AMIA Annu Symp Proc - Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases.

Tópicos

{ case(1353) use(1143) diagnosi(1136) }
{ detect(2391) sensit(1101) algorithm(908) }
{ data(3963) clinic(1234) research(1004) }
{ record(1888) medic(1808) patient(1693) }
{ ehr(2073) health(1662) electron(1139) }
{ error(1145) method(1030) estim(1020) }
{ extract(1171) text(1153) clinic(932) }
{ first(2504) two(1366) second(1323) }
{ result(1111) use(1088) new(759) }
{ algorithm(1844) comput(1787) effici(935) }
{ structur(1116) can(940) graph(676) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2675) segment(2577) method(1081) }
{ learn(2355) train(1041) set(1003) }
{ method(1557) propos(1049) approach(1037) }
{ perform(999) metric(946) measur(919) }
{ research(1218) medic(880) student(794) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ high(1669) rate(1365) level(1280) }
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{ imag(1947) propos(1133) code(1026) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

Identification of a cohort of patients with specific diseases is an important step for clinical research that is based on electronic health records (EHRs). Informatics approaches combining structured EHR data, such as billing records, with narrative text data have demonstrated utility for such tasks. This paper describes an algorithm combining machine learning and natural language processing to detect patients with colorectal cancer (CRC) from entire EHRs at Vanderbilt University Hospital. We developed a general case detection method that consists of two steps: 1) extraction of positive CRC concepts from all clinical notes (document-level concept identification); and 2) determination of CRC cases using aggregated information from both clinical narratives and structured billing data (patient-level case determination). For each step, we compared performance of rule-based and machine-learning-based approaches. Using a manually reviewed data set containing 300 possible CRC patients (150 for training and 150 for testing), we showed that our method achieved F-measures of 0.996 for document level concept identification, and 0.93 for patient level case detection.

Resumo Limpo

identif cohort patient specif diseas import step clinic research base electron health record ehr informat approach combin structur ehr data bill record narrat text data demonstr util task paper describ algorithm combin machin learn natur languag process detect patient colorect cancer crc entir ehr vanderbilt univers hospit develop general case detect method consist two step extract posit crc concept clinic note documentlevel concept identif determin crc case use aggreg inform clinic narrat structur bill data patientlevel case determin step compar perform rulebas machinelearningbas approach use manual review data set contain possibl crc patient train test show method achiev fmeasur document level concept identif patient level case detect

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