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Xiang J, Qi B, Cerou M, Zhao W, Tang Q. DN-ODE: Data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivals. Comput Biol Med 2024; 180:108876. [PMID: 39089112 DOI: 10.1016/j.compbiomed.2024.108876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/25/2024] [Accepted: 07/09/2024] [Indexed: 08/03/2024]
Abstract
Pharmacokinetic/Pharmacodynamic (PK/PD) modeling is crucial in the development of new drugs. However, traditional population-based PK/PD models encounter challenges when modeling for individual patients. We aim to explore the potential of constructing a pharmacodynamic model for individual breast cancer pharmacodynamics leveraging only limited data from early clinical trial phases. While previous studies on Neural Ordinary Differential Equations (ODEs) suggest promising results in clinical trial practices, they primarily focused on theoretical applications or independent PK/PD modeling. PD modeling from complex and irregular clinical trial data, especially when interacting with PK parameters, is still unclear. To achieve that, we introduce a Data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for breast cancer tumor dynamics and progression-free survival data. To validate this approach, experiments are conducted with early-phase clinical trial data from the Amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2), aiming to predict pharmacodynamics in the later phase (AMEERA 3). DN-ODE model achieves RMSE scores of 8.78 and 0.21 in tumor size and progression-free survival, respectively, with R2 scores over 0.9 for each task. Compared to PK/PD methodologies, DN-ODE is able to predict robust individual tumor dynamics with only limited cycle data. We also introduce Principal Component Analysis visualizations for encoder results, demonstrating the DN-ODE's capability to discern individual distributions and diverse tumor growth patterns. Therefore, DN-ODE facilitates comprehensive drug efficacy assessments, pinpoints potential responders, and aids in trial design.
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Affiliation(s)
- Jinlin Xiang
- Data and Data Science, Sanofi, 450 Water St, Cambridge, 02141, MA, USA
| | - Bozhao Qi
- Data and Data Science, Sanofi, 55 Corporate Dr, Bridgewater, 08807, NJ, USA
| | - Marc Cerou
- Translational Disease Modelling Oncology, Data and Data Science, Sanofi R&D, 55 Corporate Dr, 91380, Chilly-Mazarin, France
| | - Wei Zhao
- Data and Data Science, Sanofi, 450 Water St, Cambridge, 02141, MA, USA
| | - Qi Tang
- Data and Data Science, Sanofi, 55 Corporate Dr, Bridgewater, 08807, NJ, USA.
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Aden D, Zaheer S, Khan S. Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology. REVISTA ESPANOLA DE PATOLOGIA : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ANATOMIA PATOLOGICA Y DE LA SOCIEDAD ESPANOLA DE CITOLOGIA 2024; 57:198-210. [PMID: 38971620 DOI: 10.1016/j.patol.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/22/2024] [Accepted: 04/16/2024] [Indexed: 07/08/2024]
Abstract
The much-hyped artificial intelligence (AI) model called ChatGPT developed by Open AI can have great benefits for physicians, especially pathologists, by saving time so that they can use their time for more significant work. Generative AI is a special class of AI model, which uses patterns and structures learned from existing data and can create new data. Utilizing ChatGPT in Pathology offers a multitude of benefits, encompassing the summarization of patient records and its promising prospects in Digital Pathology, as well as its valuable contributions to education and research in this field. However, certain roadblocks need to be dealt like integrating ChatGPT with image analysis which will act as a revolution in the field of pathology by increasing diagnostic accuracy and precision. The challenges with the use of ChatGPT encompass biases from its training data, the need for ample input data, potential risks related to bias and transparency, and the potential adverse outcomes arising from inaccurate content generation. Generation of meaningful insights from the textual information which will be efficient in processing different types of image data, such as medical images, and pathology slides. Due consideration should be given to ethical and legal issues including bias.
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Affiliation(s)
- Durre Aden
- Department of Pathology, Hamdard Institute of Medical Sciences and Research, Jamia Hamdard, New Delhi, India
| | - Sufian Zaheer
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
| | - Sabina Khan
- Department of Pathology, Hamdard Institute of Medical Sciences and Research, Jamia Hamdard, New Delhi, India
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Mohtarami SA, Mostafazadeh B, Shadnia S, Rahimi M, Evini PET, Ramezani M, Borhany H, Fathy M, Eskandari H. Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence). Daru 2024:10.1007/s40199-024-00518-x. [PMID: 38771458 DOI: 10.1007/s40199-024-00518-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 04/30/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and duration of hospital stay in opioid poisoning, in order to facilitate appropriate clinical decision-making. METHOD AND RESULTS This study used artificial intelligence technology to predict the maintenance dose and duration of administration by selecting clinical and paraclinical features that were selected by Pearson correlation (filter method) (Stage 1) and then the (wrapper method) Recursive Feature Elimination Cross-Validated (RFECV) (Stage2). The duration of administration was divided into two categories: A (which includes a duration of less than or equal to 24 h of infusion) and B (more than 24 h of naloxone infusion). XGBoost algorithm model with an accuracy rate of 91.04%, a prediction rate of 91.34%, and a sensitivity rate of 91.04% and area under the Curve (AUC) 0.97 was best model for classification patients. Also, the best maintenance dose of naloxone was obtained with XGBoost algorithm with R2 = 0.678. Based on the selected algorithm, the most important features for classifying patients for the duration of treatment were bicarbonate, respiration rate, physical sign, The partial pressure of carbon dioxide (PCO2), diastolic blood pressure, pulse rate, naloxone bolus dose, Blood Creatinine(Cr), Body temperature (T). The most important characteristics for determining the maintenance dose of naloxone were physical signs, bolus dose of 4.5 mg/kg, Glasgow Coma Scale (GCS), Creatine Phosphokinase (CPK) and intensive care unit (ICU) add. CONCLUSION A predictive model can significantly enhance the decision-making and clinical care provided by emergency physicians in hospitals and medical settings. XGBoost was found to be the superior model.
