Publications

Detailed Information

Stratifying non-small cell lung cancer patients using an inverse of the treatment decision rules: validation using electronic health records with application to an administrative database

DC Field Value Language
dc.contributor.authorKim, Min-Hyung-
dc.contributor.authorPark, Sojung-
dc.contributor.authorPark, Yu Rang-
dc.contributor.authorJi, Wonjun-
dc.contributor.authorKim, Seul-Gi-
dc.contributor.authorChoo, Minji-
dc.contributor.authorHwang, Seung-Sik-
dc.contributor.authorLee, Jae Cheol-
dc.contributor.authorKim, Hyeong Ryul-
dc.contributor.authorChoi, Chang-Min-
dc.date.accessioned2023-01-25T02:28:03Z-
dc.date.available2023-01-25T02:28:03Z-
dc.date.issued2023-01-06-
dc.identifier.citationMedical Informatics and Decision Making, 3(1)ko_KR
dc.identifier.issn1472-6947-
dc.identifier.urihttps://hdl.handle.net/10371/189009-
dc.description.abstractTo validate a stratification method using an inverse of treatment decision rules that can classify non-small cell lung cancer (NSCLC) patients in real-world treatment records.
(1) To validate the index classifier against the TNM 7th edition, we analyzed electronic health records of NSCLC patients diagnosed from 2011 to 2015 in a tertiary referral hospital in Seoul, Korea. Predictive accuracy, stage-specific sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and c-statistic were measured. (2) To apply the index classifier in an administrative database, we analyzed NSCLC patients in Korean National Health Insurance Database, 2002–2013. Differential survival rates among the classes were examined with the log-rank test, and class-specific survival rates were compared with the reference survival rates.
(1) In the validation study (N = 1375), the overall accuracy was 93.8% (95% CI: 92.5–95.0%). Stage-specific c-statistic was the highest for stage I (0.97, 95% CI: 0.96–0.98) and the lowest for stage III (0.82, 95% CI: 0.77–0.87). (2) In the application study (N = 71,593), the index classifier showed a tendency for differentiating survival probabilities among classes. Compared to the reference TNM survival rates, the index classification under-estimated the survival probability for stages IA, IIIB, and IV, and over-estimated it for stages IIA and IIB.
The inverse of the treatment decision rules has a potential to supplement a routinely collected database with information encoded in the treatment decision rules to classify NSCLC patients. It requires further validation and replication in multiple clinical settings.
ko_KR
dc.language.isoenko_KR
dc.publisherBMCko_KR
dc.subjectTreatment decision rules-
dc.subjectTNM Stage-
dc.subjectNon-small cell lung cancer-
dc.subjectElectronic health record-
dc.subjectAdministrative database-
dc.titleStratifying non-small cell lung cancer patients using an inverse of the treatment decision rules: validation using electronic health records with application to an administrative databaseko_KR
dc.typeSoftwareko_KR
dc.typeTechnical Reportko_KR
dc.typeThesisko_KR
dc.typeVideoko_KR
dc.typeWorking Paperko_KR
dc.typeOtherko_KR
dc.identifier.doihttps://doi.org/10.1186/s12911-022-02088-xko_KR
dc.citation.journaltitleMedical Informatics and Decision Makingko_KR
dc.language.rfc3066en-
dc.rights.holderThe Author(s)-
dc.date.updated2023-01-08T04:13:53Z-
dc.citation.endpage10ko_KR
dc.citation.number1ko_KR
dc.citation.startpage1ko_KR
dc.citation.volume3ko_KR
Appears in Collections:
Files in This Item:

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share