Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach
Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention...
Gespeichert in:
| Veröffentlicht in: | Journal of personalized medicine Jg. 12; H. 4; S. 614 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Switzerland
MDPI AG
11.04.2022
MDPI |
| Schlagworte: | |
| ISSN: | 2075-4426, 2075-4426 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall–precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care. |
|---|---|
| AbstractList | Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall-precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care.Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall-precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care. Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall–precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care. |
| Author | Rostami, Reihaneh Hegde, Harshad Acharya, Amit Shimpi, Neel Glurich, Ingrid Olson, Brent |
| AuthorAffiliation | 1 Marshfield Clinic Research Institute, Marshfield, WI 54449, USA; shimpi.neel@marshfieldresearch.org (N.S.); glurich.ingrid@marshfieldresearch.org (I.G.) 3 Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; hegdehb@gmail.com 2 Computer Science Department, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA; reyhane.rostami@gmail.com 5 Advocate Aurora Health, Chicago, IL 60515, USA 4 Office of Research Analytics and Computing, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA; olson.brent@marshfieldresearch.org |
| AuthorAffiliation_xml | – name: 4 Office of Research Analytics and Computing, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA; olson.brent@marshfieldresearch.org – name: 5 Advocate Aurora Health, Chicago, IL 60515, USA – name: 1 Marshfield Clinic Research Institute, Marshfield, WI 54449, USA; shimpi.neel@marshfieldresearch.org (N.S.); glurich.ingrid@marshfieldresearch.org (I.G.) – name: 2 Computer Science Department, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA; reyhane.rostami@gmail.com – name: 3 Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; hegdehb@gmail.com |
| Author_xml | – sequence: 1 givenname: Neel surname: Shimpi fullname: Shimpi, Neel – sequence: 2 givenname: Ingrid surname: Glurich fullname: Glurich, Ingrid – sequence: 3 givenname: Reihaneh orcidid: 0000-0002-5825-5407 surname: Rostami fullname: Rostami, Reihaneh – sequence: 4 givenname: Harshad orcidid: 0000-0002-2411-565X surname: Hegde fullname: Hegde, Harshad – sequence: 5 givenname: Brent surname: Olson fullname: Olson, Brent – sequence: 6 givenname: Amit surname: Acharya fullname: Acharya, Amit |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35455730$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/2470759$$D View this record in Osti.gov |
| BookMark | eNptkstvEzEQhy1UREvpiTuy4IIEC37tI5dKUaBQKVCEKFfL8Y4bh117sZ2VcucPr9O0VVrhy1jjb37z8nN04LwDhF5S8oHzCfm4GnrKiCAVFU_QESN1WQjBqoO9-yE6iXFF8mlKxiryDB3yUpRlzckR-vcJRuj80INLWLkW_1adbVWy3mFvsMLfvSvO3aiiHeE9ni2VDdG2gC-C6vBMjTZtsnEaAv5p4x88jRFivJH7EXzyaTMAvozWXeFvSi-tAzwHFdzWMR2G4LPzBXpqVBfh5NYeo8uzz79mX4v5xZfz2XReaMGrVNRm0RJogDIwtGIlI21jGi50Q2BRCc2MhqY1hghmKCxERVgLbQPcEEq5JvwYne50h_Wih1bnInMXcgi2V2EjvbLy4YuzS3nlRzkhnE2aMgu83gn4mKyM2ibQS-2dA50kE3Ue-SRDb2-zBP93DTHJ3kYNXacc-HWUrCoFa2gp6oy-eYSu_Dq4PIMtlVOSuhKZerVf9n29d1vMAN0BOvgYAxiZK7vZYe7CdpISuf0scu-z5Jh3j2LuZP9HXwOAWcDN |
| CitedBy_id | crossref_primary_10_1186_s40537_023_00703_w crossref_primary_10_3389_fdmed_2022_1005140 |
