Comput Methods Programs Biomed - Supporting adaptive clinical treatment processes through recommendations.

Tópicos

{ clinic(1479) use(1117) guidelin(835) }
{ cancer(2502) breast(956) screen(824) }
{ system(1976) rule(880) can(841) }
{ can(774) often(719) complex(702) }
{ method(1219) similar(1157) match(930) }
{ patient(2315) diseas(1263) diabet(1191) }
{ care(1570) inform(1187) nurs(1089) }
{ method(1557) propos(1049) approach(1037) }
{ visual(1396) interact(850) tool(830) }
{ activ(1138) subject(705) human(624) }
{ imag(1947) propos(1133) code(1026) }
{ control(1307) perform(991) simul(935) }
{ perform(1367) use(1326) method(1137) }
{ medic(1828) order(1363) alert(1069) }
{ use(2086) technolog(871) perceiv(783) }
{ chang(1828) time(1643) increas(1301) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ learn(2355) train(1041) set(1003) }
{ data(1714) softwar(1251) tool(1186) }
{ general(901) number(790) one(736) }
{ featur(1941) imag(1645) propos(1176) }
{ studi(1410) differ(1259) use(1210) }
{ perform(999) metric(946) measur(919) }
{ first(2504) two(1366) second(1323) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
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{ data(1737) use(1416) pattern(1282) }
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{ assess(1506) score(1403) qualiti(1306) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
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{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
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{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVES: Efficient clinical treatment processes is considered a key factor of medical quality control. Current IT solutions are far away from this perspective since they typically have difficulty supporting the variances occurring in clinical practices, and providing adequate flexible support of clinical processes.METHODS: This paper proposes a hybrid approach based on rough set theory and case-based reasoning to allow physicians to rapidly adjust patients' treatment processes to changes of patients' clinical states. In detail, the proposed approach recommends appropriate treatment plans in clinical process execution by adopting a similarity measure to select appropriate clinical treatment plans executed on patients who presented similar features to the current one. Such clinical treatment plans are then applied to suggest which actions to perform next in clinical treatment process execution.RESULTS: As a motivating scenario, this study performs the experiments of type 2 diabetes patient's treatment process. The results show that the proposed approach is feasible to recommend suitable clinical treatment plans in clinical process execution, which makes adaptive clinical treatment processes possible.

Resumo Limpo

jectiv effici clinic treatment process consid key factor medic qualiti control current solut far away perspect sinc typic difficulti support varianc occur clinic practic provid adequ flexibl support clinic processesmethod paper propos hybrid approach base rough set theori casebas reason allow physician rapid adjust patient treatment process chang patient clinic state detail propos approach recommend appropri treatment plan clinic process execut adopt similar measur select appropri clinic treatment plan execut patient present similar featur current one clinic treatment plan appli suggest action perform next clinic treatment process executionresult motiv scenario studi perform experi type diabet patient treatment process result show propos approach feasibl recommend suitabl clinic treatment plan clinic process execut make adapt clinic treatment process possibl

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