Methods Inf Med - Training health professionals in bioinformatics. Experiences and lessons learned.

Tópicos

{ research(1218) medic(880) student(794) }
{ data(3963) clinic(1234) research(1004) }
{ data(1737) use(1416) pattern(1282) }
{ perform(1367) use(1326) method(1137) }
{ group(2977) signific(1463) compar(1072) }
{ activ(1452) weight(1219) physic(1104) }
{ sequenc(1873) structur(1644) protein(1328) }
{ chang(1828) time(1643) increas(1301) }
{ model(2341) predict(2261) use(1141) }
{ implement(1333) system(1263) develop(1122) }
{ system(1976) rule(880) can(841) }
{ framework(1458) process(801) describ(734) }
{ design(1359) user(1324) use(1319) }
{ howev(809) still(633) remain(590) }
{ can(981) present(881) function(850) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ studi(1410) differ(1259) use(1210) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ time(1939) patient(1703) rate(768) }
{ drug(1928) target(777) effect(648) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ concept(1167) ontolog(924) domain(897) }
{ general(901) number(790) one(736) }
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{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ visual(1396) interact(850) tool(830) }
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{ monitor(1329) mobil(1314) devic(1160) }
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{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
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{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
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{ extract(1171) text(1153) clinic(932) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ risk(3053) factor(974) diseas(938) }
{ 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) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ activ(1138) subject(705) human(624) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: Genomic technologies and particularly bioinformatics have significantly changed biomedical research along the last decade and are being recognized as potential methods for application also in medical practice and public health. There exists a growing need for different collectives of the healthcare sector to receive training in the methods, tools and databases related with these new areas.OBJECTIVES: This paper describes the teaching experience of our department during the last ten years and analyzes past activities designed for teaching bioinformatics to different groups of health professionals. We aim to illustrate the main lessons learned and offer useful clues to other groups interested in setting up training initiatives in bioinformatics for health professionals.METHODS: The group selected several methodologies for the training activities ("face-to-face", online/e-learning) on the basis of three criteria: 1) the target collective, 2) the contents of the course, and 3) its length. Courses were evaluated and the results are hereby presented and discussed.RESULTS: National and international training courses on bioinformatics, biomedical informatics and genomics were developed according to specific requirements defined by the profile of each of the targeted health professional group. These activities provided the students with the necessary skills for better understanding the use of bioinformatics tools and databases and the appropriate way of applying them into specific health domains.CONCLUSIONS: The increasing demand of training courses in new technologies related to genomics and bioinformatics by health professionals provides a good opportunity for the development of tailored courses based on their specific needs, expectations and demands geared to bridge the gap between research and practice and facilitating their everyday work.

Resumo Limpo

ckground genom technolog particular bioinformat signific chang biomed research along last decad recogn potenti method applic also medic practic public health exist grow need differ collect healthcar sector receiv train method tool databas relat new areasobject paper describ teach experi depart last ten year analyz past activ design teach bioinformat differ group health profession aim illustr main lesson learn offer use clue group interest set train initi bioinformat health professionalsmethod group select sever methodolog train activ facetofac onlineelearn basi three criteria target collect content cours length cours evalu result herebi present discussedresult nation intern train cours bioinformat biomed informat genom develop accord specif requir defin profil target health profession group activ provid student necessari skill better understand use bioinformat tool databas appropri way appli specif health domainsconclus increas demand train cours new technolog relat genom bioinformat health profession provid good opportun develop tailor cours base specif need expect demand gear bridg gap research practic facilit everyday work

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