.

Authors:  Ismaïl Ahmed; Frantz Thiessard; Ghada Miremont-Salamé; Françoise Haramburu; Carmen Kreft-Jais; Bernard Bégaud; Pascale Tubert-Bitter
Journal:  Drug Saf       Date:  2012-06-01       Impact factor: 5.606

5.  Traditional knowledge of medicinal plants in the Serra de Mariola Natural Park, South-Eastern Spain.

Authors:  A Belda; B Zaragozí; I Belda; Je Martínez; E Seva
Journal:  Afr J Tradit Complement Altern Med       Date:  2012-12-31

6.  Determinants for drug prescribing to children below the minimum licensed age.

Authors:  Geert W 't Jong; Ingo A Eland; Miriam C J M Sturkenboom; John N van den Anker; Bruno H C Stricker
Journal:  Eur J Clin Pharmacol       Date:  2003-02-06       Impact factor: 2.953

7.  Generation and validation of algorithms to identify subjects with dementia using administrative data.

Authors:  Jacopo C DiFrancesco; Alessandra Pina; Giorgia Giussani; Laura Cortesi; Elisa Bianchi; Luca Cavalieri d'Oro; Emanuele Amodio; Alessandro Nobili; Lucio Tremolizzo; Valeria Isella; Ildebrando Appollonio; Carlo Ferrarese; Ettore Beghi
Journal:  Neurol Sci       Date:  2019-06-12       Impact factor: 3.307

8.  Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.

Authors:  Xinyu Dong; Jianyuan Deng; Sina Rashidian; Kayley Abell-Hart; Wei Hou; Richard N Rosenthal; Mary Saltz; Joel H Saltz; Fusheng Wang
Journal:  J Am Med Inform Assoc       Date:  2021-07-30       Impact factor: 4.497

9.  Mouse model phenotypes provide information about human drug targets.

Authors:  Robert Hoehndorf; Tanya Hiebert; Nigel W Hardy; Paul N Schofield; Georgios V Gkoutos; Michel Dumontier
Journal:  Bioinformatics       Date:  2013-10-24       Impact factor: 6.937

10.  A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.

Authors:  Jean-François Ethier; Olivier Dameron; Vasa Curcin; Mark M McGilchrist; Robert A Verheij; Theodoros N Arvanitis; Adel Taweel; Brendan C Delaney; Anita Burgun
Journal:  J Am Med Inform Assoc       Date:  2013-04-09       Impact factor: 4.497

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