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Affiliation(s)
| | - Babak Mostafazadeh
- Clinical Research Development Unit (CRDU) of Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Toxicological Research Center, Department of Clinical Toxicology, Excellence Center of Clinical Toxicology, Loghman Hakim University Hospital Poison Center, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran.
| | - Shahin Shadnia
- Clinical Research Development Unit (CRDU) of Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Toxicological Research Center, Department of Clinical Toxicology, Excellence Center of Clinical Toxicology, Loghman Hakim University Hospital Poison Center, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Mitra Rahimi
- Clinical Research Development Unit (CRDU) of Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Toxicological Research Center, Department of Clinical Toxicology, Excellence Center of Clinical Toxicology, Loghman Hakim University Hospital Poison Center, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Peyman Erfan Talab Evini
- Clinical Research Development Unit (CRDU) of Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Toxicological Research Center, Department of Clinical Toxicology, Excellence Center of Clinical Toxicology, Loghman Hakim University Hospital Poison Center, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Maral Ramezani
- Department of Pharmacology, School of Medicine, Arak University of Medical Sciences, Arak, Iran
- Traditional and Complementary Medicine Research Center, Arak University of Medical Sciences, Arak, Iran
| | - Hamed Borhany
- Toxicological Research Center, Department of Clinical Toxicology, Excellence Center of Clinical Toxicology, Loghman Hakim University Hospital Poison Center, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Mobin Fathy
- Toxicological Research Center, Department of Clinical Toxicology, Excellence Center of Clinical Toxicology, Loghman Hakim University Hospital Poison Center, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Hamidreza Eskandari
- Toxicological Research Center, Department of Clinical Toxicology, Excellence Center of Clinical Toxicology, Loghman Hakim University Hospital Poison Center, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
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Cheng Y, Hu H, Dong X, Hao X, Li Y. Exploring Transformer Model in Longitudinal Pharmacokinetic/Pharmacodynamic Analyses and Comparing with Alternative Natural Language Processing Models. J Pharm Sci 2024; 113:1368-1375. [PMID: 38350557 DOI: 10.1016/j.xphs.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/15/2024]
Abstract
There remains a substantial need for a comprehensive assessment of various natural language processing (NLP) algorithms in longitudinal pharmacokinetic/pharmacodynamic (PK/PD) modeling despite recent advances in machine learning in the space of quantitative pharmacology. We herein investigated the application of the transformer model and further compared the performance among several different NLP models, including long short-term memory (LSTM) and neural-ODE (Ordinary Differential Equation) in analyzing longitudinal PK/PD data using virtual data containing three different regimens. Results suggested that LSTM and neural-ODE, along with their respective variants provide a strong performance when predicting from training-included (seen) regimens, albeit with slight information loss for training-excluded (unseen) regimens. Similarly, as with neural-ODE, the transformer exhibited superior performance in describing time-series PK/PD data. Nonetheless, when extrapolating to unseen regimens, while outlining the general data trends, it encountered difficulties in precisely capturing data fluctuations. Remarkably, a small integration of unseen data into the training dataset significantly bolsters predictive performance for both seen and unseen regimens. Our study marks a pioneering effort in deploying the transformer model for time-series PK/PD analysis and provides a systematic exploration of the currently available NLP models in this field.
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Affiliation(s)
- Yiming Cheng
- Clinical Pharmacology, Pharmacometrics, Disposition & Bioanalysis, Bristol Myers Squibb, 556 Morris Avenue, Summit, NJ 07901, United States
| | - Hongxiang Hu
- Clinical Pharmacology, Pharmacometrics, Disposition & Bioanalysis, Bristol Myers Squibb, 556 Morris Avenue, Summit, NJ 07901, United States
| | - Xin Dong
- Clinical Pharmacology, Pharmacometrics, Disposition & Bioanalysis, Bristol Myers Squibb, 556 Morris Avenue, Summit, NJ 07901, United States
| | - Xiaoran Hao
- Clinical Pharmacology, Pharmacometrics, Disposition & Bioanalysis, Bristol Myers Squibb, 556 Morris Avenue, Summit, NJ 07901, United States
| | - Yan Li
- Clinical Pharmacology, Pharmacometrics, Disposition & Bioanalysis, Bristol Myers Squibb, 556 Morris Avenue, Summit, NJ 07901, United States.
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Li QY, Tang BH, Wu YE, Yao BF, Zhang W, Zheng Y, Zhou Y, van den Anker J, Hao GX, Zhao W. Machine Learning: A New Approach for Dose Individualization. Clin Pharmacol Ther 2024; 115:727-744. [PMID: 37713106 DOI: 10.1002/cpt.3049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/03/2023] [Indexed: 09/16/2023]
Abstract
The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti-infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.
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Affiliation(s)
- Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bo-Hao Tang
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhang
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue Zhou
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Departments of Pediatrics, Pharmacology & Physiology, Genomics & Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
- NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Shandong University, Jinan, China
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Hao Y, Zhang J, Yu J, Yu Z, Yang L, Hao X, Gao F, Zhou C. Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence. Ann Gen Psychiatry 2024; 23:5. [PMID: 38184628 PMCID: PMC10771703 DOI: 10.1186/s12991-023-00483-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 11/25/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND Being one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue to experience troublesome symptoms. Quetiapine, as one of the most commonly prescribed antipsychotic medication worldwide, has been reported as an effective augmentation strategy to antidepressants. The right quetiapine dose and personalized quetiapine treatment are frequently challenging for clinicians. This study aimed to identify important influencing variables for quetiapine dose by maximizing the use of data from real world, and develop a predictive model of quetiapine dose through machine learning techniques to support selections for treatment regimens. METHODS The study comprised 308 depressed patients who were medicated with quetiapine and hospitalized in the First Hospital of Hebei Medical University, from November 1, 2019, to August 31, 2022. To identify the important variables influencing the dose of quetiapine, a univariate analysis was applied. The prediction abilities of nine machine learning models (XGBoost, LightGBM, RF, GBDT, SVM, LR, ANN, DT) were compared. Algorithm with the optimal model performance was chosen to develop the prediction model. RESULTS Four predictors were selected from 38 variables by the univariate analysis (p < 0.05), including quetiapine TDM value, age, mean corpuscular hemoglobin concentration, and total bile acid. Ultimately, the XGBoost algorithm was used to create a prediction model for quetiapine dose that had the greatest predictive performance (accuracy = 0.69) out of nine models. In the testing cohort (62 cases), a total of 43 cases were correctly predicted of the quetiapine dose regimen. In dose subgroup analysis, AUROC for patients with daily dose of 100 mg, 200 mg, 300 mg and 400 mg were 0.99, 0.75, 0.93 and 0.86, respectively. CONCLUSIONS In this work, machine learning techniques are used for the first time to estimate the dose of quetiapine for patients with depression, which is valuable for the clinical drug recommendations.
