A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters
Open accessAbstract
Abstract The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" to develop the prediction algorithm, and the remaining 20% constituted the "validation cohort" to test the finally selected model. Fifteen ML models were optimized and evaluated by tenfold cross-validation on the "derivation cohort,” and were filtered by the mean absolute error (MAE). On the whole, the nonlinear models outperformed the linear models. The extra-trees’ regression algorithm delivered good performance, and was chosen to establish the predictive model. The important features were then analyzed and parameters of the model adjusted to develop the best prediction model, which accurately described the C/D ratio of LTG, especially in the intermediate-to-high range (≥ 22.1 μg mL−1 g−1 day), as illustrated by a minimal bias (mean relative error (%) = + 3%), good precision (MAE = 8.7 μg mL−1 g−1 day), and a high percentage of predictions within ± 20% of the empirical values (60.47%). This is the first study, to the best of our knowledge, to use ML algorithms to predict the C/D ratio of LTG. The results here can help clinicians adjust doses of LTG administered to patients to minimize adverse reactions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ML algorithms analyze data from wearable devices such as accelerometers and gyroscopes to monitor motor symptoms and allow healthcare providers to adjust medication dosages.
“They analyze data from wearable devices, such as accelerometers and gyroscopes, to monitor motor symptoms and allow healthcare providers to adjust medication dosages [189,190].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [190]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Consensus Guidelines for Therapeutic Drug Monitoring in Neuropsychopharmacology: Update 2017
Hiemke, C., Bergemann, N., Clement, H. ‐., Conca, A., Deckert, J., Domschke, K., Eckermann, G., Egberts, K. M., Gerlach, M., Greiner, C., et al. (2017) · Pharmacopsychiatry · doi:10.1055/s-0043-116492 · Cited by 1,466 · see citing works
Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047 · see citing works
Beware of R 2 : Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
Alexander, D. L., Tropsha, A., & Winkler, D. A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00206 · Cited by 754 · see citing works
Churpek, M. M., Yuen, T. C., Winslow, C., Meltzer, D. O., Kattan, M. W., & Edelson, D. P. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001571 · Cited by 638 · see citing works
How Machine Learning Will Transform Biomedicine
Goecks, J., Jalili, V., Heiser, L. M., & Gray, J. W. (2020) · Cell · doi:10.1016/j.cell.2020.03.022 · Cited by 593 · see citing works
Machine learning: applications of artificial intelligence to imaging and diagnosis
Nichols, J., Chan, H. H., & Baker, M. A. B. (2018) · Biophysical Reviews · doi:10.1007/s12551-018-0449-9 · Cited by 458 · see citing works
Prediction of fatty liver disease using machine learning algorithms
Wu, C.-C., Yeh, W., Hsu, W., Islam, M. M., Nguyen, P., Poly, T. N., Wang, Y., Yang, H., & Li, Y. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.12.032 · Cited by 347 · see citing works
Goa, K. L., Ross, S. R., & Chrisp, P. (1993) · Drugs · doi:10.2165/00003495-199346010-00009 · Cited by 308 · see citing works
Natural and Artificial Intelligence in Neurosurgery: A Systematic Review
Senders, J. T., Arnaout, O. M., Karhade, A. V., Dasenbrock, H. H., Gormley, W. B., Broekman, M. L. D., & Smith, T. R. (2017) · Neurosurgery · doi:10.1093/neuros/nyx384 · Cited by 264 · see citing works
Pharmacokinetic Variability of Newer Antiepileptic Drugs
Johannessen, S. I., & Tomson, T. (2006) · Clinical Pharmacokinetics · doi:10.2165/00003088-200645110-00002 · Cited by 222 · see citing works
Interactions between antiepileptic drugs and hormones
Svalheim, S., Sveberg, L., Mochol, M., & Taubøll, E. (2015) · Seizure · doi:10.1016/j.seizure.2015.02.022 · Cited by 173 · see citing works
Pharmacokinetic Interactions Between Antiepileptic Drugs
Riva, R., Albani, F., Contin, M., & Baruzzi, A. (1996) · Clinical Pharmacokinetics · doi:10.2165/00003088-199631060-00005 · Cited by 157 · see citing works
