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Journal Abstract Search

199 related items for PubMed ID: 30462745

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  • 4. Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing.
    Inglis JM, Bacchi S, Troelnikov A, Smith W, Shakib S.
    Int J Med Inform; 2021 Dec; 156():104611. PubMed ID: 34653809
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  • 5. Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records.
    Lee S, Choi J, Kim HS, Kim GJ, Lee KH, Park CH, Han J, Yoon D, Park MY, Park RW, Kang HR, Kim JH.
    J Am Med Inform Assoc; 2017 Jul 01; 24(4):697-708. PubMed ID: 28087585
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  • 6. Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases.
    Zheng Y, Peng H, Zhang X, Zhao Z, Yin J, Li J.
    BMC Bioinformatics; 2018 Dec 31; 19(Suppl 19):517. PubMed ID: 30598065
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  • 7. A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding.
    McMaster C, Liew D, Keith C, Aminian P, Frauman A.
    Drug Saf; 2019 Jun 31; 42(6):721-725. PubMed ID: 30725336
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  • 8. Predicting post-stroke pneumonia using deep neural network approaches.
    Ge Y, Wang Q, Wang L, Wu H, Peng C, Wang J, Xu Y, Xiong G, Zhang Y, Yi Y.
    Int J Med Inform; 2019 Dec 31; 132():103986. PubMed ID: 31629312
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  • 11. Application and optimisation of the Comparison on Extreme Laboratory Tests (CERT) algorithm for detection of adverse drug reactions: Transferability across national boundaries.
    Tham MY, Ye Q, Ang PS, Fan LY, Yoon D, Park RW, Ling ZJ, Yip JW, Tai BC, Evans SJ, Sung C.
    Pharmacoepidemiol Drug Saf; 2018 Jan 31; 27(1):87-94. PubMed ID: 29108136
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  • 12. Evaluating performance of electronic healthcare records and spontaneous reporting data in drug safety signal detection.
    Patadia VK, Schuemie MJ, Coloma P, Herings R, van der Lei J, Straus S, Sturkenboom M, Trifirò G.
    Int J Clin Pharm; 2015 Feb 31; 37(1):94-104. PubMed ID: 25488315
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  • 13. Machine learning models to detect and predict patient safety events using electronic health records: A systematic review.
    Deimazar G, Sheikhtaheri A.
    Int J Med Inform; 2023 Dec 31; 180():105246. PubMed ID: 37837710
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  • 15. Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data.
    Farnoush A, Sedighi-Maman Z, Rasoolian B, Heath JJ, Fallah B.
    Sci Rep; 2024 Oct 09; 14(1):23636. PubMed ID: 39384938
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  • 16. Early Detection of Septic Shock Onset Using Interpretable Machine Learners.
    Misra D, Avula V, Wolk DM, Farag HA, Li J, Mehta YB, Sandhu R, Karunakaran B, Kethireddy S, Zand R, Abedi V.
    J Clin Med; 2021 Jan 15; 10(2):. PubMed ID: 33467539
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  • 17. Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system.
    Lee JE, Kim JH, Bae JH, Song I, Shin JY.
    Sci Rep; 2022 Sep 01; 12(1):14869. PubMed ID: 36050484
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  • 18. Predictive modeling of structured electronic health records for adverse drug event detection.
    Zhao J, Henriksson A, Asker L, Boström H.
    BMC Med Inform Decis Mak; 2015 Sep 01; 15 Suppl 4(Suppl 4):S1. PubMed ID: 26606038
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  • 19. A signal detection method to detect adverse drug reactions using a parametric time-to-event model in simulated cohort data.
    Cornelius VR, Sauzet O, Evans SJ.
    Drug Saf; 2012 Jul 01; 35(7):599-610. PubMed ID: 22702641
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  • 20. Machine Learning for Detection of Safety Signals From Spontaneous Reporting System Data: Example of Nivolumab and Docetaxel.
    Bae JH, Baek YH, Lee JE, Song I, Lee JH, Shin JY.
    Front Pharmacol; 2020 Jul 01; 11():602365. PubMed ID: 33628176
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