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Affiliation(s)
- Yupei Hao
- Department of Clinical Pharmacy, the First Hospital of Hebei Medical University, Shijiazhuang, 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
| | - Jing Yu
- Department of Clinical Pharmacy, the First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ze Yu
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Lin Yang
- Department of Clinical Pharmacy, the First Hospital of Hebei Medical University, Shijiazhuang, 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
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd, Beijing, China.
| | - Chunhua Zhou
- Department of Clinical Pharmacy, the First Hospital of Hebei Medical University, Shijiazhuang, 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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Wingfield LR, Salaun A, Khan A, Webb H, Zhu T, Knight S. Clinical Decision Support Systems Used in Transplantation: Are They Tools for Success or an Unnecessary Gadget? A Systematic Review. Transplantation 2024; 108:72-99. [PMID: 37143191 DOI: 10.1097/tp.0000000000004627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Although clinical decision support systems (CDSSs) have been used since the 1970s for a wide variety of clinical tasks including optimization of medication orders, improved documentation, and improved patient adherence, to date, no systematic reviews have been carried out to assess their utilization and efficacy in transplant medicine. The aim of this study is to systematically review studies that utilized a CDSS and assess impact on patient outcomes. A total of 48 articles were identified as meeting the author-derived inclusion criteria, including tools for posttransplant monitoring, pretransplant risk assessment, waiting list management, immunosuppressant management, and interpretation of histopathology. Studies included 15 984 transplant recipients. Tools aimed at helping with transplant patient immunosuppressant management were the most common (19 studies). Thirty-four studies (85%) found an overall clinical benefit following the implementation of a CDSS in clinical practice. Although there are limitations to the existing literature, current evidence suggests that implementing CDSS in transplant clinical settings may improve outcomes for patients. Limited evidence was found using more advanced technologies such as artificial intelligence in transplantation, and future studies should investigate the role of these emerging technologies.
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Affiliation(s)
- Laura R Wingfield
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Achille Salaun
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Aparajita Khan
- Department of Neurosurgery, Stanford University, Stanford, CA
| | - Helena Webb
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Simon Knight
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
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Peloso A, Naesens M, Thaunat O. The Dawn of a New Era in Kidney Transplantation: Promises and Limitations of Artificial Intelligence for Precision Diagnostics. Transpl Int 2023; 36:12010. [PMID: 38234305 PMCID: PMC10793260 DOI: 10.3389/ti.2023.12010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/05/2023] [Indexed: 01/19/2024]
Affiliation(s)
- Andrea Peloso
- Division of Transplantation, Department of Surgery, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- Division of Abdominal Surgery, Department of Surgery, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Olivier Thaunat
- International Center of Infectiology Research (CIRI), French Institute of Health and Medical Research (INSERM) Unit 1111, Claude Bernard University Lyon I, National Center for Scientific Research (CNRS) Mixed University Unit (UMR) 5308, Ecole Normale Supérieure de Lyon, University of Lyon, Lyon, France
- Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon, Edouard Herriot Hospital, Lyon, France
- Lyon-Est Medical Faculty, Claude Bernard University (Lyon 1), Lyon, France
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Iancu A, Leb I, Prokosch HU, Rödle W. Machine learning in medication prescription: A systematic review. Int J Med Inform 2023; 180:105241. [PMID: 37939541 DOI: 10.1016/j.ijmedinf.2023.105241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/17/2023] [Accepted: 09/27/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications "off-label" as children are still underrepresented in clinical studies, which leads to a high risk of an incorrect dose and adverse drug effects. METHODS PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription. RESULTS The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance. CONCLUSIONS By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.
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Affiliation(s)
- Alexa Iancu
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Ines Leb
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Wolfgang Rödle
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany.
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Rahman MA, Yilmaz I, Albadri ST, Salem FE, Dangott BJ, Taner CB, Nassar A, Akkus Z. Artificial Intelligence Advances in Transplant Pathology. Bioengineering (Basel) 2023; 10:1041. [PMID: 37760142 PMCID: PMC10525684 DOI: 10.3390/bioengineering10091041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/15/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.
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Affiliation(s)
- Md Arafatur Rahman
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA
| | - Ibrahim Yilmaz
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Sam T. Albadri
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Fadi E. Salem
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Bryan J. Dangott
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| | - C. Burcin Taner
- Department of Transplantation Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Aziza Nassar
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Zeynettin Akkus
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
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Basuli D, Roy S. Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation. J Clin Med Res 2023; 15:391-398. [PMID: 37822851 PMCID: PMC10563819 DOI: 10.14740/jocmr5012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/23/2023] [Indexed: 10/13/2023] Open
Abstract
The field of kidney transplantation is being revolutionized by the integration of artificial intelligence (AI) and machine learning (ML) techniques. AI equips machines with human-like cognitive abilities, while ML enables computers to learn from data. Challenges in transplantation, such as organ allocation and prediction of allograft function or rejection, can be addressed through AI-powered algorithms. These algorithms can optimize immunosuppression protocols and improve patient care. This comprehensive literature review provides an overview of all the recent studies on the utilization of AI and ML techniques in the optimization of immunosuppression in kidney transplantation. By developing personalized and data-driven immunosuppression protocols, clinicians can make informed decisions and enhance patient care. However, there are limitations, such as data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Future research should validate and refine AI models for different populations and treatment durations. AI and ML have the potential to revolutionize kidney transplantation by optimizing immunosuppression and improving outcomes. AI-powered algorithms enable personalized and data-driven immunosuppression protocols, enhancing patient care and decision-making. Limitations include data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Further research is needed to validate and enhance AI models for different populations and longer-term dosing decisions.
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Affiliation(s)
- Debargha Basuli
- Department of Nephrology and Hypertension, Brody School of Medicine/East Carolina University, Greenville, NC, USA
| | - Sasmit Roy
- Department of Internal Medicine, Centra Lynchburg General Hospital, Lynchburg, VA, USA
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12
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Henkel L, Jehn U, Thölking G, Reuter S. Tacrolimus-why pharmacokinetics matter in the clinic. FRONTIERS IN TRANSPLANTATION 2023; 2:1160752. [PMID: 38993881 PMCID: PMC11235362 DOI: 10.3389/frtra.2023.1160752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/07/2023] [Indexed: 07/13/2024]
Abstract
The calcineurin inhibitor (CNI) Tacrolimus (Tac) is the most prescribed immunosuppressant drug after solid organ transplantation. After renal transplantation (RTx) approximately 95% of recipients are discharged with a Tac-based immunosuppressive regime. Despite the high immunosuppressive efficacy, its adverse effects, narrow therapeutic window and high intra- and interpatient variability (IPV) in pharmacokinetics require therapeutic drug monitoring (TDM), which makes treatment with Tac a major challenge for physicians. The C/D ratio (full blood trough level normalized by daily dose) is able to classify patients receiving Tac into two major metabolism groups, which were significantly associated with the clinical outcomes of patients after renal or liver transplantation. Therefore, the C/D ratio is a simple but effective tool to identify patients at risk of an unfavorable outcome. This review highlights the challenges of Tac-based immunosuppressive therapy faced by transplant physicians in their daily routine, the underlying causes and pharmacokinetics (including genetics, interactions, and differences between available Tac formulations), and the latest data on potential solutions to optimize treatment of high-risk patients.