Tang, J., Liu, R., Zhang, Y.-L., Liu, M.-Z., Hu, Y.-F., Shao, M.-J., Zhu, L.-J., Xin, H.-W., Feng, G.-W., Shang, W.-J., et al. (2017) · Scientific Reports · doi:10.1038/srep42192 · Cited by 154 · see citing works
Ma, Z., Wang, P., Gao, Z., Wang, R., & Khalighi, K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205872 · Cited by 142 · see citing works
Predicting breast cancer risk using personal health data and machine learning models
Stark, G. F., Hart, G. R., Nartowt, B. J., & Deng, J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0226765 · Cited by 138 · see citing works
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. (2017) · Scientific Reports · doi:10.1038/s41598-017-07408-0 · Cited by 134 · see citing works
Neural Network Predicted Peak and Trough Gentamicin Concentrations
Brier, M. E., Zurada, J. M., & Aronoff, G. R. (1995) · Pharmaceutical Research · doi:10.1023/a:1016260720218 · Cited by 107 · see citing works
de León, J., Susce, M. T., Pan, R. M.-D., Wedlund, P. J., Orrego, M., & DÍAZ, F. J. (2007) · Pharmacopsychiatry · doi:10.1055/s-2007-973836 · Cited by 105 · see citing works
Correlating lamotrigine serum concentrations with tolerability in patients with epilepsy
Hirsch, L. J., Weintraub, D. B., Du, Y., Buchsbaum, R., Spencer, H. T., Hager, M., Straka, T., Bazil, C. W., Adams, D. J., Resor, S. R., et al. (2004) · Neurology · doi:10.1212/01.wnl.0000138424.33979.0c · Cited by 104 · see citing works
May, T. W., Rambeck, B., & Jürgens, U. H. (1999) · Therapeutic Drug Monitoring · doi:10.1097/00007691-199904000-00007 · Cited by 101 · see citing works
Samsonsen, C., Reimers, A., Bråthen, G., Helde, G., & Brodtkorb, E. (2014) · Epilepsia · doi:10.1111/epi.12801 · Cited by 100 · see citing works
Big-Data Analysis, Cluster Analysis, and Machine-Learning Approaches
Alonso‐Betanzos, A., & Bolón‐Canedo, V. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-77932-4_37 · Cited by 81 · see citing works
de Leon, J. (2020) · Neuropharmacology · doi:10.1016/j.neuropharm.2019.05.033 · Cited by 75 · see citing works
Revisiting Warfarin Dosing Using Machine Learning Techniques
Sharabiani, A., Bress, A. P., Douzali, E., & Darabi, H. (2015) · Computational and Mathematical Methods in Medicine · doi:10.1155/2015/560108 · Cited by 72 · see citing works
Gene-environment interactions informing therapeutic approaches to cognitive and affective disorders
Rogers, J., Renoir, T., & Hannan, A. J. (2017) · Neuropharmacology · doi:10.1016/j.neuropharm.2017.12.038 · Cited by 69 · see citing works
Suzuki, S. S., Yamashita, T., Sakama, T., Arita, T., Yagi, N., Otsuka, T., Semba, H., Kano, H., Matsuno, S., Kato, Y., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221911 · Cited by 69 · see citing works
HLA-B Alleles and Lamotrigine-Induced Cutaneous Adverse Drug Reactions in the Han Chinese Population
Shi, Y., Min, F., Liu, X., Zan, L.-X., Gao, M., Yu, M.-J., & Liao, W. (2011) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/j.1742-7843.2011.00681.x · Cited by 68 · see citing works
Wang, C., Chen, X., Du, L., Zhan, Q., Yang, T., & Fang, Z. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105267 · Cited by 68 · see citing works
Armijo, J. A., Bravo, J., Cuadrado, A., & Herranz, J. L. (1999) · Therapeutic Drug Monitoring · doi:10.1097/00007691-199904000-00008 · Cited by 66 · see citing works
Application of Neural Networks to Population Pharmacokinetic Data Analysis
Chow, H.-H., Tolle, K. M., Roe, D. J., Elsberry, V., & Chen, H. (1997) · Journal of Pharmaceutical Sciences · doi:10.1021/js9604016 · Cited by 66 · see citing works
Liu, R., Li, X., Zhang, W., & Zhou, H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135784 · Cited by 65 · see citing works
Samsonsen, C., Reimers, A., Bråthen, G., & Brodtkorb, E. (2014) · Epilepsia · doi:10.1111/epi.12886 · Cited by 57 · see citing works
Roche-Lima, A., Roman-Santiago, A., Feliu-Maldonado, R. A., Rodríguez-Maldonado, J., Nieves-Rodríguez, B. G., Carrasquillo-Carrión, K., Ramos, C. M., Sant’Ana, I. D. L., Massey, S. E., & Duconge-Soler, J. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01550 · Cited by 56 · see citing works
Ludden, T. M. (1988) · The Journal of Clinical Pharmacology · doi:10.1002/j.1552-4604.1988.tb05714.x · Cited by 48 · see citing works
Applications of machine learning in decision analysis for dose management for dofetilide
