{"id":"https://openalex.org/W7165368702","doi":"https://doi.org/10.48550/arxiv.2606.19680","title":"ImProNCDE: Impulse-Corrected Neural Controlled Differential Equations with Prototype Learning for Longitudinal Prognosis Prediction","display_name":"ImProNCDE: Impulse-Corrected Neural Controlled Differential Equations with Prototype Learning for Longitudinal Prognosis Prediction","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165368702","doi":"https://doi.org/10.48550/arxiv.2606.19680"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.19680","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19680","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.19680","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138979235","display_name":"Hao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138991345","display_name":"Yupeng Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yupeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138991234","display_name":"Jinghao Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Jinghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138999578","display_name":"Shuchang Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Shuchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139009857","display_name":"Yige Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Yige","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139011044","display_name":"Jinman Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Jinman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139010891","display_name":"Kun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101948335","display_name":"Li Bi","orcid":"https://orcid.org/0000-0003-2591-7571"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bi, Lei","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/T11438","display_name":"Retinal Imaging and Analysis","score":0.22290000319480896,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.22290000319480896,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.09679999947547913,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.08590000122785568,"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/trajectory","display_name":"Trajectory","score":0.522599995136261},{"id":"https://openalex.org/keywords/impulse","display_name":"Impulse (physics)","score":0.48750001192092896},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.47519999742507935},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.3928999900817871},{"id":"https://openalex.org/keywords/impulse-response","display_name":"Impulse response","score":0.3837999999523163},{"id":"https://openalex.org/keywords/differential-equation","display_name":"Differential equation","score":0.3776000142097473},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.3675999939441681},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.35429999232292175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5924000144004822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5338000059127808},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.522599995136261},{"id":"https://openalex.org/C70836080","wikidata":"https://www.wikidata.org/wiki/Q837940","display_name":"Impulse (physics)","level":2,"score":0.48750001192092896},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.47519999742507935},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36410000920295715},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C3019921246","wikidata":"https://www.wikidata.org/wiki/Q104853310","display_name":"Differential effects","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27459999918937683},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C207886595","wikidata":"https://www.wikidata.org/wiki/Q1456138","display_name":"Pathological","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.19680","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19680","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.19680","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19680","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5813441872596741,"id":"https://metadata.un.org/sdg/10"},{"display_name":"Peace, Justice and strong institutions","score":0.4095008969306946,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Longitudinal":[0],"ophthalmic":[1,12,129,209],"imaging":[2],"analysis":[3],"is":[4,77],"an":[5,120],"essential":[6],"step":[7],"for":[8,38,53,127],"prognosis":[9,16,34,130,190],"prediction":[10],"in":[11,65],"diseases.":[13],"However,":[14],"AI-assisted":[15],"models":[17],"are":[18],"challenged":[19],"by":[20,85],"follow-up":[21,67,92],"sequences,":[22],"which":[23,103,147,182,197],"tend":[24],"to":[25,80,184,192],"be":[26],"sparse,":[27],"irregularly":[28],"sampled,":[29],"and":[30,55,109,155,196,206],"incomplete.":[31],"Although":[32],"advanced":[33],"modeling":[35],"methods,":[36],"especially":[37],"the":[39,70,158],"methods":[40,221],"based":[41],"on":[42,203,223],"neural":[43],"controlled":[44],"differential":[45],"equations":[46],"(NCDEs),":[47],"provide":[48],"a":[49,177],"principled":[50],"continuous-time":[51],"framework":[52,123],"sparse":[54],"irregular":[56],"longitudinal":[57,128,208],"data.":[58],"Unfortunately,":[59],"two":[60],"major":[61],"concerns":[62],"remain":[63],"unsolved":[64],"clinical":[66],"modeling.":[68,225],"First,":[69],"smooth":[71,138],"latent":[72,107,139,159,186],"dynamics":[73],"of":[74],"standard":[75],"NCDEs":[76],"poorly":[78],"matched":[79],"abrupt":[81,134],"pathological":[82,135],"changes":[83,136],"induced":[84],"therapeutic":[86],"intervention,":[87],"lesion":[88],"recurrence,":[89],"or":[90],"long":[91,98,173],"gaps.":[93],"Second,":[94],"numerical":[95],"integration":[96],"over":[97,172,212],"horizons":[99],"can":[100],"accumulate":[101],"errors,":[102],"will":[104],"produce":[105],"unstable":[106],"trajectories":[108,187],"weakened":[110],"class":[111,194],"discrimination.":[112],"To":[113,132,167],"address":[114],"these":[115],"challenges,":[116],"we":[117,175],"propose":[118],"ImProNCDE,":[119],"impulse-corrected":[121],"NCDE":[122],"with":[124],"prototype":[125],"learning":[126],"prediction.":[131],"capture":[133],"beyond":[137],"dynamics,":[140],"ImProNCDE":[141,217],"introduces":[142],"Residual":[143],"Impulse":[144],"Calibration":[145],"(RIC),":[146],"injects":[148],"residual-based":[149],"impulse":[150],"corrections":[151],"at":[152],"visit":[153],"times":[154],"then":[156],"recalibrates":[157],"state":[160],"when":[161],"observations":[162],"deviate":[163],"from":[164],"continuous":[165],"predictions.":[166],"further":[168],"mitigate":[169],"error":[170],"accumulation":[171],"horizons,":[174],"introduce":[176],"Prototype-guided":[178],"Trajectory":[179],"Stabilizer":[180],"(PTS),":[181],"aims":[183],"attract":[185],"toward":[188],"learnable":[189],"prototypes":[191],"reduce":[193],"overlap":[195],"ultimately":[198],"improves":[199],"long-horizon":[200],"stability.":[201],"Experiments":[202],"multiple":[204],"private":[205],"public":[207],"datasets":[210],"(totalling":[211],"1206":[213],"samples)":[214],"show":[215],"that":[216],"outperforms":[218],"existing":[219],"SOTA":[220],"focusing":[222],"sequence":[224]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-20T00:00:00"}