ArticleProceedings. AMIA Symposium2000
Automatic coding of reasons for hospital referral from general medicine free-text reports.
Article in Proceedings. AMIA Symposium, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Toward unsupervised outbreak detection through visual perception of new patterns.BMC public health · 2009Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although the coding of medical data is expected to benefit both patients and the health care system, its implementation as a manual process often represents a poorly attractive workload for the physician. For epidemiological purpose, we developed a simple automatic coding system based on string matching, which was designed to process free-text sentences stating reasons for hospital referral, as collected from general practitioners (GPs). This system relied on a look-up table, built up from 2590 reports giving a single reason for referral, which were coded manually according to the International Classification of Primary Care (ICPC). We tested the system by entering 797 new reasons for referral. The match rate was estimated at 77%, and the accuracy rate, at 80% at code level and 92% at chapter level. This simple system is now routinely used by a national epidemiological network of sentinel physicians.
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11079931PMC2243796What OpenQuestion holds
Registered trials
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