Artif Intell Med - Automated interviews on clinical case reports to elicit directed acyclic graphs.

Tópicos

{ record(1888) medic(1808) patient(1693) }
{ survey(1388) particip(1329) question(1065) }
{ data(2317) use(1299) case(1017) }
{ concept(1167) ontolog(924) domain(897) }
{ structur(1116) can(940) graph(676) }
{ studi(1119) effect(1106) posit(819) }
{ featur(1941) imag(1645) propos(1176) }
{ framework(1458) process(801) describ(734) }
{ use(976) code(926) identifi(902) }
{ problem(2511) optim(1539) algorithm(950) }
{ extract(1171) text(1153) clinic(932) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ first(2504) two(1366) second(1323) }
{ use(1733) differ(960) four(931) }
{ model(3404) distribut(989) bayesian(671) }
{ studi(2440) review(1878) systemat(933) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ control(1307) perform(991) simul(935) }
{ howev(809) still(633) remain(590) }
{ health(1844) social(1437) communiti(874) }
{ can(774) often(719) complex(702) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ model(2341) predict(2261) use(1141) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ can(981) present(881) function(850) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ network(2748) neural(1063) input(814) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
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{ patient(2837) hospit(1953) medic(668) }
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{ patient(2315) diseas(1263) diabet(1191) }
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{ spatial(1525) area(1432) region(1030) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ high(1669) rate(1365) level(1280) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVE: Setting up clinical reports within hospital information systems makes it possible to record a variety of clinical presentations. Directed acyclic graphs (Dags) offer a useful way of representing causal relations in clinical problem domains and are at the core of many probabilistic models described in the medical literature, like Bayesian networks. However, medical practitioners are not usually trained to elicit Dag features. Part of the difficulty lies in the application of the concept of direct causality before selecting all the causal variables of interest for a specific patient. We designed an automated interview to tutor medical doctors in the development of Dags to represent their understanding of clinical reports.METHODS: Medical notions were analyzed to find patterns in medical reasoning that can be followed by algorithms supporting the elicitation of causal Dags. Clinical relevance was defined to help formulate only relevant questions by driving an expert's attention towards variables causally related to nodes already inserted in the graph. Key procedural features of the proposed interview are described by four algorithms.RESULTS: The automated interview comprises questions on medical notions, phrased in medical terms. The first elicitation session produces questions concerning the patient's chief complaints and the outcomes related to diseases serving as diagnostic hypotheses, their observable manifestations and risk factors. The second session focuses on questions that refine the initial causal paths by considering syndromes, dysfunctions, pathogenic anomalies, biases and effect modifiers. A case study concerning a gastro-enterological problem and one dealing with an infected patient illustrate the output produced by the algorithms, depending on the answers provided by the doctor.CONCLUSIONS: The proposed elicitation framework is characterized by strong consistency with medical background and by a progressive introduction of relevant medical topics. Revision and testing of the subjectively elicited Dag is performed by matching the collected answers with the evidence included in accepted sources of biomedical knowledge.

Resumo Limpo

jectiv set clinic report within hospit inform system make possibl record varieti clinic present direct acycl graph dag offer use way repres causal relat clinic problem domain core mani probabilist model describ medic literatur like bayesian network howev medic practition usual train elicit dag featur part difficulti lie applic concept direct causal select causal variabl interest specif patient design autom interview tutor medic doctor develop dag repres understand clinic reportsmethod medic notion analyz find pattern medic reason can follow algorithm support elicit causal dag clinic relev defin help formul relev question drive expert attent toward variabl causal relat node alreadi insert graph key procedur featur propos interview describ four algorithmsresult autom interview compris question medic notion phrase medic term first elicit session produc question concern patient chief complaint outcom relat diseas serv diagnost hypothes observ manifest risk factor second session focus question refin initi causal path consid syndrom dysfunct pathogen anomali bias effect modifi case studi concern gastroenterolog problem one deal infect patient illustr output produc algorithm depend answer provid doctorconclus propos elicit framework character strong consist medic background progress introduct relev medic topic revis test subject elicit dag perform match collect answer evid includ accept sourc biomed knowledg

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