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Journal Abstract Search

211 related items for PubMed ID: 38996687

  • 1. Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population.
    Kaushik M, Mahajan S, Machahary N, Thakran S, Chopra S, Tomar RV, Kushwaha SS, Agarwal R, Sharma S, Kukreti R, Biswal B.
    Epilepsy Res; 2024 Sep; 205():107404. PubMed ID: 38996687
    [Abstract] [Full Text] [Related]

  • 2. Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning.
    Yao L, Cai M, Chen Y, Shen C, Shi L, Guo Y.
    Epilepsy Behav; 2019 Jul; 96():92-97. PubMed ID: 31121513
    [Abstract] [Full Text] [Related]

  • 3. Machine learning model to predict the efficacy of antiseizure medications in patients with familial genetic generalized epilepsy.
    Wu J, Wang Y, Xiang L, Gu Y, Yan Y, Li L, Tian X, Jing W, Wang X.
    Epilepsy Res; 2022 Mar; 181():106888. PubMed ID: 35176621
    [Abstract] [Full Text] [Related]

  • 4. Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning.
    Li SP, Lin LC, Yang RC, Ouyang CS, Chiu YH, Wu MH, Tu YF, Chang TM, Wu RC.
    Epilepsy Behav; 2024 Feb; 151():109647. PubMed ID: 38232558
    [Abstract] [Full Text] [Related]

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  • 6. Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms.
    Zhang X, Feng C, Bai X, Peng X, Guo Q, Chen L, Xue J.
    Arch Esp Urol; 2023 Sep; 76(7):494-503. PubMed ID: 37867334
    [Abstract] [Full Text] [Related]

  • 7. Prospective study of epilepsy with generalized tonic-clonic seizures alone: Clinical features, response to treatment, and likelihood of medication withdrawal.
    Jaafar F, Wazne J, Hmaimess G, Nasreddine W, Beydoun A, Shatila A, Beydoun A.
    Epilepsia Open; 2024 Aug; 9(4):1426-1436. PubMed ID: 38819591
    [Abstract] [Full Text] [Related]

  • 8. Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.
    Alie MS, Negesse Y, Kindie K, Merawi DS.
    BMC Public Health; 2024 Jun 28; 24(1):1728. PubMed ID: 38943093
    [Abstract] [Full Text] [Related]

  • 9. Deep Learning and Machine Learning with Grid Search to Predict Later Occurrence of Breast Cancer Metastasis Using Clinical Data.
    Jiang X, Xu C.
    J Clin Med; 2022 Sep 29; 11(19):. PubMed ID: 36233640
    [Abstract] [Full Text] [Related]

  • 10. Machine Learning Models Identify New Inhibitors for Human OATP1B1.
    Lane TR, Urbina F, Zhang X, Fye M, Gerlach J, Wright SH, Ekins S.
    Mol Pharm; 2022 Nov 07; 19(11):4320-4332. PubMed ID: 36269563
    [Abstract] [Full Text] [Related]

  • 11. A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.
    Detrait MY, Warnon S, Lagasse R, Dumont L, De Prophétis S, Hansenne A, Raedemaeker J, Robin V, Verstraete G, Gillain A, Depasse N, Jacmin P, Pranger D.
    PLoS One; 2024 Nov 07; 19(10):e0311261. PubMed ID: 39352921
    [Abstract] [Full Text] [Related]

  • 12. Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method.
    Ayman U, Zia MS, Okon OD, Rehman NU, Meraj T, Ragab AE, Rauf HT.
    Biomedicines; 2023 Mar 07; 11(3):. PubMed ID: 36979795
    [Abstract] [Full Text] [Related]

  • 13. Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.
    Ozcan I, Aydin H, Cetinkaya A.
    Asian Pac J Cancer Prev; 2022 Oct 01; 23(10):3287-3297. PubMed ID: 36308351
    [Abstract] [Full Text] [Related]

  • 14. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.
    Wang J, Chen H, Wang H, Liu W, Peng D, Zhao Q, Xiao M.
    J Med Internet Res; 2023 Apr 06; 25():e43815. PubMed ID: 37023416
    [Abstract] [Full Text] [Related]

  • 15. Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches.
    Shariati MM, Eslami S, Shoeibi N, Eslampoor A, Sedaghat M, Gharaei H, Zarei-Ghanavati S, Derakhshan A, Abrishami M, Abrishami M, Hosseini SM, Rad SS, Astaneh MA, Farimani RM.
    BMC Med Inform Decis Mak; 2024 May 21; 24(1):131. PubMed ID: 38773484
    [Abstract] [Full Text] [Related]

  • 16. Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction.
    Güven AT, Özdede M, Şener YZ, Yıldırım AO, Altıntop SE, Yeşilyurt B, Uyaroğlu OA, Tanrıöver MD.
    Eur J Intern Med; 2023 Aug 21; 114():74-83. PubMed ID: 37217407
    [Abstract] [Full Text] [Related]

  • 17. Can we predict anti-seizure medication response in focal epilepsy using machine learning?
    Lee DA, Lee HJ, Park BS, Lee YJ, Park KM.
    Clin Neurol Neurosurg; 2021 Dec 21; 211():107037. PubMed ID: 34800813
    [Abstract] [Full Text] [Related]

  • 18. Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.
    Zakariaee SS, Naderi N, Ebrahimi M, Kazemi-Arpanahi H.
    Sci Rep; 2023 Jul 13; 13(1):11343. PubMed ID: 37443373
    [Abstract] [Full Text] [Related]

  • 19. [Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].
    Xie Z, Jin J, Liu D, Lu S, Yu H, Han D, Sun W, Huang M.
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2024 Apr 13; 36(4):345-352. PubMed ID: 38813626
    [Abstract] [Full Text] [Related]

  • 20. Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models.
    Huang L, Li J, Huang M, Zhuang J, Yuan H, Jia Q, Zeng D, Que L, Xi Y, Lin J, Dong Y.
    Eur Radiol; 2020 Mar 13; 30(3):1369-1377. PubMed ID: 31705256
    [Abstract] [Full Text] [Related]

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