Methods Inf Med - Inferring community structure in healthcare forums. An empirical study.

Tópicos

{ health(1844) social(1437) communiti(874) }
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{ sequenc(1873) structur(1644) protein(1328) }
{ network(2748) neural(1063) input(814) }
{ assess(1506) score(1403) qualiti(1306) }
{ ehr(2073) health(1662) electron(1139) }
{ result(1111) use(1088) new(759) }
{ patient(2315) diseas(1263) diabet(1191) }
{ system(1050) medic(1026) inform(1018) }
{ data(2317) use(1299) case(1017) }
{ use(2086) technolog(871) perceiv(783) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ design(1359) user(1324) use(1319) }
{ research(1085) discuss(1038) issu(1018) }
{ model(3404) distribut(989) bayesian(671) }
{ method(1219) similar(1157) match(930) }
{ chang(1828) time(1643) increas(1301) }
{ import(1318) role(1303) understand(862) }
{ blood(1257) pressur(1144) flow(957) }
{ patient(1821) servic(1111) care(1106) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
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{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
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{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

CKGROUND: Detecting community structures in complex networks is a problem interesting to several domains. In healthcare, discovering communities may enhance the quality of web offerings for people with chronic diseases. Understanding the social dynamics and community attachments is key to predicting and influencing interaction and information flow to the right patients.OBJECTIVES: The goal of the study is to empirically assess the extent to which we can infer meaningful community structures from implicit networks of peer interaction in online healthcare forums.METHODS: We used datasets from five online diabetes forums to design networks based on peer-interactions. A quality function based on user interaction similarity was used to assess the quality of the discovered communities to complement existing homophily measures.RESULTS: Results show that we can infer meaningful communities by observing forum interactions. Closely similar users tended to co-appear in the top communities, suggesting the discovered communities are intuitive. The number of years since diagnosis was a significant factor for cohesiveness in some diabetes communities.CONCLUSION: Network analysis is a tool that can be useful in studying implicit networks that form in healthcare forums. Current analysis informs further work on predicting and influencing interaction, information flow and user interests that could be useful for personalizing medical social media.

Resumo Limpo

ckground detect communiti structur complex network problem interest sever domain healthcar discov communiti may enhanc qualiti web offer peopl chronic diseas understand social dynam communiti attach key predict influenc interact inform flow right patientsobject goal studi empir assess extent can infer meaning communiti structur implicit network peer interact onlin healthcar forumsmethod use dataset five onlin diabet forum design network base peerinteract qualiti function base user interact similar use assess qualiti discov communiti complement exist homophili measuresresult result show can infer meaning communiti observ forum interact close similar user tend coappear top communiti suggest discov communiti intuit number year sinc diagnosi signific factor cohes diabet communitiesconclus network analysi tool can use studi implicit network form healthcar forum current analysi inform work predict influenc interact inform flow user interest use person medic social media

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