| Cites_doi | 10.1016/0168-9525(93)90209-Z 10.1016/j.imu.2019.100254 10.1016/j.procs.2016.04.224 10.1111/jphd.12392 10.3390/jpm11090832 10.7314/APJCP.2016.17.2.835 10.1002/cam4.84 10.1109/HIBIT.2010.5478895 10.7717/peerj.2482 10.12998/wjcc.v2.i12.866 10.3390/cancers13184600 10.1002/9780470725184 10.4103/0971-6866.132745 10.3233/THC-191642 10.3389/fdata.2020.00006 10.3233/THC-171127 10.3390/diagnostics12010203 10.1007/s10916-015-0241-3 10.3322/caac.21660 10.1007/s13187-016-1084-4 10.1038/sj.bdj.4808932 10.1038/s41746-021-00427-2 10.4103/0971-5851.203510 10.1039/C4MB00659C 10.1155/2015/234191 10.1016/j.adaj.2018.05.030 10.3402/jom.v7.28223 10.1007/978-3-319-98298-4 10.1111/odi.14123 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| CorporateAuthor | Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States) |
| CorporateAuthor_xml | – name: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States) |
| DBID | AAYXX CITATION NPM 8FE 8FH ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 OIOZB OTOTI 5PM |
| DOI | 10.3390/jpm12040614 |
| DatabaseName | CrossRef PubMed ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central ProQuest Central Student SciTech Premium Collection Biological Sciences Biological Science Database ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic OSTI.GOV - Hybrid OSTI.GOV PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Biological Science Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection Biological Science Database ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef PubMed Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2075-4426 |
| ExternalDocumentID | PMC9032985 2470759 35455730 10_3390_jpm12040614 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Family Health Center of Marshfield.Inc grantid: Not applicable – fundername: Marshfield Clinic Research Institute grantid: not applicable – fundername: Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427 grantid: UL1TR000427 – fundername: Delta Dental of Wisconsin grantid: not applicable |
| GroupedDBID | 53G 5VS 8FE 8FH AADQD AAFWJ AAYXX ADBBV AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BBNVY BCNDV BENPR BHPHI CCPQU CITATION DIK EMOBN GX1 HCIFZ HYE IAO IHR ITC KQ8 LK8 M48 M7P MODMG M~E OK1 PGMZT PHGZM PHGZT PIMPY PQGLB PROAC RPM GROUPED_DOAJ NPM ABUWG AZQEC DWQXO GNUQQ PKEHL PQEST PQQKQ PQUKI PRINS 7X8 PUEGO OIOZB OTOTI 5PM |
| ID | FETCH-LOGICAL-c436t-7fbd0e8e12ef162520d8f834c80eb64c2fce8dff042f1eb4602ded8e3f0113c03 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000786311900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2075-4426 |
| IngestDate | Tue Nov 04 02:01:36 EST 2025 Mon Apr 07 02:20:32 EDT 2025 Thu Oct 02 11:25:01 EDT 2025 Fri Jul 25 12:02:00 EDT 2025 Thu Jan 02 22:54:29 EST 2025 Tue Nov 18 20:02:17 EST 2025 Sat Nov 29 07:19:21 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | risk assessment precision medicine machine learning patient care management oral cancer |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c436t-7fbd0e8e12ef162520d8f834c80eb64c2fce8dff042f1eb4602ded8e3f0113c03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 USDOE National Institutes of Health (NIH) AC02-05CH11231; UL1TR000427 |
| ORCID | 0000-0002-5825-5407 0000-0002-2411-565X 000000022411565X 0000000258255407 |
| OpenAccessLink | https://www.proquest.com/docview/2652980764?pq-origsite=%requestingapplication% |
| PMID | 35455730 |
| PQID | 2652980764 |
| PQPubID | 2032376 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_9032985 osti_scitechconnect_2470759 proquest_miscellaneous_2654281547 proquest_journals_2652980764 pubmed_primary_35455730 crossref_citationtrail_10_3390_jpm12040614 crossref_primary_10_3390_jpm12040614 |
| PublicationCentury | 2000 |
| PublicationDate | 20220411 |
| PublicationDateYYYYMMDD | 2022-04-11 |