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Affiliation(s)
- Lino Henkel
- Department of Medicine D, University of Münster, Münster, Germany
| | - Ulrich Jehn
- Department of Medicine D, University of Münster, Münster, Germany
| | - Gerold Thölking
- Department of Medicine D, University of Münster, Münster, Germany
- Department of Internal Medicine and Nephrology, University Hospital of Münster Marienhospital Steinfurt, Steinfurt, Germany
| | - Stefan Reuter
- Department of Medicine D, University of Münster, Münster, Germany
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13
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Schagen MR, Volarevic H, Francke MI, Sassen SDT, Reinders MEJ, Hesselink DA, de Winter BCM. Individualized dosing algorithms for tacrolimus in kidney transplant recipients: current status and unmet needs. Expert Opin Drug Metab Toxicol 2023; 19:429-445. [PMID: 37642358 DOI: 10.1080/17425255.2023.2250251] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/17/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Tacrolimus is a potent immunosuppressive drug with many side effects including nephrotoxicity and post-transplant diabetes mellitus. To limit its toxicity, therapeutic drug monitoring (TDM) is performed. However, tacrolimus' pharmacokinetics are highly variable within and between individuals, which complicates their clinical management. Despite TDM, many kidney transplant recipients will experience under- or overexposure to tacrolimus. Therefore, dosing algorithms have been developed to limit the time a patient is exposed to off-target concentrations. AREAS COVERED Tacrolimus starting dose algorithms and models for follow-up doses developed and/or tested since 2015, encompassing both adult and pediatric populations. Literature was searched in different databases, i.e. Embase, PubMed, Web of Science, Cochrane Register, and Google Scholar, from inception to February 2023. EXPERT OPINION Many algorithms have been developed, but few have been prospectively evaluated. These performed better than bodyweight-based starting doses, regarding the time a patient is exposed to off-target tacrolimus concentrations. No benefit in reduced tacrolimus toxicity has yet been observed. Most algorithms were developed from small datasets, contained only a few tacrolimus concentrations per person, and were not externally validated. Moreover, other matrices should be considered which might better correlate with tacrolimus toxicity than the whole-blood concentration, e.g. unbound plasma or intra-lymphocytic tacrolimus concentrations.
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Affiliation(s)
- Maaike R Schagen
- Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Erasmus MC, Rotterdam Clinical Pharmacometrics Group, Rotterdam, the Netherlands
| | - Helena Volarevic
- Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marith I Francke
- Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sebastiaan D T Sassen
- Erasmus MC, Rotterdam Clinical Pharmacometrics Group, Rotterdam, the Netherlands
- Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marlies E J Reinders
- Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Dennis A Hesselink
- Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Brenda C M de Winter
- Erasmus MC, Rotterdam Clinical Pharmacometrics Group, Rotterdam, the Netherlands
- Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Mao J, Chao K, Jiang FL, Ye XP, Yang T, Li P, Zhu X, Hu PJ, Zhou BJ, Huang M, Gao X, Wang XD. Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population. World J Gastroenterol 2023; 29:3855-3870. [PMID: 37426324 PMCID: PMC10324537 DOI: 10.3748/wjg.v29.i24.3855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/07/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Thalidomide is an effective treatment for refractory Crohn’s disease (CD). However, thalidomide-induced peripheral neuropathy (TiPN), which has a large individual variation, is a major cause of treatment failure. TiPN is rarely predictable and recognized, especially in CD. It is necessary to develop a risk model to predict TiPN occurrence.
AIM To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic variables.
METHODS A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the model. The National Cancer Institute Common Toxicity Criteria Sensory Scale (version 4.0) was used to assess TiPN. With 18 clinical features and 150 genetic variables, five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), specificity, sensitivity (recall rate), precision, accuracy, and F1 score.
RESULTS The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248 [P = 0.0004, odds ratio (OR): 8.983, 95% confidence interval (CI): 2.497-30.90], dose (mg/d, P = 0.002), brain-derived neurotrophic factor (BDNF) rs2030324 (P = 0.001, OR: 3.164, 95%CI: 1.561-6.434), BDNF rs6265 (P = 0.001, OR: 3.150, 95%CI: 1.546-6.073) and BDNF rs11030104 (P = 0.001, OR: 3.091, 95%CI: 1.525-5.960). In the training set, gradient boosting decision tree (GBDT), extremely random trees (ET), random forest, logistic regression and extreme gradient boosting (XGBoost) obtained AUROC values > 0.90 and AUPRC > 0.87. Among these models, XGBoost and GBDT obtained the first two highest AUROC (0.90 and 1), AUPRC (0.98 and 1), accuracy (0.96 and 0.98), precision (0.90 and 0.95), F1 score (0.95 and 0.98), specificity (0.94 and 0.97), and sensitivity (1). In the validation set, XGBoost algorithm exhibited the best predictive performance with the highest specificity (0.857), accuracy (0.818), AUPRC (0.86) and AUROC (0.89). ET and GBDT obtained the highest sensitivity (1) and F1 score (0.8). Overall, compared with other state-of-the-art classifiers such as ET, GBDT and RF, XGBoost algorithm not only showed a more stable performance, but also yielded higher ROC-AUC and PRC-AUC scores, demonstrating its high accuracy in prediction of TiPN occurrence.
CONCLUSION The powerful XGBoost algorithm accurately predicts TiPN using 18 clinical features and 14 genetic variables. With the ability to identify high-risk patients using single nucleotide polymorphisms, it offers a feasible option for improving thalidomide efficacy in CD patients.