Levy, A. E., Biswas, M., Weber, R. A., Tarakji, K. G., Chung, M. K., Noseworthy, P. A., Newton‐Cheh, C., & Rosenberg, M. A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0227324 · Cited by 40 · see citing works
Poynton, M., Choi, B., Kim, Y., Park, I. S., Noh, G., Hong, S., Boo, Y., & Kang, S. (2009) · Journal of International Medical Research · doi:10.1177/147323000903700603 · Cited by 38 · see citing works
Shen, C., Zhang, Y., Lu, R., Jin, B., Wang, S., Liu, Z., Tang, Y., & Ding, M. (2016) · Epilepsy Research · doi:10.1016/j.eplepsyres.2016.09.004 · Cited by 38 · see citing works
Impact of age, weight and concomitant treatment on lamotrigine pharmacokinetics
Brzaković, B., Kovačević, S. V., Vučićević, K. M., Miljković, B., Martinović, Ž., Pokrajac, M., & Prostran, M. S. (2012) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1111/j.1365-2710.2012.01351.x · Cited by 37 · see citing works
Therapeutic drug monitoring in a developing country: an overview
Gogtay, Kshirsagar, & Dalvi (1999) · British Journal of Clinical Pharmacology · doi:10.1046/j.1365-2125.1999.00088.x · Cited by 34 · see citing works
Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7
Johansson, Å. M., Ueckert, S., Plan, E., Hooker, A., & Karlsson, M. (2014) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-014-9359-z · Cited by 32 · see citing works
Yamamoto, Y., Inoue, Y., Matsuda, K., Takahashi, Y., & Kagawa, Y. (2012) · Biological and Pharmaceutical Bulletin · doi:10.1248/bpb.35.487 · Cited by 32 · see citing works
Therapeutic Drug Monitoring of Levetiracetam and Lamotrigine
Naik, G. S., Kodagali, R., Mathew, B., Thomas, M. M., Prabha, R., Mathew, V., & Fleming, D. H. (2014) · Therapeutic Drug Monitoring · doi:10.1097/ftd.0000000000000158 · Cited by 32 · see citing works
The impact of age on lamotrigine and oxcarbazepine kinetics: A historical cohort study
Wegner, I., Wilhelm, A. J., Sander, J. W. A. S., & Lindhout, D. (2013) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2013.07.016 · Cited by 31 · see citing works
Lie, I. A., Hoggen, I., Samsonsen, C., & Brodtkorb, E. (2015) · Epilepsy Research · doi:10.1016/j.eplepsyres.2015.03.007 · Cited by 31 · see citing works
Optimizing Levothyroxine Dose Adjustment After Thyroidectomy With a Decision Tree
Chen, S. S., Zaborek, N. A., Doubleday, A. R., Schaefer, S. C., Long, K. L., Pitt, S. C., Sippel, R. S., & Schneider, D. F. (2019) · Journal of Surgical Research · doi:10.1016/j.jss.2019.06.025 · Cited by 31 · see citing works
Domjanović, I. K., Lovrić, M., Trkulja, V., Petelin‐Gadže, Ž., Ganoci, L., Čajić, I., & Božina, N. (2018) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.13646 · Cited by 30 · see citing works
Zhou, S., AbdelWahab, A. M., Sapp, J. L., Warren, J. W., & Horáček, B. M. (2018) · Annals of Biomedical Engineering · doi:10.1007/s10439-018-02168-y · Cited by 28 · see citing works
Pharmacokinetic patterns of risperidone-associated adverse drug reactions
Schoretsanitis, G., Stegmann, B., Hiemke, C., Gründer, G., Schruers, K. R. J., Walther, S., Lammertz, S. E., Haen, E., & Paulzen, M. (2016) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-016-2085-2 · Cited by 27 · see citing works
Example-based support vector machine for drug concentration analysis
You, W., Widmer, N., & Micheli, G. D. (2011) · doi:10.1109/iembs.2011.6089917 · Cited by 23 · see citing works
Xu, S., Liu, L., Chen, Y., Liu, M., Lu, T., Wang, H., Liu, S., Zhao, M., & Zhao, L. (2018) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-018-2414-8 · Cited by 22 · see citing works
Lamotrigine as a mood stabilizer: insights from the pre-clinical evidence
de Miranda, A. S., de Miranda, A. S., & Teixeira, A. L. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1553951 · Cited by 21 · see citing works
Pharmacokinetic variability of valproate in women of childbearing age
Landmark, C. J., Burns, M. L., Baftiu, A., Farmen, A. H., Lossius, M. I., Johannessen, S. I., & Tomson, T. (2017) · Epilepsia · doi:10.1111/epi.13872 · Cited by 20 · see citing works
Launay, M., Roux, A., Beaumont, L., Douvry, B., Lécuyer, L., Douez, E., Picard, C., Grenet, D., Jullien, V., Boussaud, V., et al. (2018) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.02061-17 · Cited by 20 · see citing works