| PublicationDate_xml | – month: 4 year: 2022 text: 20220411 day: 11 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel – name: United States |
| PublicationTitle | Journal of personalized medicine |
| PublicationTitleAlternate | J Pers Med |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Yardimci (ref_7) 2014; 2 Tseng (ref_38) 2015; 39 ref_36 ref_35 ref_12 Nycz (ref_15) 2020; 80 ref_34 ref_33 ref_32 Shimpi (ref_17) 2020; 28 Rosma (ref_39) 2010; 3 Sharma (ref_29) 2012; 302 Speight (ref_31) 1995; 179 ref_19 Glurich (ref_5) 2015; 7 ref_18 Podolsky (ref_26) 2016; 17 ref_37 Jurel (ref_10) 2014; 20 Asri (ref_22) 2016; 83 Atchison (ref_14) 2018; 149 Hegde (ref_16) 2019; 17 Sharma (ref_30) 2015; 2015 Hegde (ref_42) 2018; 26 Nartowt (ref_25) 2020; 3 Vogelstein (ref_11) 1993; 9 Wulczyn (ref_24) 2021; 4 ref_23 ref_21 Sung (ref_3) 2021; 71 ref_41 ref_1 Tan (ref_40) 2016; 4 ref_2 Cai (ref_27) 2015; 11 ref_28 Chaturvedi (ref_9) 2017; 38 Shimpi (ref_13) 2018; 33 ref_8 Niu (ref_20) 2013; 2 ref_4 ref_6 |
| References_xml | – ident: ref_28 – volume: 9 start-page: 138 year: 1993 ident: ref_11 article-title: The multistep nature of cancer publication-title: Trends Genet. doi: 10.1016/0168-9525(93)90209-Z – volume: 17 start-page: 100254 year: 2019 ident: ref_16 article-title: Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment publication-title: Inform. Med. Unlocked doi: 10.1016/j.imu.2019.100254 – volume: 83 start-page: 1064 year: 2016 ident: ref_22 article-title: Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2016.04.224 – volume: 3 start-page: 10 year: 2010 ident: ref_39 article-title: The use of artificial intelligence to identify people at risk of oral cancer: Empirical evidence in Malaysian university publication-title: Int. J. Sci. Res. Educ. – volume: 80 start-page: S71 year: 2020 ident: ref_15 article-title: Positioning operations in the dental safety net to enhance value-based care delivery in an integrated health-care setting publication-title: J. Public Health Dent. doi: 10.1111/jphd.12392 – ident: ref_18 doi: 10.3390/jpm11090832 – volume: 17 start-page: 835 year: 2016 ident: ref_26 article-title: Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels publication-title: Asian Pac. J. Cancer Prev. doi: 10.7314/APJCP.2016.17.2.835 – ident: ref_34 – volume: 2 start-page: 403 year: 2013 ident: ref_20 article-title: Cancer survival disparities by health insurance status publication-title: Cancer Med. doi: 10.1002/cam4.84 – ident: ref_23 doi: 10.1109/HIBIT.2010.5478895 – volume: 4 start-page: e2482 year: 2016 ident: ref_40 article-title: A genetic programming approach to oral cancer prognosis publication-title: PeerJ doi: 10.7717/peerj.2482 – volume: 2 start-page: 866 year: 2014 ident: ref_7 article-title: Precancerous lesions of oral mucosa publication-title: World J. Clin. Cases doi: 10.12998/wjcc.v2.i12.866 – ident: ref_19 doi: 10.3390/cancers13184600 – ident: ref_35 doi: 10.1002/9780470725184 – volume: 20 start-page: 4 year: 2014 ident: ref_10 article-title: Genes and oral cancer publication-title: Indian J. Hum. Genet. doi: 10.4103/0971-6866.132745 – ident: ref_37 – ident: ref_1 – volume: 28 start-page: 143 year: 2020 ident: ref_17 article-title: Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting publication-title: Technol. Health Care doi: 10.3233/THC-191642 – volume: 3 start-page: 6 year: 2020 ident: ref_25 article-title: Robust Machine Learning for Colorectal Cancer Risk Prediction and Stratification publication-title: Front. Big Data doi: 10.3389/fdata.2020.00006 – volume: 26 start-page: 445 year: 2018 ident: ref_42 article-title: Tobacco use