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Affiliation(s)
- Jing Mao
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
- Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Kang Chao
- Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Fu-Lin Jiang
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Xiao-Ping Ye
- Department of Pharmacy, Guangdong Women and Children Hospital, Guangzhou 510000, Guangdong Province, China
| | - Ting Yang
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
- Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Pan Li
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
- Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Xia Zhu
- Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Pin-Jin Hu
- Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Bai-Jun Zhou
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Min Huang
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
- Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Xiang Gao
- Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
| | - Xue-Ding Wang
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
- Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, Sun Yat-sen University, Guangzhou 510006, Guangdong Province, China
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Quinino RM, Agena F, Modelli de Andrade LG, Furtado M, Chiavegatto Filho ADP, David-Neto E. A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation. Transplantation 2023; 107:1380-1389. [PMID: 36872507 DOI: 10.1097/tp.0000000000004510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
BACKGROUND After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms. METHODS Unsensitized recipients who received their first KTx deceased donor between January 1, 2010, and December 31, 2019, were classified according to the conduct of renal function after transplantation. Variables related to the donor, recipient, kidney preservation, and immunology were used. The patients were randomly divided into 2 groups: 70% were assigned to the training and 30% to the test group. Popular machine learning algorithms were used: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Gradient Boosting classifier, Logistic Regression, CatBoost classifier, AdaBoost classifier, and Random Forest classifier. Comparative performance analysis on the test dataset was performed using the results of the AUC values, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. RESULTS Of the 859 patients, 21.7% (n = 186) had IGF. The best predictive performance resulted from the eXtreme Gradient Boosting model (AUC, 0.78; 95% CI, 0.71-0.84; sensitivity, 0.64; specificity, 0.78). Five variables with the highest predictive value were identified. CONCLUSIONS Our results indicated the possibility of creating a model for the prediction of IGF, enhancing the selection of patients who would benefit from an expensive treatment, as in the case of machine perfusion preservation.
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Affiliation(s)
- Raquel M Quinino
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Fabiana Agena
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | | | - Mariane Furtado
- Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil
| | | | - Elias David-Neto
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
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Sauthier N, Bouchakri R, Carrier FM, Sauthier M, Mullie LA, Cardinal H, Fortin MC, Lahrichi N, Chassé M. Automated screening of potential organ donors using a temporal machine learning model. Sci Rep 2023; 13:8459. [PMID: 37231073 DOI: 10.1038/s41598-023-35270-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/15/2023] [Indexed: 05/27/2023] Open
Abstract
Organ donation is not meeting demand, and yet 30-60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors. Using routine clinical data and laboratory time-series, we retrospectively developed and tested a neural network model to automatically identify potential organ donors. We first trained a convolutive autoencoder that learned from the longitudinal changes of over 100 types of laboratory results. We then added a deep neural network classifier. This model was compared to a simpler logistic regression model. We observed an AUROC of 0.966 (CI 0.949-0.981) for the neural network and 0.940 (0.908-0.969) for the logistic regression model. At a prespecified cutoff, sensitivity and specificity were similar between both models at 84% and 93%. Accuracy of the neural network model was robust across donor subgroups and remained stable in a prospective simulation, while the logistic regression model performance declined when applied to rarer subgroups and in the prospective simulation. Our findings support using machine learning models to help with the identification of potential organ donors using routinely collected clinical and laboratory data.
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Affiliation(s)
- Nicolas Sauthier
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Rima Bouchakri
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | | | - Michaël Sauthier
- Centre Hospitalier Universitaire Sainte-Justine, Montreal, Canada
| | | | - Héloïse Cardinal
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | | | | | - Michaël Chassé
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada.
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17
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Khusial R, Bies RR, Akil A. Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine. Pharmaceutics 2023; 15:pharmaceutics15041139. [PMID: 37111625 PMCID: PMC10145228 DOI: 10.3390/pharmaceutics15041139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/17/2023] [Accepted: 03/31/2023] [Indexed: 04/07/2023] Open
Abstract
Pharmacometrics and the utilization of population pharmacokinetics play an integral role in model-informed drug discovery and development (MIDD). Recently, there has been a growth in the application of deep learning approaches to aid in areas within MIDD. In this study, a deep learning model, LSTM-ANN, was developed to predict olanzapine drug concentrations from the CATIE study. A total of 1527 olanzapine drug concentrations from 523 individuals along with 11 patient-specific covariates were used in model development. The hyperparameters of the LSTM-ANN model were optimized through a Bayesian optimization algorithm. A population pharmacokinetic model using the NONMEM model was constructed as a reference to compare to the performance of the LSTM-ANN model. The RMSE of the LSTM-ANN model was 29.566 in the validation set, while the RMSE of the NONMEM model was 31.129. Permutation importance revealed that age, sex, and smoking were highly influential covariates in the LSTM-ANN model. The LSTM-ANN model showed potential in the application of drug concentration predictions as it was able to capture the relationships within a sparsely sampled pharmacokinetic dataset and perform comparably to the NONMEM model.
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Affiliation(s)
- Richard Khusial
- Department of Pharmaceutical Sciences, College of Pharmacy, Mercer University, Atlanta, GA 30341, USA
| | - Robert R. Bies
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, NY 14214, USA
- Institute for Artificial Intelligence and Data Science, University at Buffalo, Buffalo, NY 14260, USA
| | - Ayman Akil
- Department of Pharmaceutical Sciences, College of Pharmacy, Mercer University, Atlanta, GA 30341, USA
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18
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Nwanosike EM, Sunter W, Ansari MA, Merchant HA, Conway B, Hasan SS. A Real-World Exploration into Clinical Outcomes of Direct Oral Anticoagulant Dosing Regimens in Morbidly Obese Patients Using Data-Driven Approaches. Am J Cardiovasc Drugs 2023; 23:287-299. [PMID: 36872389 DOI: 10.1007/s40256-023-00569-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/27/2022] [Indexed: 03/07/2023]
Abstract
INTRODUCTION The clinical outcomes of direct oral anticoagulant (DOAC) dosage regimens in morbid obesity are uncertain due to limited clinical evidence. This study seeks to bridge this evidence gap by identifying the factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients. METHOD A data-driven observational study was carried out using supervised machine learning (ML) models with a dataset extracted from electronic health records and preprocessed. Following 70%:30% partitioning of the overall dataset via stratified sampling, the selected ML classifiers (e.g., random forest, decision trees, bootstrap aggregation) were applied to the training dataset (70%). The outcomes of the models were evaluated against the test dataset (30%). Multivariate regression analysis explored the association between DOAC regimens and clinical outcomes. RESULTS A sample of 4,275 morbidly obese patients was extracted and analysed. The decision trees, random forest, and bootstrap aggregation classifiers achieved acceptable (excellent) values of precision, recall, and F1 scores in terms of their contribution to clinical outcomes. The length of stay, treatment days, and age were ranked highest for relevance to mortality and stroke. Among DOAC regimens, apixaban 2.5 mg twice daily ranked highest for its association with mortality, increasing the mortality risk by 43% (odds ratio [OR] 1.430, 95% confidence interval [CI] 1.181-1.732, p = 0.001). On the other hand, apixaban 5 mg twice daily reduced the odds of mortality by 25% (OR 0.751, 95% CI 0.632-0.905, p = 0.003) but increased the odds of stroke events. No clinically relevant non-major bleeding events occurred in this group. CONCLUSION Data-driven approaches can identify key factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients. This will help design further studies to explore well tolerated and effective DOAC doses for morbidly obese patients.