Wang, Z., Zhang, Y., Huang, W., Wang, X., Ni, X., Lu, H., Hu, J., Deng, S., Zhu, X., Xie, H.-S., et al. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00832 · Cited by 19 · see citing works
Jovanović, M., Sokić, D. V., Grabnar, I., Vovk, T., Prostran, M. S., Erić, S., Kuzmanovski, I., Vučićević, K. M., & Miljković, B. (2015) · Journal of Pharmacy & Pharmaceutical Sciences · doi:10.18433/j33031 · Cited by 18 · see citing works
Factors that influence the pharmacokinetics of lamotrigine in Japanese patients with epilepsy
Inoue, K., Yamamoto, Y., Suzuki, E., Takahashi, T., Umemura, A., Takahashi, Y., Imai, K., Inoue, Y., Hirai, K., Tsuji, D., et al. (2016) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-016-2008-2 · Cited by 16 · see citing works
The Application of Machine Learning Techniques in Clinical Drug Therapy
Meng, H., Jin, W., Yan, C., & Yang, H. (2018) · Current Computer - Aided Drug Design · doi:10.2174/1573409914666180525124608 · Cited by 16 · see citing works
Nakamura, A. M., Mihara, K., Nagai, G., Kagawa, S., Suzuki, T., Nemoto, K., & Kondo, T. (2016) · Therapeutic Drug Monitoring · doi:10.1097/ftd.0000000000000279 · Cited by 13 · see citing works
Therapeutic drug monitoring in a developing country: an overview
Gogtay, N. J., Kshirsagar, N. A., & Dalvi, S. S. (2001) · British Journal of Clinical Pharmacology · doi:10.1046/j.1365-2125.2001.0520s1103.x · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The emergence of AI-based wearable sensors for digital health technology: a review
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535 · see citing works
Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., da Silva, I. C. B., Magalhães, P., Schmidt, S., & Vale, N. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16030332 · Cited by 400 · see citing works
Poweleit, E. A., Vinks, A. A., & Mizuno, T. (2023) · Therapeutic Drug Monitoring · doi:10.1097/ftd.0000000000001078 · Cited by 119 · see citing works
Machine Learning: A New Approach for Dose Individualization
Li, Q., Tang, B., Wu, Y., Yao, B., Zhang, W., Zheng, Y., Zhou, Y., van den Anker, J. N., Hao, G., & Zhao, W. (2023) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3049 · Cited by 63 · see citing works
Chen, E., Prakash, S., Reddi, V. J., Kim, D. A., & Rajpurkar, P. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01115-0 · Cited by 51 · see citing works
Ma, P., Liu, R., Gu, W., Dai, Q., Gan, Y., Cen, J., Shang, S., Liu, F., & Chen, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.808969 · Cited by 35 · see citing works
Machine Learning for Prediction of Drug Concentrations: Application and Challenges
Huang, S., Xu, Q., Yang, G., Ding, J., & Pei, Q. (2025) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3577 · Cited by 34 · see citing works
Zhu, X., Hu, J., Xiao, T., Huang, S., Wen, Y., & Shang, D. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.975855 · Cited by 33 · see citing works
Zhu, X., Zhang, M., Wen, Y., & Shang, D. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.994665 · Cited by 24 · see citing works
Machine learning in medication prescription: A systematic review
Iancu, A., Leb, I., Prokosch, H., & Rödle, W. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105241 · Cited by 25 · see citing works
AI-Driven Chemical Design: Transforming the Sustainability of the Pharmaceutical Industry
Ruiz-Gonzalez, A. (2025) · Future Pharmacology · doi:10.3390/futurepharmacol5020024 · Cited by 23 · see citing works
Deep Learning and Site‐Specific Drug Delivery
Meenakshi, D. U., Selvasudha, N., Francis, A. P., Sweety, J. P., Fuloria, S., Fuloria, N. K., Subramaniyan, V., & Khan, S. A. (2022) · Wiley eBooks · doi:10.1002/9781119857983.ch1 · Cited by 19 · see citing works
Damnjanović, I., Tsyplakova, N., STEFANOVIĆ, N. Z., Tošić, T., Đorđević, A. C., & Karalis, V. D. (2023) · Therapeutic Advances in Drug Safety · doi:10.1177/20420986231181337 · Cited by 17 · see citing works
Zhu, X., Huang, J., Huang, S., Wen, Y., Lan, X., Wang, X., Lu, C., Wang, Z., Fan, N., & Shang, D. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.760669 · Cited by 13 · see citing works
Application of machine learning in personalized medicine
Wu, Y. F., Li, L., Xin, B., Hu, Q., Dong, X., & Li, Z. (2023) · Intelligent Pharmacy · doi:10.1016/j.ipha.2023.06.004 · Cited by 13 · see citing works
Artificial intelligence in pharmacovigilance: Do we need explainability?
