{"id":"https://openalex.org/W7212040101","doi":"https://doi.org/10.64898/2026.09.07.26362404","title":"Prediction of Parkinson\u2019s disease progression from sparse longitudinal trajectories using multiclass likelihood contrast learning","display_name":"Prediction of Parkinson\u2019s disease progression from sparse longitudinal trajectories using multiclass likelihood contrast learning","publication_year":2026,"publication_date":"2026-09-08","ids":{"openalex":"https://openalex.org/W7212040101","doi":"https://doi.org/10.64898/2026.09.07.26362404"},"language":"en","primary_location":{"id":"doi:10.64898/2026.09.07.26362404","doi":"https://doi.org/10.64898/2026.09.07.26362404","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.09.07.26362404","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/09/08/2026.09.07.26362404.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.medrxiv.org/content/medrxiv/early/2026/09/08/2026.09.07.26362404.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135315550","display_name":"Sangam pangeni","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I169521973","display_name":"University of New Mexico","ror":"https://ror.org/05fs6jp91","country_code":"US","type":"education","lineage":["https://openalex.org/I169521973"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sangam Pangeni","raw_affiliation_strings":["Division of Mathematics, Physical and Natural Sciences, University of New Mexico\u2013Gallup"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Mathematics, Physical and Natural Sciences, University of New Mexico\u2013Gallup","institution_ids":["https://openalex.org/I169521973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041197131","display_name":"Md Rejuan Haque","orcid":"https://orcid.org/0000-0002-0582-5269","observed_orcids":["https://orcid.org/0000-0002-0582-5269"]},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Rejuan Haque","raw_affiliation_strings":["Department of Biomedical Informatics, The Ohio State University"],"raw_orcid":"https://orcid.org/0000-0002-0582-5269","affiliations":[{"raw_affiliation_string":"Department of Biomedical Informatics, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012181372","display_name":"Fahad B. Mostafa","orcid":"https://orcid.org/0000-0003-0672-2955","observed_orcids":["https://orcid.org/0000-0003-0672-2955"]},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fahad Mostafa","raw_affiliation_strings":["School of Mathematical and Natural Sciences, Arizona State University"],"raw_orcid":"https://orcid.org/0000-0003-0672-2955","affiliations":[{"raw_affiliation_string":"School of Mathematical and Natural Sciences, Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5012181372"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.3752000033855438,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.3752000033855438,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.18379999697208405,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.1137000024318695,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/parkinson-s-disease-progression","display_name":"Parkinson's disease progression","score":0.8859999775886536},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"multi-class classification","score":0.7929999828338623},{"id":"https://openalex.org/keywords/mixed-effects-models","display_name":"mixed models","score":0.8259999752044678},{"id":"https://openalex.org/keywords/trajectory-reconstruction","display_name":"trajectory reconstruction","score":0.6190000176429749},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.4189999997615814},{"id":"https://openalex.org/keywords/wearable-sensor-data","display_name":"wearable sensor data","score":0.44999998807907104}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6122999787330627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5898000001907349},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5641000270843506},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5555999875068665},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5468000173568726},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4050999879837036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3968000113964081},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3334999978542328},{"id":"https://openalex.org/C16012445","wikidata":"https://www.wikidata.org/wiki/Q1501135","display_name":"Mixed model","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C2983524802","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic model","level":3,"score":0.32580000162124634},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.2946000099182129},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C102959455","wikidata":"https://www.wikidata.org/wiki/Q7313954","display_name":"Repeated measures design","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C168743327","wikidata":"https://www.wikidata.org/wiki/Q1826427","display_name":"Random effects model","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C2779466056","wikidata":"https://www.wikidata.org/wiki/Q107630651","display_name":"Time point","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.64898/2026.09.07.26362404","doi":"https://doi.org/10.64898/2026.09.07.26362404","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.09.07.26362404","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/09/08/2026.09.07.26362404.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.64898/2026.09.07.26362404","doi":"https://doi.org/10.64898/2026.09.07.26362404","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.09.07.26362404","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/09/08/2026.09.07.26362404.