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Affiliation(s)
- Ezekwesiri Michael Nwanosike
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK
| | - Wendy Sunter
- Anticoagulant Services, Calderdale and Huddersfield NHS Foundation Trust Hospital, Lindley, HD3 3EA, Huddersfield, UK
| | - Muhammad Ayub Ansari
- School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, West Yorkshire, UK
| | - Hamid A Merchant
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK
| | - Barbara Conway
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK
| | - Syed Shahzad Hasan
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK.
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Song L, Huang CR, Pan SZ, Zhu JG, Cheng ZQ, Yu X, Xue L, Xia F, Zhang JY, Wu DP, Miao LY. A model based on machine learning for the prediction of cyclosporin A trough concentration in Chinese allo-HSCT patients. Expert Rev Clin Pharmacol 2023; 16:83-91. [PMID: 36373407 DOI: 10.1080/17512433.2023.2142561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Cyclosporin A is a calcineurin inhibitor which has a narrow therapeutic window and high interindividual variability. Various population pharmacokinetic models have been reported; however, professional software and technical personnel were needed and the variables of the models were limited. Therefore, the aim of this study was to establish a model based on machine learning to predict CsA trough concentrations in Chinese allo-HSCT patients. METHODS A total of 7874 cases of CsA therapeutic drug monitoring data from 2069 allo-HSCT patients were retrospectively included. Sequential forward selection was used to select variable subsets, and eight different algorithms were applied to establish the prediction model. RESULTS XGBoost exhibited the highest prediction ability. Except for the variables that were identified by previous studies, some rarely reported variables were found, such as norethindrone, WBC, PAB, and hCRP. The prediction accuracy within ±30% of the actual trough concentration was above 0.80, and the predictive ability of the models was demonstrated to be effective in external validation. CONCLUSION In this study, models based on machine learning technology were established to predict CsA levels 3-4 days in advance during the early inpatient phase after HSCT. A new perspective for CsA clinical application is provided.
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Affiliation(s)
- Lin Song
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China.,College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Chen-Rong Huang
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shi-Zheng Pan
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China.,College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Jian-Guo Zhu
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zong-Qi Cheng
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xun Yu
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ling Xue
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Fan Xia
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | | | - De-Pei Wu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Li-Yan Miao
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China.,College of Pharmaceutical Sciences, Soochow University, Suzhou, China.,National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Suzhou, China
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Zhou XJ, Zhong XH, Duan LX. Integration of artificial intelligence and multi-omics in kidney diseases. FUNDAMENTAL RESEARCH 2023; 3:126-148. [PMID: 38933564 PMCID: PMC11197676 DOI: 10.1016/j.fmre.2022.01.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/14/2021] [Accepted: 01/24/2022] [Indexed: 10/18/2022] Open
Abstract
Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.
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Affiliation(s)
- Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
| | - Xu-Hui Zhong
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Li-Xin Duan
- The Big Data Research Center, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
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21
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Yuan W, Sui L, Xin H, Liu M, Shi H. Discussion on machine learning technology to predict tacrolimus blood concentration in patients with nephrotic syndrome and membranous nephropathy in real-world settings. BMC Med Inform Decis Mak 2022; 22:336. [PMID: 36539772 PMCID: PMC9764593 DOI: 10.1186/s12911-022-02089-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Given its narrow treatment window, high toxicity, adverse effects, and individual differences in its use, we collected and sorted data on tacrolimus use by real patients with kidney diseases. We then used machine learning technology to predict tacrolimus blood concentration in order to provide a basis for tacrolimus dose adjustment and ensure patient safety. METHODS This study involved 913 hospitalized patients with nephrotic syndrome and membranous nephropathy treated with tacrolimus. We evaluated data related to patient demographics, laboratory tests, and combined medication. After data cleaning and feature engineering, six machine learning models were constructed, and the predictive performance of each model was evaluated via external verification. RESULTS The XGBoost model outperformed other investigated models, with a prediction accuracy of 73.33%, F-beta of 91.24%, and AUC of 0.5531. CONCLUSIONS Through this exploratory study, we could determine the ability of machine learning to predict TAC blood concentration. Although the results prove the predictive potential of machine learning to some extent, in-depth research is still needed to resolve the XGBoost model's bias towards positive class and thereby facilitate its use in real-world settings.
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Affiliation(s)
- Weijia Yuan
- grid.414252.40000 0004 1761 8894Department of Information, Medical Supplies Center of PLA General Hospital, Beijing, China
| | - Lin Sui
- grid.414252.40000 0004 1761 8894Department of Pharmacy, Medical Supplies Center of PLA General Hospital, Beijing, China
| | - Haili Xin
- grid.414252.40000 0004 1761 8894Department of Pharmacy, Medical Supplies Center of PLA General Hospital, Beijing, China
| | - Minchao Liu
- grid.414252.40000 0004 1761 8894Department of Information, Medical Supplies Center of PLA General Hospital, Beijing, China
| | - Huayu Shi
- grid.414252.40000 0004 1761 8894Department of Information, Medical Supplies Center of PLA General Hospital, Beijing, China
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22
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Mekov E, Ilieva V. Machine learning in lung transplantation: Where are we? Presse Med 2022; 51:104140. [PMID: 36252820 DOI: 10.1016/j.lpm.2022.104140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022] Open
Abstract
Lung transplantation has been accepted as a viable treatment for end-stage respiratory failure. While regression models continue to be a standard approach for attempting to predict patients' outcomes after lung transplantation, more sophisticated supervised machine learning (ML) techniques are being developed and show encouraging results. Transplant clinicians could utilize ML as a decision-support tool in a variety of situations (e.g. waiting list mortality, donor selection, immunosuppression, rejection prediction). Although for some topics ML is at an advanced stage of research (i.e. imaging and pathology) there are certain topics in lung transplantation that needs to be aware of the benefits it could provide.