Hauben, M. (2022) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5501 · Cited by 11 · see citing works
Chung, T. K., & Lee, H. (2025) · Clinical and Translational Science · doi:10.1111/cts.70353 · Cited by 10 · see citing works
How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors
Ali, M. A., Park, I. H., Kim, J., Kim, G., Oh, J., You, J. S., Kim, J., Shin, J., & Yoon, S. S. (2023) · Biomedicines · doi:10.3390/biomedicines11123134 · Cited by 11 · see citing works
Moses, A., Gui, Y., Chen, B. H., Micheal, M., & Chen, D. (2024) · Mechanics of Materials · doi:10.1016/j.mechmat.2024.105168 · Cited by 9 · see citing works
Ma, P., Shang, S., Liu, R., Dong, Y., Wu, J., Gu, W., Yu, M., Liu, J., Li, Y., & Chen, Y. (2024) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkae292 · Cited by 8 · see citing works
Zhu, X., Xiao, T., Huang, S., Liu, S., Li, X., Shang, D., & Wen, Y. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.706329 · Cited by 7 · see citing works
Al-Otaibi, J. S., Mary, Y. S., & Gamberini, M. C. (2025) · Surface Science · doi:10.1016/j.susc.2025.122789 · Cited by 5 · see citing works
Chen, Y., Lin, X., Huang, C., Wu, W., Lin, W., Chen, S., Lu, Z., You, Y., & Liu, Z. (2024) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.16112 · Cited by 5 · see citing works
Aazam, E. S., & Thomas, R. (2023) · Chemical Physics Impact · doi:10.1016/j.chphi.2023.100404 · Cited by 5 · see citing works
Mohtarami, S. A., Mostafazadeh, B., Shadnia, S., Rahimi, M., Evini, P. E. T., Ramezani, M., Borhany, H., Fathy, M., & Eskandari, H. (2024) · DARU Journal of Pharmaceutical Sciences · doi:10.1007/s40199-024-00518-x · Cited by 5 · see citing works
Yao, N., Zhao, Q., Cao, Y. J., Gu, D., & Zhang, N. (2025) · Pharmaceutical Research · doi:10.1007/s11095-025-03817-3 · Cited by 4 · see citing works
Verhaeghe, J., Corte, T. D., Waele, J. J. D., Ongenae, F., & Hoecke, S. V. (2024) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3673971.3673982 · Cited by 4 · see citing works
Jhawat, V., Gupta, S., Gulia, M., & Nair, A. B. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-98352-5.00005-7 · Cited by 4 · see citing works
Robust Nonlinear State Space Model Identification for Hemorrhage Resuscitation
Estiri, E., & Mirinejad, H. (2023) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi58575.2023.10313391 · Cited by 3 · see citing works
Kan‐Tor, Y., Ness, L., Szlak, L., Benninger, F., Ravid, S., Chorev, M., Rosen‐Zvi, M., Shimoni, Y., & Fisher, R. S. (2024) · Epilepsy Research · doi:10.1016/j.eplepsyres.2024.107313 · Cited by 3 · see citing works
Makho, G. A., Ho, S.-B., & Chai, I. (2024) · AIP conference proceedings · doi:10.1063/5.0216651 · Cited by 3 · see citing works
Pena-Gralle, A. P. B., Schnitzer, M. E., Boureguaa, S.-N., Morin, F., Legault, M., Sirois, C., Dragomir, A., & Blais, L. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103312 · Cited by 2 · see citing works
Artificial Intelligence in Drug Development
Saikia, S., Anandan, A., Annenkottil, U., Athilingam, V. P., Kalita, P. P., & Padma, V. V. (2025) · Wiley eBooks · doi:10.1002/9781394251315.ch6 · Cited by 3 · see citing works
The Role of DNA Microarrays and Machine Learning in Cancer Research
Agrawal, A., Gupta, D., Tomar, A., Bhargava, C. P., & Shrivastava, D. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-7462-7.ch007 · Cited by 1 · see citing works