status from clinical notes using Natural Language Processing and rule based algorithm publication-title: Technol. Health Care doi: 10.3233/THC-171127 – ident: ref_21 doi: 10.3390/diagnostics12010203 – volume: 39 start-page: 59 year: 2015 ident: ref_38 article-title: The Application of Data Mining Techniques to Oral Cancer Prognosis publication-title: J. Med. Syst. doi: 10.1007/s10916-015-0241-3 – volume: 71 start-page: 209 year: 2021 ident: ref_3 article-title: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries publication-title: CA Cancer J. Clin. doi: 10.3322/caac.21660 – volume: 33 start-page: 359 year: 2018 ident: ref_13 article-title: Knowledgeability, Attitude and Behavior of Primary Care Providers Towards Oral Cancer: A Pilot Study publication-title: J. Cancer Educ. doi: 10.1007/s13187-016-1084-4 – ident: ref_6 – ident: ref_4 – ident: ref_33 – ident: ref_2 – volume: 179 start-page: 382 year: 1995 ident: ref_31 article-title: The use of artificial intelligence to identify people at risk of oral cancer and precancer publication-title: Br. Dent. J. doi: 10.1038/sj.bdj.4808932 – ident: ref_12 – volume: 4 start-page: 1 year: 2021 ident: ref_24 article-title: Interpretable survival prediction for colorectal cancer using deep learning publication-title: NPJ Digit. Med. doi: 10.1038/s41746-021-00427-2 – volume: 38 start-page: 44 year: 2017 ident: ref_9 article-title: The role of chronic mucosal trauma in oral cancer: A review of literature publication-title: Indian J. Med Paediatr. Oncol. doi: 10.4103/0971-5851.203510 – volume: 11 start-page: 791 year: 2015 ident: ref_27 article-title: Classification of lung cancer using ensemble-based feature selection and machine learning methods publication-title: Mol. BioSyst. doi: 10.1039/C4MB00659C – ident: ref_41 – volume: 2015 start-page: 234191 year: 2015 ident: ref_30 article-title: Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer publication-title: Sci. World, J. doi: 10.1155/2015/234191 – volume: 149 start-page: 850 year: 2018 ident: ref_14 article-title: Bridging the dental-medical divide: Case studies integrating oral health care and primary care publication-title: J. Am. Dent. Assoc. doi: 10.1016/j.adaj.2018.05.030 – volume: 7 start-page: 28223 year: 2015 ident: ref_5 article-title: Progress in oral personalized medicine: Contribution of ‘omics’ publication-title: J. Oral Microbiol. doi: 10.3402/jom.v7.28223 – ident: ref_32 doi: 10.1007/978-3-319-98298-4 – ident: ref_36 – volume: 302 start-page: 302 year: 2012 ident: ref_29 article-title: Framework for early detection and prevention of oral cancer using data mining publication-title: Int. J. Adv. Eng. Technol. – ident: ref_8 doi: 10.1111/odi.14123 |
| SSID | ssj0000852260 |
| Score | 2.1999016 |
| Snippet | Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an... |
| SourceID | pubmedcentral osti proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 614 |
| SubjectTerms | 60 APPLIED LIFE SCIENCES Alcohol Algorithms Cancer therapies Clinics Data mining Data models Data warehouses Datasets Electronic medical records Head & neck cancer Hispanic Americans Informatics Learning algorithms Machine learning Morbidity Mortality Oral cancer Oral carcinoma Oral cavity patient care management Patients Phenotypes Precision medicine Primary care Risk assessment Risk factors Tobacco Tumors |
| Title | Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35455730 https://www.proquest.com/docview/2652980764 https://www.proquest.com/docview/2654281547 https://www.osti.gov/servlets/purl/2470759 https://pubmed.ncbi.nlm.nih.gov/PMC9032985 |
| Volume | 12 |
