@article {1127,
	title = {Evaluating Open-Source Full-Text Search Engines for Matching ICD-10 Codes.},
	journal = {Stud Health Technol Inform},
	volume = {226},
	year = {2016},
	month = {2016},
	pages = {127-30},
	abstract = {<p>This research presents the results of evaluating multiple free, open-source engines on matching ICD-10 diagnostic codes via full-text searches. The study investigates what it takes to get an accurate match when searching for a specific diagnostic code. For each code the evaluation starts by extracting the words that make up its text and continues with building full-text search queries from the combinations of these words. The queries are then run against all the ICD-10 codes until a match indicates the code in question as a match with the highest relative score. This method identifies the minimum number of words that must be provided in order for the search engines choose the desired entry. The engines analyzed include a popular Java-based full-text search engine, a lightweight engine written in JavaScript which can even execute on the user{\textquoteright}s browser, and two popular open-source relational database management systems.</p>},
	issn = {0926-9630},
	author = {Jurc{\u a}u, Daniel-Alexandru and Stoicu-Tivadar, Vasile}
}
