{"id":"https://openalex.org/W7117731595","doi":"https://doi.org/10.1145/3761712.3761774","title":"Risk Prediction and Interpretation for Fall Events Using Explainable AI and Large Language Models","display_name":"Risk Prediction and Interpretation for Fall Events Using Explainable AI and Large Language Models","publication_year":2025,"publication_date":"2025-05-16","ids":{"openalex":"https://openalex.org/W7117731595","doi":"https://doi.org/10.1145/3761712.3761774","pmid":"https://pubmed.ncbi.nlm.nih.gov/42051978"},"language":"en","primary_location":{"id":"doi:10.1145/3761712.3761774","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3761712.3761774","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 9th International Conference on Medical and Health Informatics","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3761712.3761774","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120994843","display_name":"Jake Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148948","display_name":"Milwaukee Health Department","ror":"https://ror.org/0561hjj97","country_code":"US","type":"government","lineage":["https://openalex.org/I4210148948"]},{"id":"https://openalex.org/I43579087","display_name":"University of Wisconsin\u2013Milwaukee","ror":"https://ror.org/031q21x57","country_code":"US","type":"education","lineage":["https://openalex.org/I43579087"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jake Luo","raw_affiliation_strings":["Computer Science Department, University of Wisconsin-Milwaukee, Milwaukee, USA","Health Informatics Department, Zilber College of Pubic Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA"],"raw_orcid":"https://orcid.org/0000-0002-3900-643X","affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Wisconsin-Milwaukee, Milwaukee, USA","institution_ids":["https://openalex.org/I43579087"]},{"raw_affiliation_string":"Health Informatics Department, Zilber College of Pubic Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA","institution_ids":["https://openalex.org/I4210148948","https://openalex.org/I43579087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043982641","display_name":"Masoud Khani","orcid":"https://orcid.org/0000-0003-3131-0661"},"institutions":[{"id":"https://openalex.org/I4210148948","display_name":"Milwaukee Health Department","ror":"https://ror.org/0561hjj97","country_code":"US","type":"government","lineage":["https://openalex.org/I4210148948"]},{"id":"https://openalex.org/I43579087","display_name":"University of Wisconsin\u2013Milwaukee","ror":"https://ror.org/031q21x57","country_code":"US","type":"education","lineage":["https://openalex.org/I43579087"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Masoud Khani","raw_affiliation_strings":["Health Informatics &amp; Computer Science Department, University of Wisconsin-Milwaukee, Milwaukee, USA"],"raw_orcid":"https://orcid.org/0000-0003-3131-0661","affiliations":[{"raw_affiliation_string":"Health Informatics &amp; Computer Science Department, University of Wisconsin-Milwaukee, Milwaukee, USA","institution_ids":["https://openalex.org/I4210148948","https://openalex.org/I43579087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121015207","display_name":"Jazzmyne Adams","orcid":null},"institutions":[{"id":"https://openalex.org/I204308271","display_name":"Medical College of Wisconsin","ror":"https://ror.org/00qqv6244","country_code":"US","type":"education","lineage":["https://openalex.org/I204308271"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jazzmyne Adams","raw_affiliation_strings":["Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, Milwaukee, USA"],"raw_orcid":"https://orcid.org/0000-0001-9809-9915","affiliations":[{"raw_affiliation_string":"Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, Milwaukee, USA","institution_ids":["https://openalex.org/I204308271"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qiang Lu","orcid":"https://orcid.org/0000-0001-8217-2305"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Lu","raw_affiliation_strings":["Department of Computer Science &amp; Technology, China University of Petroleum, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8217-2305","affiliations":[{"raw_affiliation_string":"Department of Computer Science &amp; Technology, China University of Petroleum, Beijing, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024793082","display_name":"Kristian M. O\u2019Connor","orcid":"https://orcid.org/0000-0001-8272-4077"},"institutions":[{"id":"https://openalex.org/I43579087","display_name":"University of Wisconsin\u2013Milwaukee","ror":"https://ror.org/031q21x57","country_code":"US","type":"education","lineage":["https://openalex.org/I43579087"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kristian O'Connor","raw_affiliation_strings":["Department of Kinesiology, University of Wisconsin-Milwaukee, Milwaukee, USA"],"raw_orcid":"https://orcid.org/0000-0001-8272-4077","affiliations":[{"raw_affiliation_string":"Department of Kinesiology, University of Wisconsin-Milwaukee, Milwaukee, USA","institution_ids":["https://openalex.org/I43579087"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045152801","display_name":"David R. Friedland","orcid":"https://orcid.org/0000-0002-7653-1508"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David R. Friedland","raw_affiliation_strings":["Keck School of Medicine, University of South California, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0000-0002-7653-1508","affiliations":[{"raw_affiliation_string":"Keck School of Medicine, University of South California, Los Angeles, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"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":"2025","issue":null,"first_page":"270","last_page":"277"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.534600019454956,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.534600019454956,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.15119999647140503,"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/T11011","display_name":"Frailty in Older Adults","score":0.05290000140666962,"subfield":{"id":"https://openalex.org/subfields/2717","display_name":"Geriatrics and Gerontology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9146000146865845},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49939998984336853},{"id":"https://openalex.org/keywords/risk-assessment","display_name":"Risk