BMC Med Inform Decis Mak - The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ record(1888) medic(1808) patient(1693) }
{ risk(3053) factor(974) diseas(938) }
{ cancer(2502) breast(956) screen(824) }
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Resumo

CKGROUND: Electronic health records are invaluable for medical research, but much information is stored as free text rather than in a coded form. For example, in the UK General Practice Research Database (GPRD), causes of death and test results are sometimes recorded only in free text. Free text can be difficult to use for research if it requires time-consuming manual review. Our aim was to develop an automated method for extracting coded information from free text in electronic patient records.METHODS: We reviewed the electronic patient records in GPRD of a random sample of 3310 patients who died in 2001, to identify the cause of death. We developed a computer program called the Freetext Matching Algorithm (FMA) to map diagnoses in text to the Read Clinical Terminology. The program uses lookup tables of synonyms and phrase patterns to identify diagnoses, dates and selected test results. We tested it on two random samples of free text from GPRD (1000 texts associated with death in 2001, and 1000 general texts from cases and controls in a coronary artery disease study), comparing the output to the U.S. National Library of Medicine's MetaMap program and the gold standard of manual review.RESULTS: Among 3310 patients registered in the GPRD who died in 2001, the cause of death was recorded in coded form in 38.1% of patients, and in the free text alone in 19.4%. On the 1000 texts associated with death, FMA coded 683 of the 735 positive diagnoses, with precision (positive predictive value) 98.4% (95% confidence interval (CI) 97.2, 99.2) and recall (sensitivity) 92.9% (95% CI 90.8, 94.7). On the general sample, FMA detected 346 of the 447 positive diagnoses, with precision 91.5% (95% CI 88.3, 94.1) and recall 77.4% (95% CI 73.2, 81.2), which was similar to MetaMap.CONCLUSIONS: We have developed an algorithm to extract coded information from free text in GP records with good precision. It may facilitate research using free text in electronic patient records, particularly for extracting the cause of death.

Resumo Limpo

ckground electron health record invalu medic research much inform store free text rather code form exampl uk general practic research databas gprd caus death test result sometim record free text free text can difficult use research requir timeconsum manual review aim develop autom method extract code inform free text electron patient recordsmethod review electron patient record gprd random sampl patient die identifi caus death develop comput program call freetext match algorithm fma map diagnos text read clinic terminolog program use lookup tabl synonym phrase pattern identifi diagnos date select test result test two random sampl free text gprd text associ death general text case control coronari arteri diseas studi compar output us nation librari medicin metamap program gold standard manual reviewresult among patient regist gprd die caus death record code form patient free text alon text associ death fma code posit diagnos precis posit predict valu confid interv ci recal sensit ci general sampl fma detect posit diagnos precis ci recal ci similar metamapconclus develop algorithm extract code inform free text gp record good precis may facilit research use free text electron patient record particular extract caus death

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