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8560000061988831,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7212040101.pdf","grobid_xml":"https://content.openalex.org/works/W7212040101.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1480271113","https://openalex.org/W1492555660","https://openalex.org/W1951724000","https://openalex.org/W2025984697","https://openalex.org/W2082246284","https://openalex.org/W2112455323","https://openalex.org/W2140978740","https://openalex.org/W2142635246","https://openalex.org/W2144148350","https://openalex.org/W2145954079","https://openalex.org/W2146855196","https://openalex.org/W2158698691","https://openalex.org/W2170505850","https://openalex.org/W2742491462","https://openalex.org/W2763160983","https://openalex.org/W2964010366","https://openalex.org/W2996331899","https://openalex.org/W3002868451","https://openalex.org/W3108372623","https://openalex.org/W4230076583","https://openalex.org/W4239510810","https://openalex.org/W4382937268","https://openalex.org/W7099567267"],"related_works":["https://openalex.org/W2800956162","https://openalex.org/W3128960514","https://openalex.org/W2807039648","https://openalex.org/W2890411499","https://openalex.org/W3199719950","https://openalex.org/W7164030551","https://openalex.org/W3183249008","https://openalex.org/W7164012852","https://openalex.org/W3183919604","https://openalex.org/W3091578488"],"abstract_inverted_index":{"Abstract":[0],"Background":[1],"Biomedical":[2],"studies":[3],"increasingly":[4],"collect":[5],"repeated":[6,92],"measurements,":[7],"such":[8],"as":[9,281],"clinical":[10,58,290,302,318],"ratings,":[11],"speech":[12],"measures,":[13],"gait":[14],"summaries,":[15],"and":[16,29,55,71,117,143,158,186,209,238,252,267,309],"wearable-sensor":[17],"features,":[18],"at":[19],"irregular":[20],"subject-specific":[21,140],"follow-up":[22,59],"times.":[23],"These":[24],"data":[25],"are":[26,173,183,188],"often":[27],"sparse":[28,79,163,196,217,284],"unaligned,":[30],"making":[31],"them":[32],"difficult":[33],"to":[34,86,100,122,176,190],"use":[35],"with":[36,125,139,154,248,299],"standard":[37],"classifiers":[38],"that":[39,314],"require":[40],"fixed-length":[41],"input":[42],"vectors.":[43],"Parkinson\u2019s":[44,161,216,276],"disease":[45],"(PD)":[46],"provides":[47],"a":[48,64,73,135,151,160,200],"motivating":[49],"example":[50],"because":[51],"progression":[52],"is":[53,60,85,148],"longitudinal":[54,80,273,305],"heterogeneous,":[56],"but":[57],"rarely":[61],"observed":[62,91],"on":[63],"common":[65],"schedule.":[66],"Objective":[67],"This":[68],"study":[69,287],"develops":[70],"evaluates":[72],"multiclass":[74],"likelihood":[75,129,262],"contrast":[76],"classifier":[77],"for":[78,256,270],"trajectory":[81,138,164,179,285],"recovery.":[82],"The":[83,106,131,275],"goal":[84],"classify":[87],"subjects":[88],"using":[89],"their":[90],"measurements":[93],"while":[94],"avoiding":[95],"aggressive":[96],"reduction":[97],"of":[98,223],"trajectories":[99],"averages":[101],"or":[102],"simple":[103],"slopes.":[104],"Methods":[105],"proposed":[107,229,258],"method":[108,298],"fits":[109],"one":[110],"class-specific":[111],"mixed-effects":[112],"model":[113,230],"per":[114],"outcome":[115],"class":[116,124,156],"assigns":[118],"each":[119],"held-out":[120,246],"subject":[121],"the":[123,126,168,192,195,215,228,257,297],"largest":[127],"marginal":[128],"score.":[130],"main":[132],"specification":[133],"uses":[134],"quadratic":[136],"mean":[137],"random":[141,144,207],"intercept":[142],"slope":[145],"terms.":[146],"Performance":[147],"evaluated":[149],"in":[150,159],"three-class":[152],"simulation":[153],"imbalanced":[155],"sizes":[157],"motor-UPDRS":[162],"recovery":[165,218,286],"experiment.":[166],"In":[167,214],"Parkin-son\u2019s":[169],"experiment,":[170,219],"dense":[171,224],"histories":[172],"first":[174],"used":[175],"define":[177],"mathematical":[178],"phenotypes;":[180],"most":[181],"observations":[182,225],"then":[184],"hidden,":[185,227],"models":[187],"asked":[189],"recover":[191],"phenotype":[193],"from":[194],"record.":[197],"Baselines":[198],"include":[199],"linear":[201],"mixed-likelihood":[202],"classifier,":[203],"functional":[204],"k-nearest":[205],"neighbors,":[206],"forest,":[208],"support":[210],"vector":[211],"machine.":[212],"Results":[213],"after":[220],"approximately":[221],"90%":[222],"were":[226],"achieved":[231],"accuracy":[232],"0.8095,":[233],"macro-F1":[234,250,254],"0.8160,":[235],"MCC":[236],"0.7107,":[237],"macro-AUC":[239],"0.9268.":[240],"A":[241],"train\u2013validation\u2013test":[242],"diagnostic":[243,291],"gave":[244],"similar":[245],"behavior,":[247],"validation":[249],"0.7980":[251],"test":[253],"0.8160":[255],"model.":[259],"Conclusion":[260],"Multiclass":[261],"contrasts":[263],"offer":[264],"an":[265,282],"interpretable":[266],"competitive":[268],"approach":[269],"sparse,":[271],"unaligned":[272],"classification.":[274],"analysis":[277],"should":[278,295],"be":[279],"read":[280],"algorithmic":[283],"rather":[288],"than":[289],"validation.":[292],"Future":[293],"work":[294],"evaluate":[296],"externally":[300],"assigned":[301],"outcomes,":[303],"multivariate":[304],"biomarkers,":[306],"prospective":[307],"cohorts,":[308],"ordinal":[310],"generalized":[311],"mixed-model":[312],"likelihoods":[313],"better":[315],"match":[316],"bounded":[317],"rating":[319],"scales.":[320]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-09-10T00:00:00"}