{"id":"https://openalex.org/W4290603363","doi":"https://doi.org/10.1142/s021812662350041x","title":"Extraction of Meaningful Information from Unstructured Clinical Notes Using Web Scraping","display_name":"Extraction of Meaningful Information from Unstructured Clinical Notes Using Web Scraping","publication_year":2022,"publication_date":"2022-08-08","ids":{"openalex":"https://openalex.org/W4290603363","doi":"https://doi.org/10.1142/s021812662350041x"},"language":"en","primary_location":{"id":"doi:10.1142/s021812662350041x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021812662350041x","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058032641","display_name":"K Varshini","orcid":"https://orcid.org/0000-0002-2493-3150"},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"K. Sukanya Varshini","raw_affiliation_strings":["CINTEL, SRM Institute of Science and Technology, Kattankulathur, Chengelpet, Tamil Nadu 603203, India"],"raw_orcid":"https://orcid.org/0000-0002-2493-3150","affiliations":[{"raw_affiliation_string":"CINTEL, SRM Institute of Science and Technology, Kattankulathur, Chengelpet, Tamil Nadu 603203, India","institution_ids":["https://openalex.org/I145286018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041530728","display_name":"R. Annie Uthra","orcid":"https://orcid.org/0000-0001-7027-9623"},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"R. Annie Uthra","raw_affiliation_strings":["CINTEL, SRM Institute of Science and Technology, Kattankulathur, Chengelpet, Tamil Nadu 603203, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CINTEL, SRM Institute of Science and Technology, Kattankulathur, Chengelpet, Tamil Nadu 603203, India","institution_ids":["https://openalex.org/I145286018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5058032641"],"corresponding_institution_ids":["https://openalex.org/I145286018"],"apc_list":null,"apc_paid":null,"fwci":0.0883,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.3789755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"32","issue":"03","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13918","display_name":"Advanced Data and IoT Technologies","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13918","display_name":"Advanced Data and IoT Technologies","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9686999917030334,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9631999731063843,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7253749370574951},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7103090882301331},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6766937971115112},{"id":"https://openalex.org/keywords/unstructured-data","display_name":"Unstructured data","score":0.6242315769195557},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5671379566192627},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.552396833896637},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.4928710460662842},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4896553158760071},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4716476798057556},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4562486410140991},{"id":"https://openalex.org/keywords/medical-record","display_name":"Medical record","score":0.43673980236053467},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4267902374267578},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3826279640197754},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3798615038394928},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.3432523012161255},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1718325912952423}],"concepts":[{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7253749370574951},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7103090882301331},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6766937971115112},{"id":"https://openalex.org/C2781252014","wikidata":"https://www.wikidata.org/wiki/Q1141900","display_name":"Unstructured data","level":3,"score":0.6242315769195557},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5671379566192627},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.552396833896637},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.4928710460662842},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4896553158760071},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4716476798057556},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4562486410140991},{"id":"https://openalex.org/C195910791","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Medical record","level":2,"score":0.43673980236053467},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4267902374267578},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3826279640197754},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3798615038394928},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3432523012161255},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1718325912952423},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s021812662350041x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021812662350041x","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1902526473","https://openalex.org/W1999802986","https://openalex.org/W2019782190","https://openalex.org/W2059980448","https://openalex.org/W2083093865","https://openalex.org/W2087694898","https://openalex.org/W2107182304","https://openalex.org/W2161299495","https://openalex.org/W2277786047","https://openalex.org/W2298551783","https://openalex.org/W3095147220","https://openalex.org/W3159162481","https://openalex.org/W3203151276","https://openalex.org/W3208807485","https://openalex.org/W3216006944","https://openalex.org/W3217476834","https://openalex.org/W4220674695","https://openalex.org/W4225976909"],"related_works":["https://openalex.org/W4367336074","https://openalex.org/W4379620016","https://openalex.org/W3154045278","https://openalex.org/W3210764983","https://openalex.org/W3089416646","https://openalex.org/W4367335949","https://openalex.org/W4380048833","https://openalex.org/W4285162676","https://openalex.org/W3036529732","https://openalex.org/W4382052559"],"abstract_inverted_index":{"In":[0,120],"the":[1,4,9,21,31,34,57,64,94,102,106,121,132,135,138,149,154,158,165,175,180,184,190,197,214,227],"medical":[2,13,24],"field,":[3],"clinical":[5,103,115,159],"notes":[6,104,116],"taken":[7],"by":[8],"doctor,":[10],"nurse,":[11],"or":[12],"practitioner":[14],"are":[15,66,146,172],"considered":[16],"to":[17,90,100,130,226],"be":[18,61],"one":[19],"of":[20,72,117,137,177,222],"most":[22],"important":[23,54],"documents.":[25],"These":[26],"documents":[27,52],"hold":[28,112],"information":[29,55],"regarding":[30,56],"patient":[32],"including":[33,164,196],"patient\u2019s":[35],"current":[36],"condition,":[37],"family":[38],"history,":[39],"disease,":[40,178],"symptoms,":[41],"medications,":[42],"lab":[43],"test":[44],"reports,":[45],"and":[46,134,169,179,206],"other":[47,228],"vital":[48],"information.":[49],"Despite":[50],"these":[51],"holding":[53],"patients,":[58],"they":[59],"cannot":[60],"used":[62,99,129,147,173],"as":[63],"data":[65,73,85,156],"unstructured.":[67],"Organizing":[68],"a":[69,219],"huge":[70],"amount":[71],"without":[74],"any":[75],"mistakes":[76],"is":[77,86,98,128,187],"highly":[78],"impossible":[79],"for":[80,148],"humans,":[81],"so":[82],"ignoring":[83],"unstructured":[84],"not":[87],"advisable.":[88],"Hence,":[89],"overcome":[91],"this":[92],"issue,":[93],"web":[95],"scraping":[96],"method":[97],"extract":[101],"from":[105,157,183],"Medical":[107],"Transcription":[108],"(MT)":[109],"samples":[110],"which":[111],"many":[113],"transcripted":[114],"various":[118],"departments.":[119],"proposed":[122,185],"method,":[123],"Natural":[124],"Language":[125],"Processing":[126],"(NLP)":[127],"pre-process":[131],"data,":[133],"variants":[136],"Term":[139],"Frequency-Inverse":[140],"Document":[141],"Frequency":[142],"(TF-IDF)-based":[143],"vector":[144],"model":[145],"feature":[150],"selection,":[151],"thus":[152],"extracting":[153],"required":[155],"notes.":[160],"The":[161,209],"performance":[162],"measures":[163],"accuracy,":[166],"precision,":[167],"recall":[168],"F1":[170],"score":[171],"in":[174],"identification":[176],"result":[181,210],"obtained":[182,211,218],"system":[186],"compared":[188,225],"with":[189],"best":[191],"performing":[192],"machine":[193],"learning":[194],"algorithms":[195],"Logistic":[198],"Regression,":[199],"Multinomial":[200],"Naive":[201],"Bayes,":[202],"Random":[203,215],"Forest":[204,216],"classifier":[205],"Linear":[207],"SVC.":[208],"proves":[212],"that":[213],"Classifier":[217],"higher":[220],"accuracy":[221],"90%":[223],"when":[224],"algorithms.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}