{"id":"https://openalex.org/W4225674677","doi":"https://doi.org/10.48550/arxiv.2203.06369","title":"The Health Gym: Synthetic Health-Related Datasets for the Development of\\n Reinforcement Learning Algorithms","display_name":"The Health Gym: Synthetic Health-Related Datasets for the Development of\\n Reinforcement Learning Algorithms","publication_year":2022,"publication_date":"2022-03-12","ids":{"openalex":"https://openalex.org/W4225674677","doi":"https://doi.org/10.48550/arxiv.2203.06369"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2203.06369","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.06369","pdf_url":"https://arxiv.org/pdf/2203.06369","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.06369","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021444442","display_name":"Nicholas I-Hsien Kuo","orcid":"https://orcid.org/0000-0001-8749-7280"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuo, Nicholas I-Hsien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062384332","display_name":"Mark N. Polizzotto","orcid":"https://orcid.org/0000-0002-6446-183X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Polizzotto, Mark N.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011278994","display_name":"Simon Finfer","orcid":"https://orcid.org/0000-0002-2785-5864"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Finfer, Simon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033012471","display_name":"F\u00e9derico Garc\u00eda","orcid":"https://orcid.org/0000-0001-7611-781X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garcia, Federico","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034073363","display_name":"Anders S\u00f6nnerborg","orcid":"https://orcid.org/0000-0001-8928-3374"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S\u00f6nnerborg, Anders","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002487044","display_name":"Maurizio Zazzi","orcid":"https://orcid.org/0000-0002-0344-6281"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zazzi, Maurizio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008678419","display_name":"Michael B\u00f6hm","orcid":"https://orcid.org/0000-0002-2976-2514"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B\u00f6hm, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084618823","display_name":"Louisa Jorm","orcid":"https://orcid.org/0000-0003-0390-661X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jorm, Louisa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5033997225","display_name":"Sebastiano Barbieri","orcid":"https://orcid.org/0000-0002-5919-372X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barbieri, Sebastiano","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":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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9606999754905701,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9606999754905701,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9344000220298767,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9114000201225281,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/machine-learning","display_name":"Machine learning","score":0.7805696725845337},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6813870668411255},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6483426094055176},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6316035985946655},{"id":"https://openalex.org/keywords/confidentiality","display_name":"Confidentiality","score":0.5946215987205505},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5876369476318359},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.501516580581665},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.49549973011016846},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4175738990306854},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.26739078760147095},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.09917163848876953}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7805696725845337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6813870668411255},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6483426094055176},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6316035985946655},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.5946215987205505},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5876369476318359},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.501516580581665},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.49549973011016846},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4175738990306854},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26739078760147095},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.09917163848876953},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:arXiv.org:2203.06369","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.06369","pdf_url":"https://arxiv.org/pdf/2203.06369","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2203.06369","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.06369","pdf_url":"https://arxiv.org/pdf/2203.06369","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4387497383","https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2948807893","https://openalex.org/W2935909890","https://openalex.org/W2778153218","https://openalex.org/W2758277628","https://openalex.org/W1531601525","https://openalex.org/W2378211422"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"the":[3,11,32,46,91,128,134,137,148],"machine":[4,67],"learning":[5,38],"research":[6],"community":[7],"has":[8,30],"benefited\\ntremendously":[9],"from":[10],"availability":[12],"of":[13,34,52,118,139,147],"openly":[14,22],"accessible":[15],"benchmark":[16],"datasets.\\nClinical":[17],"data":[18],"are":[19],"usually":[20],"not":[21],"available":[23],"due":[24],"to":[25,62,153],"their":[26],"highly":[27,53],"confidential\\nnature.":[28],"This":[29],"hampered":[31],"development":[33],"reproducible":[35],"and":[36,65,88,95,120],"generalisable\\nmachine":[37],"applications":[39],"in":[40,80,90,104,127,133],"health":[41],"care.":[42],"Here":[43],"we":[44],"introduce":[45],"Health":[47],"Gym\\n-":[48],"a":[49,70,111],"growing":[50],"collection":[51],"realistic":[54],"synthetic":[55,129,149],"medical":[56],"datasets":[57,78,107,130,150],"that":[58],"can\\nbe":[59],"freely":[60],"accessed":[61],"prototype,":[63],"evaluate,":[64],"compare":[66],"learning\\nalgorithms,":[68],"with":[69,86,97,144],"specific":[71],"focus":[72],"on":[73],"reinforcement":[74],"learning.":[75],"The":[76,106,116],"three\\nsynthetic":[77],"described":[79],"this":[81],"paper":[82],"present":[83],"patient":[84],"cohorts":[85],"acute\\nhypotension":[87],"sepsis":[89],"intensive":[92],"care":[93],"unit,":[94],"people":[96],"human\\nimmunodeficiency":[98],"virus":[99],"(HIV)":[100],"receiving":[101],"antiretroviral":[102],"therapy":[103],"ambulatory\\ncare.":[105],"were":[108],"created":[109],"using":[110],"novel":[112],"generative":[113],"adversarial":[114],"network\\n(GAN).":[115],"distributions":[117],"variables,":[119],"correlations":[121],"between":[122],"variables":[123],"and\\ntrends":[124],"over":[125],"time":[126],"mirror":[131],"those":[132],"real":[135],"datasets.\\nFurthermore,":[136],"risk":[138],"sensitive":[140],"information":[141],"disclosure":[142],"associated":[143],"the\\npublic":[145],"distribution":[146],"is":[151],"estimated":[152],"be":[154],"very":[155],"low.\\n":[156]},"counts_by_year":[],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2022-05-05T00:00:00"}
