{"id":"https://openalex.org/W7160954583","doi":"https://doi.org/10.48550/arxiv.2605.09440","title":"Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports","display_name":"Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports","publication_year":2026,"publication_date":"2026-05-10","ids":{"openalex":"https://openalex.org/W7160954583","doi":"https://doi.org/10.48550/arxiv.2605.09440"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09440","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09440","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.09440","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135983239","display_name":"Yu Wang","orcid":"https://orcid.org/0000-0002-8683-6196"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135478539","display_name":"Yingyun Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yingyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135971779","display_name":"Ying Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125288265","display_name":"Haiyang Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Haiyang","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/T13702","display_name":"Machine Learning in Healthcare","score":0.3797000050544739,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.3797000050544739,"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/T10028","display_name":"Topic Modeling","score":0.21719999611377716,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.11909999698400497,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.8258000016212463},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5182999968528748},{"id":"https://openalex.org/keywords/alias","display_name":"Alias","score":0.36550000309944153},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.3628999888896942},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.3580999970436096},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.33709999918937683}],"concepts":[{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.8258000016212463},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311999797821045},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5182999968528748},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.43459999561309814},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4235999882221222},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40049999952316284},{"id":"https://openalex.org/C46681722","wikidata":"https://www.wikidata.org/wiki/Q4725589","display_name":"Alias","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C2776841711","wikidata":"https://www.wikidata.org/wiki/Q856","display_name":"Barcode","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09440","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09440","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.09440","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09440","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"score":0.47933313250541687,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"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],"reports":[1,32,156,235],"are":[2,77,109,190],"often":[3,27],"fragmented":[4],"across":[5],"healthcare":[6,85],"institutions":[7],"because":[8,74],"privacy":[9],"regulations":[10],"and":[11,40,43,61,83,133,137,176,181,236,249],"data":[12],"silos":[13],"limit":[14],"direct":[15],"information":[16],"sharing.":[17],"When":[18],"patients":[19],"seek":[20],"care":[21],"at":[22],"a":[23,123,142,149,199,211],"different":[24],"hospital,":[25],"they":[26],"carry":[28],"paper":[29],"or":[30],"scanned":[31],"from":[33,157],"prior":[34],"visits.":[35],"This":[36],"hinders":[37],"EHR":[38],"integration":[39],"longitudinal":[41],"review,":[42],"downstream":[44],"applications":[45],"that":[46,195],"depend":[47],"on":[48,154,227],"more":[49,158],"complete":[50],"patient":[51,55],"records,":[52],"such":[53,68],"as":[54,95,141],"management,":[56],"follow-up":[57],"care,":[58],"real-world":[59,155],"studies,":[60],"clinical-trial":[62],"matching.":[63],"Although":[64,218],"OCR":[65,79],"can":[66,237],"digitize":[67],"reports,":[69],"reliable":[70],"extraction":[71],"remains":[72],"challenging":[73],"clinical":[75,103,234],"documents":[76],"heterogeneous,":[78],"text":[80],"is":[81,119,198,222],"noisy,":[82],"many":[84],"settings":[86,242],"require":[87],"low-cost":[88],"on-premise":[89],"deployment.":[90],"We":[91,121],"formulate":[92],"this":[93],"problem":[94],"canonical":[96,124,188,246],"key-conditioned":[97],"extractive":[98],"question":[99],"answering":[100],"over":[101],"OCR-derived":[102],"reports.":[104],"Because":[105],"the":[106,116,186,224,228],"key":[107,117,125,129,139,167,196,247],"fields":[108],"neither":[110],"fixed":[111],"nor":[112],"known":[113],"in":[114],"advance,":[115],"space":[118],"open.":[120],"maintain":[122],"inventory":[126,146,248],"through":[127],"iterative":[128],"mining,":[130],"normalization,":[131],"clustering,":[132],"lightweight":[134],"human":[135],"verification,":[136],"introduce":[138],"coverage":[140,197],"metric":[143],"to":[144,240],"quantify":[145],"completeness.":[147],"Using":[148],"0.2B":[150],"BERT-based":[151],"model,":[152],"experiments":[153],"than":[159],"20":[160],"hospitals":[161],"show":[162,194],"performance":[163],"improves":[164],"monotonically":[165],"with":[166],"coverage.":[168],"The":[169],"model":[170,209],"achieves":[171],"F1":[172],"scores":[173],"of":[174,232],"0.839":[175],"0.893":[177],"under":[178,215],"exact":[179,216],"match":[180],"boundary-tolerant":[182],"matching,":[183],"respectively,":[184],"once":[185],"Top-90":[187,206],"keys":[189],"covered.":[191],"These":[192],"results":[193],"dominant":[200],"factor":[201],"for":[202],"end-to-end":[203],"performance.":[204],"At":[205],"coverage,":[207],"our":[208,219],"outperforms":[210],"fine-tuned":[212],"Qwen3-0.6B":[213],"baseline":[214],"match.":[217],"annotated":[220],"corpus":[221],"Chinese,":[223],"method":[225],"relies":[226],"language-agnostic":[229],"key-value":[230],"organization":[231],"semi-structured":[233],"be":[238],"adapted":[239],"other":[241],"given":[243],"an":[244],"appropriate":[245],"alias":[250],"mapping.":[251]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}