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Liu Y, Yu Z, Ye X, Zhang J, Hao X, Gao F, Yu J, Zhou C. Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data. Int J Clin Pharm 2024; 46:926-936. [PMID: 38733475 DOI: 10.1007/s11096-024-01729-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 03/21/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Venlafaxine dose regimens vary considerably between individuals, requiring personalized dosing. AIM This study aimed to identify dose-related influencing factors of venlafaxine through real-world data analysis and to construct a personalized dose model using advanced artificial intelligence techniques. METHOD We conducted a retrospective study on patients with depression treated with venlafaxine. Significant variables were selected through a univariate analysis. Subsequently, the predictive performance of seven models (XGBoost, LightGBM, CatBoost, GBDT, ANN, TabNet, and DT) was compared. The algorithm that demonstrated optimal performance was chosen to establish the dose prediction model. Model validation used confusion matrices and ROC analysis. Additionally, a dose subgroup analysis was conducted. RESULTS A total of 298 patients were included. TabNet was selected to establish the venlafaxine dose prediction model, which exhibited the highest performance with an accuracy of 0.80. The analysis identified seven crucial variables correlated with venlafaxine daily dose, including blood venlafaxine concentration, total protein, lymphocytes, age, globulin, cholinesterase, and blood platelet count. The area under the curve (AUC) for predicting venlafaxine doses of 75 mg, 150 mg, and 225 mg were 0.90, 0.85, and 0.90, respectively. CONCLUSION We successfully developed a TabNet model to predict venlafaxine doses using real-world data. This model demonstrated substantial predictive accuracy, offering a personalized dosing regimen for venlafaxine. These findings provide valuable guidance for the clinical use of the drug.
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Affiliation(s)
- Yimeng Liu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People's Republic of China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People's Republic of China
| | - Ze Yu
- Beijing Medicinovo Technology Co., Ltd, Beijing, 100161, People's Republic of China
| | - Xuxiao Ye
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co., Ltd, Beijing, 100161, People's Republic of China
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd., Dalian, 116021, People's Republic of China
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd, Beijing, 100161, People's Republic of China
| | - Jing Yu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People's Republic of China.
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People's Republic of China.
| | - Chunhua Zhou
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People's Republic of China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People's Republic of China
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Chang L, Hao X, Yu J, Zhang J, Liu Y, Ye X, Yu Z, Gao F, Pang X, Zhou C. Developing a machine learning model for predicting venlafaxine active moiety concentration: a retrospective study using real-world evidence. Int J Clin Pharm 2024; 46:899-909. [PMID: 38753076 DOI: 10.1007/s11096-024-01724-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/12/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Venlafaxine is frequently prescribed for patients with depression. To control the concentration of venlafaxine within the therapeutic window for the best treatment effect, a model to predict venlafaxine concentration is necessary. AIM Our objective was to develop a prediction model for venlafaxine concentration using real-world evidence based on machine learning and deep learning techniques. METHOD Patients who underwent venlafaxine treatment between November 2019 and August 2022 were included in the study. Important variables affecting venlafaxine concentration were identified using a combination of univariate analysis, sequential forward selection, and machine learning techniques. Predictive performance of nine machine learning and deep learning algorithms were assessed, and the one with the optimal performance was selected for modeling. The final model was interpreted using SHapley Additive exPlanations. RESULTS A total of 330 eligible patients were included. Five influential variables that affect venlafaxine concentration were venlafaxine daily dose, sex, age, hyperlipidemia, and adenosine deaminase. The venlafaxine concentration prediction model was developed using the eXtreme Gradient Boosting algorithm (R2 = 0.65, mean absolute error = 77.92, root mean square error = 93.58). In the testing cohort, the accuracy of the predicted concentration within ± 30% of the actual concentration was 73.49%. In the subgroup analysis, the prediction accuracy was 69.39% within the recommended therapeutic range of venlafaxine concentration within ± 30% of the actual value. CONCLUSION The XGBoost model for predicting blood concentration of venlafaxine using real-world evidence was developed, guiding the adjustment of regimen in clinical practice.
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Affiliation(s)
- Luyao Chang
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, 89 Donggang Road, Yuhua District, Shijiazhuang, 066003, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd., Dalian, China
| | - Jing Yu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, 89 Donggang Road, Yuhua District, Shijiazhuang, 066003, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co., Ltd., Beijing, China
| | - Yimeng Liu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, 89 Donggang Road, Yuhua District, Shijiazhuang, 066003, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xuxiao Ye
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ze Yu
- Beijing Medicinovo Technology Co., Ltd., Beijing, China
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd., Beijing, China
| | - Xiaolu Pang
- Hebei Medical University, 361 Zhongshan East Road, Chang'an District, Shijiazhuang, 050011, China
| | - Chunhua Zhou
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, 89 Donggang Road, Yuhua District, Shijiazhuang, 066003, China.
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China.
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Yu Z, Ye X, Liu H, Li H, Hao X, Zhang J, Kou F, Wang Z, Wei H, Gao F, Zhai Q. Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study. Front Oncol 2022; 12:893966. [PMID: 35719963 PMCID: PMC9203846 DOI: 10.3389/fonc.2022.893966] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/05/2022] [Indexed: 11/26/2022] Open
Abstract
Lapatinib is used for the treatment of metastatic HER2(+) breast cancer. We aim to establish a prediction model for lapatinib dose using machine learning and deep learning techniques based on a real-world study. There were 149 breast cancer patients enrolled from July 2016 to June 2017 at Fudan University Shanghai Cancer Center. The sequential forward selection algorithm based on random forest was applied for variable selection. Twelve machine learning and deep learning algorithms were compared in terms of their predictive abilities (logistic regression, SVM, random forest, Adaboost, XGBoost, GBDT, LightGBM, CatBoost, TabNet, ANN, Super TML, and Wide&Deep). As a result, TabNet was chosen to construct the prediction model with the best performance (accuracy = 0.82 and AUC = 0.83). Afterward, four variables that strongly correlated with lapatinib dose were ranked via importance score as follows: treatment protocols, weight, number of chemotherapy treatments, and number of metastases. Finally, the confusion matrix was used to validate the model for a dose regimen of 1,250 mg lapatinib (precision = 81% and recall = 95%), and for a dose regimen of 1,000 mg lapatinib (precision = 87% and recall = 64%). To conclude, we established a deep learning model to predict lapatinib dose based on important influencing variables selected from real-world evidence, to achieve an optimal individualized dose regimen with good predictive performance.
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Affiliation(s)
- Ze Yu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xuan Ye
- Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Hongyue Liu
- Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Huan Li
- Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd., Dalian, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co., Ltd., Beijing, China
| | - Fang Kou
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zeyuan Wang
- Faculty of Engineering, School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Hai Wei
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd., Beijing, China
| | - Qing Zhai
- Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
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Gómez-Ospina JC, García-Perdomo HA. Re: Systematic review and meta-analysis of narrow band imaging for non-muscle-invasive bladder cancer. Int J Urol 2021; 29:366. [PMID: 34873753 DOI: 10.1111/iju.14764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Herney Andrés García-Perdomo
- UROGIV Research Group, School of Medicine, Universidad del Valle, Cali, Colombia.,Division of Urology/Urooncology, Department of Surgery, Universidad del Valle, Cali, Colombia
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