AMIA Annu Symp Proc - Measuring the Information Gain of Diagnosis vs. Diagnosis Category Coding.

Tópicos

{ use(976) code(926) identifi(902) }
{ extract(1171) text(1153) clinic(932) }
{ concept(1167) ontolog(924) domain(897) }
{ activ(1452) weight(1219) physic(1104) }
{ network(2748) neural(1063) input(814) }
{ case(1353) use(1143) diagnosi(1136) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ model(2220) cell(1177) simul(1124) }
{ record(1888) medic(1808) patient(1693) }
{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ decis(3086) make(1611) patient(1517) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ take(945) account(800) differ(722) }
{ treatment(1704) effect(941) patient(846) }
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{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ 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) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Coding categories of diseases, injuries, symptoms, findings, etc. with ICD-9-CM necessarily imparts a loss of information vs. coding such entities with a terminology or ontology-a consequence of the nature of classifications. However, to our knowledge, no one has attempted to quantify this information loss or conversely, the information to be gained by coding entities as opposed to categories. We estimated a lower bound on information gain of coding with SNOMED CT instead of ICD-9-CM, as measured by Shannon's information entropy. We found that the nation could gain more than 97 megabytes of information per year by coding diagnoses vs. diagnosis categories, an increase of 10%. This increase is more than that obtained from coding ICD-9-CM at the 5(th) instead of the 3(rd) digit level. We recommend that ICD-9-CM be removed from electronic medical record (EMR) stage 2 and later meaningful use criteria.

Resumo Limpo

code categori diseas injuri symptom find etc icdcm necessarili impart loss inform vs code entiti terminolog ontologya consequ natur classif howev knowledg one attempt quantifi inform loss convers inform gain code entiti oppos categori estim lower bound inform gain code snome ct instead icdcm measur shannon inform entropi found nation gain megabyt inform per year code diagnos vs diagnosi categori increas increas obtain code icdcm th instead rd digit level recommend icdcm remov electron medic record emr stage later meaning use criteria

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