{"id":"https://openalex.org/W4393063492","doi":"https://doi.org/10.48550/arxiv.2403.13369","title":"Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting","display_name":"Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting","publication_year":2024,"publication_date":"2024-03-20","ids":{"openalex":"https://openalex.org/W4393063492","doi":"https://doi.org/10.48550/arxiv.2403.13369"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2403.13369","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.13369","pdf_url":"https://arxiv.org/pdf/2403.13369","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2403.13369","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085813595","display_name":"Phillip Richter-Pechanski","orcid":"https://orcid.org/0000-0003-0121-373X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Richter-Pechanski, Phillip","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031301838","display_name":"Philipp Wiesenbach","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wiesenbach, Philipp","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032473028","display_name":"Dominic M. Schwab","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schwab, Dominic M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051871322","display_name":"Christina Kiriakou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kiriakou, Christina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062293789","display_name":"Nicolas A. Geis","orcid":"https://orcid.org/0000-0003-3431-3104"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geis, Nicolas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083154676","display_name":"Christoph Dieterich","orcid":"https://orcid.org/0000-0001-9468-6311"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dieterich, Christoph","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5023977688","display_name":"Anette Frank","orcid":"https://orcid.org/0000-0003-4706-9817"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frank, Anette","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9678000211715698,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13629","display_name":"Text Readability and Simplification","score":0.9243999719619751,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6515592336654663},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.5437889695167542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5353397130966187},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5195786952972412},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4984726905822754},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.4752994477748871},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.46233317255973816},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.0685361921787262}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6515592336654663},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.5437889695167542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5353397130966187},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5195786952972412},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4984726905822754},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.4752994477748871},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.46233317255973816},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0685361921787262},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2403.13369","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.13369","pdf_url":"https://arxiv.org/pdf/2403.13369","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2403.13369","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2403.13369","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2403.13369","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.13369","pdf_url":"https://arxiv.org/pdf/2403.13369","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4393063492.pdf","grobid_xml":"https://content.openalex.org/works/W4393063492.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2074502265","https://openalex.org/W4214877189","https://openalex.org/W2773965352","https://openalex.org/W2381179799","https://openalex.org/W2980279061","https://openalex.org/W2334685461","https://openalex.org/W2366718574","https://openalex.org/W2359774528","https://openalex.org/W4298312966","https://openalex.org/W2368651715"],"abstract_inverted_index":{"Automatic":[0],"extraction":[1,136],"of":[2,13,19,62,91,102],"medical":[3],"information":[4,135],"from":[5],"clinical":[6,15,134],"documents":[7],"poses":[8],"several":[9],"challenges:":[10],"high":[11],"costs":[12],"required":[14],"expertise,":[16],"limited":[17],"interpretability":[18,52,101],"model":[20,103,122],"predictions,":[21],"restricted":[22],"computational":[23],"resources":[24],"and":[25,32,97],"privacy":[26],"regulations.":[27],"Recent":[28],"advances":[29],"in":[30,65],"domain-adaptation":[31],"prompting":[33],"methods":[34,64],"showed":[35],"promising":[36],"results":[37,127],"with":[38,114,139],"minimal":[39],"training":[40,94],"data":[41,95],"using":[42],"lightweight":[43],"masked":[44],"language":[45],"models,":[46],"which":[47],"are":[48,55],"suited":[49],"for":[50,133],"well-established":[51],"methods.":[53],"We":[54,78,105],"first":[56],"to":[57,87,98],"present":[58],"a":[59,66,108,119,130],"systematic":[60],"evaluation":[61],"these":[63],"low-resource":[67],"setting,":[68],"by":[69,84,123],"performing":[70],"multi-class":[71],"section":[72],"classification":[73,121],"on":[74],"German":[75],"doctor's":[76],"letters.":[77],"conduct":[79],"extensive":[80],"class-wise":[81],"evaluations":[82],"supported":[83],"Shapley":[85],"values,":[86],"validate":[88],"the":[89,100],"quality":[90],"our":[92],"small":[93],"set":[96],"ensure":[99],"predictions.":[104],"demonstrate":[106],"that":[107],"lightweight,":[109],"domain-adapted":[110],"pretrained":[111],"model,":[112],"prompted":[113],"just":[115],"20":[116],"shots,":[117],"outperforms":[118],"traditional":[120],"30.5%":[124],"accuracy.":[125],"Our":[126],"serve":[128],"as":[129],"process-oriented":[131],"guideline":[132],"projects":[137],"working":[138],"low-resource.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}
