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 *

111 related articles for article (PubMed ID: 38901160)

  • 1. Classification of autonomous vehicle crash severity: Solving the problems of imbalanced datasets and small sample size.
    Kuo PF; Hsu WT; Lord D; Putra IGB
    Accid Anal Prev; 2024 Sep; 205():107666. PubMed ID: 38901160
    [TBL] [Abstract][Full Text] [Related]  

  • 2. What can we learn from autonomous vehicle collision data on crash severity? A cost-sensitive CART approach.
    Zhu S; Meng Q
    Accid Anal Prev; 2022 Sep; 174():106769. PubMed ID: 35858521
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach.
    Boggs AM; Wali B; Khattak AJ
    Accid Anal Prev; 2020 Feb; 135():105354. PubMed ID: 31790970
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis.
    Lee S; Arvin R; Khattak AJ
    Accid Anal Prev; 2023 Mar; 181():106932. PubMed ID: 36580765
    [TBL] [Abstract][Full Text] [Related]  

  • 5. What can we learn from the AV crashes? - An association rule analysis for identifying the contributing risky factors.
    Liu P; Guo Y; Liu P; Ding H; Cao J; Zhou J; Feng Z
    Accid Anal Prev; 2024 May; 199():107492. PubMed ID: 38428241
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Analyzing relationships between latent topics in autonomous vehicle crash narratives and crash severity using natural language processing techniques and explainable XGBoost.
    Li P; Chen S; Yue L; Xu Y; Noyce DA
    Accid Anal Prev; 2024 Aug; 203():107605. PubMed ID: 38743983
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Crash injury severity prediction considering data imbalance: A Wasserstein generative adversarial network with gradient penalty approach.
    Li Y; Yang Z; Xing L; Yuan C; Liu F; Wu D; Yang H
    Accid Anal Prev; 2023 Nov; 192():107271. PubMed ID: 37659275
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches.
    Wang S; Li Z
    PLoS One; 2019; 14(3):e0214550. PubMed ID: 30921396
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automated vehicle crash sequences: Patterns and potential uses in safety testing.
    Song Y; Chitturi MV; Noyce DA
    Accid Anal Prev; 2021 Apr; 153():106017. PubMed ID: 33578268
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data.
    Mohammadpour SI; Khedmati M; Zada MJH
    PLoS One; 2023; 18(3):e0281901. PubMed ID: 36947539
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Classification of motor vehicle crash injury severity: A hybrid approach for imbalanced data.
    Jeong H; Jang Y; Bowman PJ; Masoud N
    Accid Anal Prev; 2018 Nov; 120():250-261. PubMed ID: 30173007
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Statistical analysis of the patterns and characteristics of connected and autonomous vehicle involved crashes.
    Xu C; Ding Z; Wang C; Li Z
    J Safety Res; 2019 Dec; 71():41-47. PubMed ID: 31862043
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros.
    Chen T; Lu Y; Fu X; Sze NN; Ding H
    Accid Anal Prev; 2022 Jan; 164():106496. PubMed ID: 34801838
    [TBL] [Abstract][Full Text] [Related]  

  • 14. How would autonomous vehicles behave in real-world crash scenarios?
    Zhou R; Zhang G; Huang H; Wei Z; Zhou H; Jin J; Chang F; Chen J
    Accid Anal Prev; 2024 Jul; 202():107572. PubMed ID: 38657314
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology.
    Liu Q; Wang X; Wu X; Glaser Y; He L
    Accid Anal Prev; 2021 Sep; 159():106281. PubMed ID: 34273622
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Injury severity analysis of two-vehicle crashes at unsignalized intersections using mixed logit models.
    Yuan R; Gan J; Peng Z; Xiang Q
    Int J Inj Contr Saf Promot; 2022 Sep; 29(3):348-359. PubMed ID: 35276053
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic inferences.
    Novat N; Kidando E; Kutela B; Kitali AE
    J Safety Res; 2023 Feb; 84():251-260. PubMed ID: 36868654
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling.
    Morris C; Yang JJ
    Accid Anal Prev; 2021 Sep; 159():106240. PubMed ID: 34144225
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections.
    Liu Q; Wang X; Liu S; Yu C; Glaser Y
    Accid Anal Prev; 2024 Feb; 195():107383. PubMed ID: 37984113
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine learning-based injury severity prediction of level 1 trauma center enrolled patients associated with car-to-car crashes in Korea.
    Kong JS; Lee KH; Kim OH; Lee HY; Kang CY; Choi D; Kim SC; Jeong H; Kang DR; Sung TE
    Comput Biol Med; 2023 Feb; 153():106393. PubMed ID: 36586232
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.