assessment","score":0.49549999833106995},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4129999876022339},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.4034999907016754},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.40310001373291016},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.3962000012397766}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9146000146865845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7042999863624573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6905999779701233},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6485999822616577},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49939998984336853},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4129999876022339},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4016000032424927},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3962000012397766},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3761712.3761774","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3761712.3761774","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 9th International Conference on Medical and Health Informatics","raw_type":"proceedings-article"},{"id":"pmid:42051978","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42051978","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 9th International Conference on Medical and Health Informatics. International Conference on Medical and Health Informatics (9th : 2025 : Kyoto, Japan)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:13112544","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13112544/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc 2025 9th Int Conf Med Health Inform (2025)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1145/3761712.3761774","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3761712.3761774","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 9th International Conference on Medical and Health Informatics","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7174516039","display_name":null,"funder_award_id":"UL1 TR001436","funder_id":"https://openalex.org/F4320337472","funder_display_name":"National Center for Advancing Translational Sciences"}],"funders":[{"id":"https://openalex.org/F4320337472","display_name":"National Center for Advancing Translational Sciences","ror":"https://ror.org/04pw6fb54"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2000333654","https://openalex.org/W2015462679","https://openalex.org/W2060762518","https://openalex.org/W2414900880","https://openalex.org/W2803516046","https://openalex.org/W2964629181","https://openalex.org/W3008233702","https://openalex.org/W3032442715","https://openalex.org/W3124016887","https://openalex.org/W3127672048","https://openalex.org/W3139236016","https://openalex.org/W3176714582","https://openalex.org/W3205360896","https://openalex.org/W4283320106","https://openalex.org/W4294559022","https://openalex.org/W4306947767","https://openalex.org/W4308718476","https://openalex.org/W4385287322"],"related_works":[],"abstract_inverted_index":{"Falls":[0],"represent":[1],"a":[2,272],"significant":[3,273],"public":[4],"health":[5,95],"concern,":[6],"particularly":[7],"among":[8],"older":[9],"adults,":[10],"with":[11,60,103,141,249,257],"approximately":[12],"35.6":[13],"million":[14],"cases":[15],"annually":[16],"in":[17,37,275,292],"the":[18,104,129,163,184,241,258,287],"United":[19],"States":[20],"alone.":[21],"Traditional":[22],"machine":[23,49,86],"learning-based":[24],"fall":[25,53,79,200,246,282],"risk":[26,54,136,152,201,226,247,269,283],"prediction":[27,55,248],"methods":[28],"often":[29],"lack":[30],"precision":[31],"and":[32,45,63,100,123,138,149,177,228,235,253,265],"interpretability,":[33],"limiting":[34],"their":[35],"effectiveness":[36],"clinical":[38,281],"settings.":[39,294],"This":[40],"study":[41],"aims":[42],"to":[43,74,92,199,261],"develop":[44],"validate":[46],"an":[47,84,173,178],"explainable":[48,250],"learning":[50,87],"approach":[51,239],"for":[52,68,232,280],"that":[56,222],"combines":[57],"predictive":[58],"accuracy":[59],"interpretable":[61,263],"results":[62,264],"leverages":[64],"large":[65,124,206,254],"language":[66,119,125,207,255],"models":[67,208],"enhanced":[69,107],"communication,":[70],"enabling":[71],"healthcare":[72,233,293],"providers":[73,234],"make":[75],"informed":[76],"decisions":[77],"about":[78],"prevention":[80],"strategies.":[81],"We":[82],"developed":[83],"integrated":[85,238],"pipeline":[88],"utilizing":[89],"XGBoost":[90],"classifier":[91,160],"process":[93],"key":[94],"indicators":[96],"such":[97],"as":[98,162],"age,":[99],"diagnosis":[101],"history,":[102],"model's":[105],"interpretability":[106],"through":[108,209],"SHAP":[109,188],"(SHapley":[110],"Additive":[111],"exPlanations)":[112],"values.":[113],"Results":[114],"were":[115],"transformed":[116,212],"into":[117,144,193,216],"natural":[118],"narratives":[120],"using":[121],"LangChain":[122,210],"models,":[126,256],"which":[127],"automated":[128],"generation":[130],"of":[131,175,181,205,243,289],"personalized":[132,268],"reports":[133],"combining":[134,244],"both":[135,224],"assessments":[137],"feature":[139,194,230],"explanations,":[140],"patients":[142],"classified":[143],"low":[145],"(0-40%),":[146],"medium":[147],"(40-70%),":[148],"high":[150],"(70-100%)":[151],"groups":[153],"based":[154],"on":[155,183],"predicted":[156],"probabilities.":[157],"The":[158,187,203],"XG-Boost":[159],"emerged":[161],"best-performing":[164],"model,":[165],"achieving":[166],"71%":[167],"accuracy,":[168],"69%":[169],"precision,":[170],"76%":[171],"recall,":[172],"F1-score":[174],"72%,":[176],"ROC":[179],"AUC":[180],"0.71":[182],"testing":[185],"dataset.":[186],"analysis":[189],"provided":[190],"transparent":[191],"insights":[192],"importance,":[195],"highlighting":[196],"critical":[197],"contributors":[198],"prediction.":[202],"integration":[204],"successfully":[211],"complex":[213],"model":[214],"outputs":[215],"comprehensible":[217],"narratives,":[218],"generating":[219],"detailed":[220],"explanations":[221],"included":[223],"high-level":[225],"summaries":[227],"specific":[229],"contributions":[231],"patients.":[236],"Our":[237],"demonstrates":[240],"feasibility":[242],"high-accuracy":[245],"AI":[251],"techniques":[252],"system's":[259],"ability":[260],"provide":[262],"generate":[266],"clear,":[267],"communications":[270],"representing":[271],"advancement":[274],"developing":[276],"practical":[277],"AI-driven":[278],"tools":[279],"assessment,":[284],"potentially":[285],"improving":[286],"implementation":[288],"preventive":[290],"interventions":[291]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-12-31T00:00:00"}
