Artif Intell Med - Argumentation-logic for creating and explaining medical hypotheses.

Tópicos

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{ imag(1947) propos(1133) code(1026) }
{ patient(2315) diseas(1263) diabet(1191) }
{ framework(1458) process(801) describ(734) }
{ data(1714) softwar(1251) tool(1186) }
{ health(3367) inform(1360) care(1135) }
{ state(1844) use(1261) util(961) }
{ activ(1138) subject(705) human(624) }
{ implement(1333) system(1263) develop(1122) }
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{ process(1125) use(805) approach(778) }
{ problem(2511) optim(1539) algorithm(950) }
{ concept(1167) ontolog(924) domain(897) }
{ studi(1410) differ(1259) use(1210) }
{ group(2977) signific(1463) compar(1072) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
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{ compound(1573) activ(1297) structur(1058) }
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{ 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) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ estim(2440) model(1874) function(577) }
{ 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

JECTIVE: While EIRA has proved to be successful in the detection of anomalous patient responses to treatments in the Intensive Care Unit, it could not describe to clinicians the rationales behind the anomalous detections. The aim of this paper is to address this problem.METHODS: Few attempts have been made in the past to build knowledge-based medical systems that possess both argumentation and explanation capabilities. Here we propose an approach based on Dung's seminal calculus of opposition.RESULTS: We have developed a new tool, arguEIRA, which is an extension of the existing EIRA system. In this paper we extend EIRA by providing it with an argumentation-based justification system that formalizes and communicates to the clinicians the reasons why a patient response is anomalous.CONCLUSION: Our comparative evaluation of the EIRA system against the newly developed tool highlights the multiple benefits that the use of argumentation-logic can bring to the field of medical decision support and explanation.

Resumo Limpo

jectiv eira prove success detect anomal patient respons treatment intens care unit describ clinician rational behind anomal detect aim paper address problemmethod attempt made past build knowledgebas medic system possess argument explan capabl propos approach base dung semin calculus oppositionresult develop new tool argueira extens exist eira system paper extend eira provid argumentationbas justif system formal communic clinician reason patient respons anomalousconclus compar evalu eira system newli develop tool highlight multipl benefit use argumentationlog can bring field medic decis support explan

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