Artificial intelligence in GPCR drug discovery: A paradigm shift in computational pharmacology
Vinter, A., & Grgičević, I. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00047-9 · Cited by 1 · see citing works
Abdalla, Y., Nandiraju, L. P., Yue, H., Monsales, C. B. D., Yeung, C., & Basit, A. W. (2026) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2026.115855 · Cited by 3 · see citing works
Bamgboye, A. O., Coles, L. D., Illamola, S. M., Wolfson, J. A., & Cloyd, J. C. (2026) · European Journal of Drug Metabolism and Pharmacokinetics · doi:10.1007/s13318-026-00996-1 · Cited by 1 · see citing works
Hemkiran, S., & Sadasivam, G. S. (2022) · Current Bioinformatics · doi:10.2174/1574893617666220329181607 · Cited by 1 · see citing works
Wearable Health Technology—a Perspective
Dehankar, P., & Das, S. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-76152-2_3 · Cited by 2 · see citing works
IsHak, W. W., Magakian, N., Ishak, W. W., Mikhail, A. A., & Lim, R. F. (2023) · Springer eBooks · doi:10.1007/978-3-031-15401-0_25
How artificial intelligence is revolutionizing precision medicine and drug discovery?
Yousefi, A., Zandi, A., Shojaeian, F., Marzban, M. R., Tavakol, M. R., Abiri, H., Hojabri, M., Kaviani, G., & Pournezhad, S. (2023) · Iranian Journal of Blood and Cancer · doi:10.61186/ijbc.15.3.42
Innovative Pharmacometric Approaches to Inform Drug Development and Clinical Use
(2024) · Frontiers research topics · doi:10.3389/978-2-8325-4503-4
Transformative Roles of Artificial Intelligence in Clinical Trials
Jie, C. H. E. N. (2026) · Statistics in Biosciences · doi:10.1007/s12561-026-09517-9
Wang, F., Zhang, M. Q., Ye, S., Yan, J., Li, X., Zhang, J., Yu, X. H., Wang, Y., Yu, Z., Gao, F., et al. (2026) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-026-02124-0
Alsayed, A. R., Al-Darraji, M., Al-Qaiseiah, M., Samara, A., & Al-Bayati, M. (2026) · Technologies · doi:10.3390/technologies14040241
- Authors
- Zhu, X., Huang, W., Lu, H., Wang, Z., Ni, X., Hu, J., Deng, S., Tan, Y., Li, L., Zhang, M., Qiu, C., Luo, Y., Chen, H., Huang, S., Xiao, T., Shang, D., & Wen, Y.
- Affiliations
- Guangzhou Medical University (CN)
- Guangzhou Psychiatric Hospital (CN)
- Corresponding author
- Shang, Dewei
- Wen, Yuguan
- ORCID
- Venue
- Scientific Reports
- Published
- 2021-03-10
- Volume
- 11
- Issue
- 1
- Pages
- 5568-5568
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7946912
- OpenAlex
- W3133517216
- Cited by
- 46
- References
- 59
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 10 · 2023: 13 · 2022: 6 · 2021: 2
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Guangzhou Municipal Science and Technology Project for Medicine and Healthcare (20201A011047)
- Natural Science Foundation of Guangdong Province (2018A0303130074, 2019A1515010265)
- National Natural Science Foundation of China (81701319)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Keywords
- lamotrigine, pharmacokinetic variability, therapeutic drug monitoring, extra trees regression, antiepileptic drugs, adverse drug reactions
- MeSH terms
- Machine Learning, Lamotrigine, Female, Humans, Male, Middle Aged, Models, Biological, Retrospective Studies, Drug Monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
