121 related articles for article (PubMed ID: 38189744)
1. Applying machine learning to identify pediatric patients with newly diagnosed acute lymphoblastic leukemia using administrative data.
Cao L; Huang YS; Getz KD; Seif AE; Ruiz J; Miller TP; Fisher BT; Aplenc R; Li Y
Pediatr Blood Cancer; 2024 Mar; 71(3):e30858. PubMed ID: 38189744
[TBL] [Abstract][Full Text] [Related]
2. Leveraging machine learning to identify acute myeloid leukemia patients and their chemotherapy regimens in an administrative database.
Cao L; Huang YS; Wu C; Getz K; Miller TP; Ruiz J; Fisher BT; Seif AE; Aplenc R; Li Y
Pediatr Blood Cancer; 2023 May; 70(5):e30260. PubMed ID: 36815580
[TBL] [Abstract][Full Text] [Related]
3. Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach.
Mahmood N; Shahid S; Bakhshi T; Riaz S; Ghufran H; Yaqoob M
Med Biol Eng Comput; 2020 Nov; 58(11):2631-2640. PubMed ID: 32840766
[TBL] [Abstract][Full Text] [Related]
4. Machine learning to predict high-dose methotrexate-related neutropenia and fever in children with B-cell acute lymphoblastic leukemia.
Zhan M; Chen ZB; Ding CC; Qu Q; Wang GQ; Liu S; Wen FQ
Leuk Lymphoma; 2021 Oct; 62(10):2502-2513. PubMed ID: 33899650
[TBL] [Abstract][Full Text] [Related]
5. A machine learning model for predicting congenital heart defects from administrative data.
Shi H; Book W; Raskind-Hood C; Downing KF; Farr SL; Bell MN; Sameni R; Rodriguez FH; Kamaleswaran R
Birth Defects Res; 2023 Nov; 115(18):1693-1707. PubMed ID: 37681293
[TBL] [Abstract][Full Text] [Related]
6. Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia.
Pan L; Liu G; Lin F; Zhong S; Xia H; Sun X; Liang H
Sci Rep; 2017 Aug; 7(1):7402. PubMed ID: 28784991
[TBL] [Abstract][Full Text] [Related]
7. Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning.
Fathi E; Rezaee MJ; Tavakkoli-Moghaddam R; Alizadeh A; Montazer A
Proc Inst Mech Eng H; 2020 Oct; 234(10):1051-1069. PubMed ID: 32633668
[TBL] [Abstract][Full Text] [Related]
8. Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital.
Kashef A; Khatibi T; Mehrvar A
Asian Pac J Cancer Prev; 2020 Nov; 21(11):3211-3219. PubMed ID: 33247677
[TBL] [Abstract][Full Text] [Related]
9. Establishment of an 11-year cohort of 8733 pediatric patients hospitalized at United States free-standing children's hospitals with de novo acute lymphoblastic leukemia from health care administrative data.
Fisher BT; Harris T; Torp K; Seif AE; Shah A; Huang YS; Bailey LC; Kersun LS; Reilly AF; Rheingold SR; Walker D; Li Y; Aplenc R
Med Care; 2014 Jan; 52(1):e1-6. PubMed ID: 22410405
[TBL] [Abstract][Full Text] [Related]
10. Identifying relapses and stem cell transplants in pediatric acute lymphoblastic leukemia using administrative data: Capturing national outcomes irrespective of trial enrollment.
Cahen VC; Li Y; Getz KD; Elgarten CW; DiNofia AM; Wilkes JJ; Winestone LE; Huang YV; Miller TP; Gramatges MM; Rabin KR; Fisher BT; Aplenc R; Seif AE
Pediatr Blood Cancer; 2021 Sep; 68(9):e28315. PubMed ID: 32391940
[TBL] [Abstract][Full Text] [Related]
11. Identifying lupus patients in electronic health records: Development and validation of machine learning algorithms and application of rule-based algorithms.
Jorge A; Castro VM; Barnado A; Gainer V; Hong C; Cai T; Cai T; Carroll R; Denny JC; Crofford L; Costenbader KH; Liao KP; Karlson EW; Feldman CH
Semin Arthritis Rheum; 2019 Aug; 49(1):84-90. PubMed ID: 30665626
[TBL] [Abstract][Full Text] [Related]
12. Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study.
Shouval R; Labopin M; Bondi O; Mishan-Shamay H; Shimoni A; Ciceri F; Esteve J; Giebel S; Gorin NC; Schmid C; Polge E; Aljurf M; Kroger N; Craddock C; Bacigalupo A; Cornelissen JJ; Baron F; Unger R; Nagler A; Mohty M
J Clin Oncol; 2015 Oct; 33(28):3144-51. PubMed ID: 26240227
[TBL] [Abstract][Full Text] [Related]
13. Validation of administrative case ascertainment algorithms for chronic childhood arthritis in Manitoba, Canada.
Shiff NJ; Oen K; Rabbani R; Lix LM
Rheumatol Int; 2017 Sep; 37(9):1575-1584. PubMed ID: 28502061
[TBL] [Abstract][Full Text] [Related]
14. Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses.
Wu B; Chow W; Sakthivel M; Kakade O; Gupta K; Israel D; Chen YW; Kuruvilla AS
Adv Ther; 2021 Feb; 38(2):1314-1327. PubMed ID: 33432543
[TBL] [Abstract][Full Text] [Related]
15. Development and validation of algorithms to identify newly diagnosed type 1 and type 2 diabetes in pediatric population using electronic medical records and claims data.
Teltsch DY; Fazeli Farsani S; Swain RS; Kaspers S; Huse S; Cristaldi C; Nordstrom BL; Brodovicz KG
Pharmacoepidemiol Drug Saf; 2019 Feb; 28(2):234-243. PubMed ID: 30677205
[TBL] [Abstract][Full Text] [Related]
16. IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.
Bibi N; Sikandar M; Ud Din I; Almogren A; Ali S
J Healthc Eng; 2020; 2020():6648574. PubMed ID: 33343851
[TBL] [Abstract][Full Text] [Related]
17. Assembly of a cohort of children treated for acute myeloid leukemia at free-standing children's hospitals in the United States using an administrative database.
Kavcic M; Fisher BT; Torp K; Li Y; Huang YS; Seif AE; Vujkovic M; Aplenc R
Pediatr Blood Cancer; 2013 Mar; 60(3):508-11. PubMed ID: 23192853
[TBL] [Abstract][Full Text] [Related]
18. Validation of algorithms to identify colorectal cancer patients from administrative claims data of a Japanese hospital.
Hirano T; Negishi M; Kuwatsuru Y; Arai M; Wakabayashi R; Saito N; Kuwatsuru R
BMC Health Serv Res; 2023 Mar; 23(1):274. PubMed ID: 36944932
[TBL] [Abstract][Full Text] [Related]
19. Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.
Jamian L; Wheless L; Crofford LJ; Barnado A
Arthritis Res Ther; 2019 Dec; 21(1):305. PubMed ID: 31888720
[TBL] [Abstract][Full Text] [Related]
20. A systematic review of validated methods for identifying lymphoma using administrative data.
Herman RA; Gilchrist B; Link BK; Carnahan R
Pharmacoepidemiol Drug Saf; 2012 Jan; 21 Suppl 1():203-12. PubMed ID: 22262607
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]