These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

164 related articles for article (PubMed ID: 34352026)

  • 1. Crash severity analysis of vulnerable road users using machine learning.
    Komol MMR; Hasan MM; Elhenawy M; Yasmin S; Masoud M; Rakotonirainy A
    PLoS One; 2021; 16(8):e0255828. PubMed ID: 34352026
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Exploring the factors contribute to the injury severities of vulnerable roadway user involved crashes.
    Dong C; Khattak AJ; Shao C; Xie K
    Int J Inj Contr Saf Promot; 2019 Sep; 26(3):302-314. PubMed ID: 31169068
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Deriving functional safety (ISO 26262) S-parameters for vulnerable road users from national crash data.
    Krampe J; Junge M
    Accid Anal Prev; 2021 Feb; 150():105884. PubMed ID: 33360036
    [TBL] [Abstract][Full Text] [Related]  

  • 4. The road to recovery for vulnerable road users hospitalised for orthopaedic injury following an on-road crash.
    Devlin A; Beck B; Simpson PM; Ekegren CL; Giummarra MJ; Edwards ER; Cameron PA; Liew S; Oppy A; Richardson M; Page R; Gabbe BJ
    Accid Anal Prev; 2019 Nov; 132():105279. PubMed ID: 31491683
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates.
    Sengupta A; Ilgin Guler S; Gayah VV; Warchol S
    Accid Anal Prev; 2024 Aug; 203():107614. PubMed ID: 38781631
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm.
    Zhu C; Brown CT; Dadashova B; Ye X; Sohrabi S; Potts I
    Accid Anal Prev; 2023 Mar; 182():106964. PubMed ID: 36638723
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Unveiling the relevance of traffic enforcement cameras on the severity of vehicle-pedestrian collisions in an urban environment with machine learning models.
    Pineda-Jaramillo J; Barrera-Jiménez H; Mesa-Arango R
    J Safety Res; 2022 Jun; 81():225-238. PubMed ID: 35589294
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparison of epidemiology and injury profile between vulnerable road users and motor vehicle occupants in road traffic fatalities.
    Kim SC; Lee HJ; Kim JM; Kong SY; Park JS; Jeon HJ; In YN; Kim H; Lee SW; Kim YT
    Traffic Inj Prev; 2019; 20(6):581-587. PubMed ID: 31329479
    [No Abstract]   [Full Text] [Related]  

  • 9. A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw.
    Ijaz M; Lan L; Zahid M; Jamal A
    Accid Anal Prev; 2021 May; 154():106094. PubMed ID: 33756425
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Describing and comparing the characteristics of injured bicyclists and other injured road users: a prospective cohort study.
    Gopinath B; Jagnoor J; Craig A; Kifley A; Dinh M; Ivers R; Boufous S; Cameron ID
    BMC Public Health; 2016 Apr; 16():324. PubMed ID: 27074801
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Modelling injury severity of bicyclists in bicycle-car crashes at intersections.
    Bahrololoom S; Young W; Logan D
    Accid Anal Prev; 2020 Sep; 144():105597. PubMed ID: 32559658
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes.
    Lu W; Liu J; Fu X; Yang J; Jones S
    Accid Anal Prev; 2022 Apr; 168():106622. PubMed ID: 35231695
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Fatal and serious injuries related to vulnerable road users in Canada.
    Vanlaar W; Mainegra Hing M; Brown S; McAteer H; Crain J; McFaull S
    J Safety Res; 2016 Sep; 58():67-77. PubMed ID: 27620936
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Road traffic deaths in Brazil: rising trends in pedestrian and motorcycle occupant deaths.
    Chandran A; Sousa TR; Guo Y; Bishai D; Pechansky F;
    Traffic Inj Prev; 2012; 13 Suppl 1():11-6. PubMed ID: 22414123
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A comparative study on machine learning based algorithms for prediction of motorcycle crash severity.
    Wahab L; Jiang H
    PLoS One; 2019; 14(4):e0214966. PubMed ID: 30947250
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of traffic-injury related hospitalisation between bicyclists and motorcyclists in Taiwan.
    Pai CW; Lin HY; Tsai SH; Chen PL
    PLoS One; 2018; 13(1):e0191221. PubMed ID: 29342208
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones.
    Rahman MS; Abdel-Aty M; Hasan S; Cai Q
    J Safety Res; 2019 Sep; 70():275-288. PubMed ID: 31848006
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Road accident fatality risks for "vulnerable" versus "protected" road users in northern Ghana.
    Damsere-Derry J; Palk G; King M
    Traffic Inj Prev; 2017 Oct; 18(7):736-743. PubMed ID: 28296466
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Road crash fatality rates in France: a comparison of road user types, taking account of travel practices.
    Bouaoun L; Haddak MM; Amoros E
    Accid Anal Prev; 2015 Feb; 75():217-25. PubMed ID: 25496915
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A population-based case-control study of hospitalisation due to head injuries among bicyclists and motorcyclists in Taiwan.
    Pai CW; Chen YC; Lin HY; Chen PL
    BMJ Open; 2017 Nov; 7(11):e018574. PubMed ID: 29122803
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.