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Affiliation(s)
- Evgeni Mekov
- Department of Occupational Diseases, Faculty of Medicine, Medical University - Sofia, Sofia, Bulgaria
| | - Viktoria Ilieva
- Department of Anesthesiology and Intensive Care, Faculty of Medicine, Medical University - Sofia, Sofia, Bulgaria.
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23
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Huang Q, Lin X, Wang Y, Chen X, Zheng W, Zhong X, Shang D, Huang M, Gao X, Deng H, Li J, Zeng F, Mo X. Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction. Front Pharmacol 2022; 13:942129. [DOI: 10.3389/fphar.2022.942129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/30/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC.Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance.Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42).Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.
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24
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Kherabi Y, Messika J, Peiffer‐Smadja N. Machine learning, antimicrobial stewardship, and solid organ transplantation: Is this the future? Transpl Infect Dis 2022; 24:e13957. [DOI: 10.1111/tid.13957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Yousra Kherabi
- Infectious and Tropical Diseases Department Bichat‐Claude Bernard Hospital Assistance Publique‐Hôpitaux de Paris Paris France
| | - Jonathan Messika
- Université Paris Cité AP‐HP Bichat‐Claude Bernard Hospital Pneumologie B et Transplantation Pulmonaire Paris France
| | - Nathan Peiffer‐Smadja
- Infectious and Tropical Diseases Department Bichat‐Claude Bernard Hospital Assistance Publique‐Hôpitaux de Paris Paris France
- Université Paris Cité and Université Sorbonne Paris Nord Inserm IAME Paris France
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25
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Verhaeghe J, Dhaese SAM, De Corte T, Vander Mijnsbrugge D, Aardema H, Zijlstra JG, Verstraete AG, Stove V, Colin P, Ongenae F, De Waele JJ, Van Hoecke S. Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients. BMC Med Inform Decis Mak 2022; 22:224. [PMID: 36008808 PMCID: PMC9404625 DOI: 10.1186/s12911-022-01970-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 08/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations. Furthermore, many currently developed models for clinical applications often lack uncertainty quantification. We, therefore, aimed to develop machine learning (ML) models for the prediction of piperacillin plasma concentrations while also providing uncertainty quantification with the aim of clinical practice. METHODS Blood samples for piperacillin analysis were prospectively collected from critically ill patients receiving continuous infusion of piperacillin/tazobactam. Interpretable ML models for the prediction of piperacillin concentrations were designed using CatBoost and Gaussian processes. Distribution-based Uncertainty Quantification was added to the CatBoost model using a proposed Quantile Ensemble method, useable for any model optimizing a quantile function. These models are subsequently evaluated using the distribution coverage error, a proposed interpretable uncertainty quantification calibration metric. Development and internal evaluation of the ML models were performed on the Ghent University Hospital database (752 piperacillin concentrations from 282 patients). Ensuing, ML models were compared with a published PopPK model on a database from the University Medical Centre of Groningen where a different dosing regimen is used (46 piperacillin concentrations from 15 patients.). RESULTS The best performing model was the Catboost model with an RMSE and [Formula: see text] of 31.94-0.64 and 33.53-0.60 for internal evaluation with and without previous concentration. Furthermore, the results prove the added value of the proposed Quantile Ensemble model in providing clinically useful individualized uncertainty predictions and show the limits of homoscedastic methods like Gaussian Processes in clinical applications. CONCLUSIONS Our results show that ML models can consistently estimate piperacillin concentrations with acceptable and high predictive accuracy when identical dosing regimens as in the training data are used while providing highly relevant uncertainty predictions. However, generalization capabilities to other dosing schemes are limited. Notwithstanding, incorporating ML models in therapeutic drug monitoring programs seems definitely promising and the current work provides a basis for validating the model in clinical practice.
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Affiliation(s)
- Jarne Verhaeghe
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium.
| | - Sofie A M Dhaese
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | - Thomas De Corte
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | | | - Heleen Aardema
- Department of Critical Care, University Medical Center Groningen, Groningen, The Netherlands
| | - Jan G Zijlstra
- Department of Critical Care, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Veronique Stove
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Pieter Colin
- Department of Anesthesiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Femke Ongenae
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium
| | - Jan J De Waele
- Department of Critical Care Medicine, Ghent University Hospital, Ghent, Belgium
| | - Sofie Van Hoecke
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium.
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26
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Peloso A, Moeckli B, Delaune V, Oldani G, Andres A, Compagnon P. Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation. Transpl Int 2022; 35:10640. [PMID: 35859667 PMCID: PMC9290190 DOI: 10.3389/ti.2022.10640] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022]
Abstract
Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolving and it undoubtedly holds an enormous potential for the field of solid organ transplantation. In this review, we provide an overview of AI-based approaches in solid organ transplantation. Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology. The potential implementations are vast—from improved allocation algorithms, smart donor-recipient matching and dynamic adaptation of immunosuppression to automated analysis of transplant pathology. We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival. This manuscript provides a glimpse into how AI innovations could shape an exciting future for the transplantation community.
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Affiliation(s)
- Andrea Peloso
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- *Correspondence: Andrea Peloso,
| | - Beat Moeckli
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Vaihere Delaune
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Graziano Oldani
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Axel Andres
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Philippe Compagnon
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
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27
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Zhang Q, Tian X, Chen G, Yu Z, Zhang X, Lu J, Zhang J, Wang P, Hao X, Huang Y, Wang Z, Gao F, Yang J. A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques. Front Med (Lausanne) 2022; 9:813117. [PMID: 35712101 PMCID: PMC9197124 DOI: 10.3389/fmed.2022.813117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, the narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcomes. The aim of this study was to compare different machine learning and deep learning algorithms and establish individualized dose prediction models by using the best performing algorithm. Therefore, among the 10 commonly used algorithms we compared, the TabNet algorithm outperformed other algorithms with the highest R2 (0.824), the lowest prediction error [mean absolute error (MAE) 0.468, mean square error (MSE) 0.558, and root mean square error (RMSE) 0.745], and good performance of overestimated (5.29%) or underestimated dose percentage (8.52%). In the final prediction model, the last tacrolimus daily dose, the last tacrolimus therapeutic drug monitoring value, time after transplantation, hematocrit, serum creatinine, aspartate aminotransferase, weight, CYP3A5, body mass index, and uric acid were the most influential variables on tacrolimus daily dose. Our study provides a reference for the application of deep learning technique in tacrolimus dose estimation, and the TabNet model with desirable predictive performance is expected to be expanded and applied in future clinical practice.