| WOSCitedRecordID | wos000786311900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2075-4426 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000852260 issn: 2075-4426 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2075-4426 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000852260 issn: 2075-4426 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2075-4426 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000852260 issn: 2075-4426 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2075-4426 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000852260 issn: 2075-4426 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bb9MwFD5iHUK8MG6DsFEZaU-IaIntJM4T6qpNTKIlmmAqT1HiCyuXpLTZfgE_nOPEDS2aeOEpiWwplo59znfsz98BOCoY44j6MTsRkfG5KUs_Varw4wKDQajSuGxJNJfvk-lUzGZp5jbcVo5WufaJraNWtbR75Mc0jmgqMOvmbxc_fVs1yp6uuhIaO7BrVRJoS93L-j0WhBOILoLuWh7D7P746-JHSAMbxPhWIBrUuKBuA5l_cyU3gs_Z3v8O-yE8cLCTjLp58gju6Oox3Ju4g_Un8GuDPESKSpFLxOdduSVSG1KQaV3559VNYdnub8j4yh4DzZUmH3C0ZFzYChT4wBm0JBfz1Tcy6hU_Sbasm9pu9ZKWn0AmLX9TEyft-oWMnK75U_h0dvpx_M53BRp8yVnc-IkpVaCFDqk2ISZSNFDCCMalCHQZc0mN1EIZg47BhLrkcUCVVkIzg16FyYDtw6CqK_0cSBEbnpoiUtq-pGWJodUwo2XKGWOl8uD12lq5dOrltojG9xyzGGvafMO0Hhz1nRedaMft3Q6s2XPEGlYwV1pmkWxyyhPEUakHh2uL5m5dr_I_5vTgVd-MK9IesxSVrq_bPpjTITRNPHjWTZ5-FAwBa4RO1YNka1r1Haza93ZLNb9qVb_TgOGvoxf_HtYB3Kf2goZVowwPYdAsr_VLuCtvmvlqOYSdZCaGsHtyOs0uhu1iwa_sfJJ9_g2EmyF8 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VBQEX3o_QAkYqF9Soie28DgitFqquurusUKl6C44fdHkky25axJ3fw29knBe7qOLWA6dEspVEzjeeb-zPMwDbgjGOrB-jkzgwLjdZ5iZKCTcU6Ax8lYRZJaI5GkWTSXx8nEw34Fd7FsbKKts5sZqoVSHtGvkuDQOaxBh181fzb66tGmV3V9sSGjUsDvSP7xiyLV8OX-P_fU7p3pvDwb7bVBVwJWdh6UYmU56OtU-18ZH9U0_FJmZcxp7OQi6pkTpWxiCaja8zHnpUaRVrZtAUmPQYPvcSXEYaQWklFZx2azpIX5DNePUxQMYSb_fT_KtPPes0-Zrj6xVowOeR2r-1mSvObu_m_zZMt-BGQ6tJv7aD27Ch8ztwddwIB-7CzxVxFBG5IkcYf9TlpEhhiCCTIneH-Zmwav4dMjix21wzpclbHB0yELbCBl7QQhbk3Wz5mfS7jKZkuijKwi5lk0p_QcaVPlWTJnXtR9Jv8rbfg_cXMgb3oZcXuX4IRISGJ0YEStubJMuQOhhmtEw4YyxTDrxo0ZHKJju7LRLyJcUozUIpXYGSA9td53mdlOT8bpsWZilyKZsQWFrllCxTyiPkiYkDWy2C0mbeWqZ_4OPAs64ZZxy7jSRyXZxWfTBmReodOfCgBmv3FQwJeYBOw4FoDcZdB5vNfL0ln51UWc0Tj-Grg0f__qyncG3_cDxKR8PJwSZcp_Ywis286W9Br1yc6sdwRZ6Vs-XiSWWaBD5cNMh_A0PPe_k |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VBVVceD9CCxipXBDRJrbzOiC02rJi1XZZIah6C44f7RZIlt20iDu_il_HOC92UcWtB06JZCuJnG8839ifZwB2BGMcWT9GJ3FgXG6yzE2UEm4o0Bn4KgmzSkRzuB9NJvHRUTLdgF_tWRgrq2znxGqiVoW0a-R9GgY0iTHq5n3TyCKmu6PX82-urSBld1rbcho1RPb0j-8Yvi1fjXfxXz-ndPTmw_Ct21QYcCVnYelGJlOejrVPtfExEqCeik3MuIw9nYVcUiN1rIxBZBtfZzz0qNIq1sygWTDpMXzuFbgaYQOvZIPTbn0HqQwyG68-EshY4vVP51996lkHytecYK9AY76I4P6t01xxfKOb__OQ3YIbDd0mg9o-bsOGzu_A5kEjKLgLP1dEU0TkihxiXFKXmSKFIYJMitwd5-fCqvxfkuGJ3f6aKU3e4UiRobCVN_CClrMg72fLz2TQZTol00VRFnaJm1S6DHJQ6VY1aVLaHpNBk8_9Hny8lDG4D728yPVDICI0PDEiUNreJFmGlMIwo2XCGWOZcuBFi5RUNlnbbfGQLylGbxZW6QqsHNjpOs_rZCUXd9uykEuRY9lEwdIqqmSZUh4hf0wc2G7RlDbz2TL9AyUHnnXNOBPZ7SWR6-Ks6oOxLFLyyIEHNXC7r2BI1AN0Jg5Ea5DuOtgs5-st-eykynaeeAxfHTz692c9hU3Edro_nuxtwXVqz6jYhJz-NvTKxZl-DNfkeTlbLp5UVkrg02Vj_DcD-YS_ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Development+and+Validation+of+a+Non-Invasive%2C+Chairside+Oral+Cavity+Cancer+Risk+Assessment+Prototype+Using+Machine+Learning+Approach&rft.jtitle=Journal+of+personalized+medicine&rft.au=Shimpi%2C+Neel&rft.au=Glurich%2C+Ingrid&rft.au=Rostami%2C+Reihaneh&rft.au=Hegde%2C+Harshad&rft.date=2022-04-11&rft.issn=2075-4426&rft.eissn=2075-4426&rft.volume=12&rft.issue=4&rft_id=info:doi/10.3390%2Fjpm12040614&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2075-4426&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2075-4426&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2075-4426&client=summon |