{"id":"https://openalex.org/W7165632165","doi":"https://doi.org/10.48550/arxiv.2606.21689","title":"Clinical Term Extraction using Open-Source Small Language Models","display_name":"Clinical Term Extraction using Open-Source Small Language Models","publication_year":2026,"publication_date":"2026-06-19","ids":{"openalex":"https://openalex.org/W7165632165","doi":"https://doi.org/10.48550/arxiv.2606.21689"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.21689","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21689","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.21689","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056914774","display_name":"Noah Marchal","orcid":"https://orcid.org/0000-0003-2591-3984"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marchal, Noah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034799556","display_name":"William E. Janes","orcid":"https://orcid.org/0000-0002-6502-8130"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Janes, William E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060329217","display_name":"Mihail Popescu","orcid":"https://orcid.org/0000-0002-6145-8096"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Popescu, Mihail","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5057400650","display_name":"Xing Song","orcid":"https://orcid.org/0000-0002-3712-2904"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Xing","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":false,"cited_by_count":0,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.23090000450611115,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.23090000450611115,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.0674000009894371,"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/T12790","display_name":"Nursing Diagnosis and Documentation","score":0.06289999932050705,"subfield":{"id":"https://openalex.org/subfields/2910","display_name":"Issues, ethics and legal aspects"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/json","display_name":"JSON","score":0.5835000276565552},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.569599986076355},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.46230000257492065},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.43880000710487366},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.43290001153945923},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.4113999903202057},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.3774999976158142},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.37450000643730164},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.37439998984336853}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6477000117301941},{"id":"https://openalex.org/C2780416260","wikidata":"https://www.wikidata.org/wiki/Q2063","display_name":"JSON","level":2,"score":0.5835000276565552},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.569599986076355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5162000060081482},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5074999928474426},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.43290001153945923},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.4113999903202057},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.37450000643730164},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35519999265670776},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.34529998898506165},{"id":"https://openalex.org/C3018949938","wikidata":"https://www.wikidata.org/wiki/Q17166101","display_name":"Text messaging","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C110615152","wikidata":"https://www.wikidata.org/wiki/Q1469824","display_name":"Controlled vocabulary","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2953999936580658},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29100000858306885},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.271699994802475},{"id":"https://openalex.org/C56666940","wikidata":"https://www.wikidata.org/wiki/Q788790","display_name":"Documentation","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C2779804580","wikidata":"https://www.wikidata.org/wiki/Q102047","display_name":"Suffix","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.21689","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21689","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.21689","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21689","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.44311442971229553}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Clinical":[0,63],"information":[1],"for":[2,28,157],"amyotrophic":[3],"lateral":[4],"sclerosis":[5],"(ALS)":[6],"care":[7],"documented":[8],"in":[9,36],"unstructured":[10],"clinical":[11,34,59],"notes":[12],"limits":[13],"downstream":[14],"analysis":[15],"without":[16,41],"extraction":[17,166],"into":[18],"structured":[19,79],"formats.":[20],"Open-source":[21],"small":[22],"language":[23],"models":[24,86],"with":[25,74,134],"few-shot":[26],"prompting":[27],"detecting":[29],"the":[30,121,135],"presence":[31],"of":[32,171],"ALS-relevant":[33],"terms":[35],"patient":[37],"documentation":[38],"were":[39],"evaluated":[40],"task-specific":[42],"training":[43],"data.":[44],"The":[45],"detection":[46],"task":[47],"targeted":[48,164],"17":[49],"categories":[50],"spanning":[51],"functional":[52],"scores,":[53],"respiratory":[54],"measures,":[55],"medications,":[56],"and":[57,60,72,90,107,131,152],"related":[58],"non-clinical":[61],"attributes.":[62],"note":[64],"content":[65],"was":[66,120],"normalized":[67],"from":[68],"JSON-encoded":[69],"discharge":[70],"summaries":[71],"processed":[73],"a":[75,99],"prompt":[76],"template":[77],"having":[78],"JSON":[80],"outputs.":[81],"We":[82],"compared":[83],"26":[84],"open-source":[85],"using":[87],"aggregate,":[88],"label-level,":[89],"manual-validation":[91],"multilabel":[92],"classification":[93],"metrics.":[94],"Manual":[95],"validation":[96],"showed":[97],"that":[98],"regex":[100],"rule":[101],"baseline":[102,137],"had":[103],"higher":[104,147],"overall":[105],"micro-F1":[106],"lower":[108,150],"Hamming":[109],"loss":[110],"than":[111,169],"any":[112],"single":[113],"SLM":[114,123],"or":[115],"TF-IDF":[116,136],"baseline,":[117],"while":[118],"Qwen3-4B-Instruct-2507":[119],"highest-performing":[122],"by":[124,129],"micro-F1.":[125],"Model":[126],"rankings":[127],"varied":[128],"metric":[130],"label":[132],"category,":[133],"showing":[138,146,154],"high":[139],"recall":[140],"but":[141,149],"low":[142],"precision,":[143],"some":[144],"SLMs":[145],"precision":[148],"recall,":[151],"Hammer2.1-7b":[153],"strong":[155],"performance":[156],"ALSFRS-R":[158],"subscore":[159],"detection.":[160],"These":[161],"findings":[162],"support":[163],"hybrid":[165],"workflows":[167],"rather":[168],"replacement":[170],"existing":[172],"rule-based":[173],"methods.":[174]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}