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Affiliation(s)
- Qiwen Zhang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Xueke Tian
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Guang Chen
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Ze Yu
- Beijing Medicinovo Technology Co. Ltd, Beijing, China
| | - Xiaojian Zhang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Jingli Lu
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co. Ltd, Beijing, China
| | - Peile Wang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Xin Hao
- Dalian Medicinovo Technology Co. Ltd, Dalian, China
| | - Yining Huang
- McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Zeyuan Wang
- Beijing Medicinovo Technology Co. Ltd, Beijing, China
| | - Fei Gao
- Beijing Medicinovo Technology Co. Ltd, Beijing, China
| | - Jing Yang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
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28
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Yang M, Peng B, Zhuang Q, Li J, Liu H, Cheng K, Ming Y. Models to predict the short-term survival of acute-on-chronic liver failure patients following liver transplantation. BMC Gastroenterol 2022; 22:80. [PMID: 35196992 PMCID: PMC8867783 DOI: 10.1186/s12876-022-02164-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 02/15/2022] [Indexed: 12/23/2022] Open
Abstract
Background Acute-on-chronic liver failure (ACLF) is featured with rapid deterioration of chronic liver disease and poor short-term prognosis. Liver transplantation (LT) is recognized as the curative option for ACLF. However, there is no standard in the prediction of the short-term survival among ACLF patients following LT. Method Preoperative data of 132 ACLF patients receiving LT at our center were investigated retrospectively. Cox regression was performed to determine the risk factors for short-term survival among ACLF patients following LT. Five conventional score systems (the MELD score, ABIC, CLIF-C OFs, CLIF-SOFAs and CLIF-C ACLFs) in forecasting short-term survival were estimated through the receiver operating characteristic (ROC). Four machine-learning (ML) models, including support vector machine (SVM), logistic regression (LR), multi-layer perceptron (MLP) and random forest (RF), were also established for short-term survival prediction. Results Cox regression analysis demonstrated that creatinine (Cr) and international normalized ratio (INR) were the two independent predictors for short-term survival among ACLF patients following LT. The ROC curves showed that the area under the curve (AUC) ML models was much larger than that of conventional models in predicting short-term survival. Among conventional models the model for end stage liver disease (MELD) score had the highest AUC (0.704), while among ML models the RF model yielded the largest AUC (0.940). Conclusion Compared with the traditional methods, the ML models showed good performance in the prediction of short-term prognosis among ACLF patients following LT and the RF model perform the best. It is promising to optimize organ allocation and promote transplant survival based on the prediction of ML models. Supplementary Information The online version contains supplementary material available at 10.1186/s12876-022-02164-6.
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Affiliation(s)
- Min Yang
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Bo Peng
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Quan Zhuang
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Junhui Li
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Hong Liu
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Ke Cheng
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Yingzi Ming
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China.
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29
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Zhu X, Peng B, Yi Q, Liu J, Yan J. Prediction Model of Immunosuppressive Medication Non-adherence for Renal Transplant Patients Based on Machine Learning Technology. Front Med (Lausanne) 2022; 9:796424. [PMID: 35252242 PMCID: PMC8895304 DOI: 10.3389/fmed.2022.796424] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 01/27/2022] [Indexed: 11/25/2022] Open
Abstract
Objectives Predicting adherence to immunosuppressive medication (IM) is important to improve and design future prospective, personalized interventions in Chinese renal transplant patients (RTPs). Methods A retrospective, multicenter, cross-sectional study was performed in 1,191 RTPs from October 2020 to February 2021 in China. The BAASIS was used as the standard to determine the adherence of the patients. Variables of the combined theory, including the general data, the HBM, the TPB, the BMQ, the PSSS and the GSES, were used to build the models. The machine learning (ML) models included LR, RF, MLP, SVM, and XG Boost. The SHAP method was used to evaluate the contribution of predictors to predicting the risk of IM non-adherence in RTPs. Results The IM non-adherence rate in the derivation cohort was 38.5%. Ten predictors were screened to build the model based on the database. The SVM model performed better among the five models, with sensitivity of 0.59, specificity of 0.73, and average AUC of 0.75. The SHAP analysis showed that age, marital status, HBM-perceived barriers, use pill box after transplantation, and PSSS-family support were the most important predictors in the prediction model. All of the models had good performance validated by external data. Conclusions The IM non-adherence rate of RTPs was high, and it is important to improve IM adherence. The model developed by ML technology could identify high-risk patients and provide a basis for the development of relevant improvement measures.
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Affiliation(s)
- Xiao Zhu
- Nursing Department of Third Xiangya Hospital of Central South University, Changsha, China
- Research Center of Chinese Health Ministry on Transplantation Medicine Engineering and Technology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Bo Peng
- Research Center of Chinese Health Ministry on Transplantation Medicine Engineering and Technology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - QiFeng Yi
- Nursing School of Central South University, Changsha, China
- Nursing Department of Third Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: QiFeng Yi
| | - Jia Liu
- Nursing School of Central South University, Changsha, China
- Nursing Department of Third Xiangya Hospital of Central South University, Changsha, China
- Research Center of Chinese Health Ministry on Transplantation Medicine Engineering and Technology, The Third Xiangya Hospital, Central South University, Changsha, China
- Jia Liu
| | - Jin Yan
- Nursing School of Central South University, Changsha, China
- Nursing Department of Third Xiangya Hospital of Central South University, Changsha, China
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30
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Artificial Intelligence and Cardiovascular Genetics. Life (Basel) 2022; 12:life12020279. [PMID: 35207566 PMCID: PMC8875522 DOI: 10.3390/life12020279] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/26/2022] [Accepted: 02/09/2022] [Indexed: 12/13/2022] Open
Abstract
Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.
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31
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Mo X, Chen X, Wang X, Zhong X, Liang H, Wei Y, Deng H, Hu R, Zhang T, Chen Y, Gao X, Huang M, Li J. Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach. Pharmgenomics Pers Med 2022; 15:143-155. [PMID: 35228813 PMCID: PMC8881964 DOI: 10.2147/pgpm.s339318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/20/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose Tacrolimus (TAC) is a first-line immunosuppressant for patients with refractory nephrotic syndrome (NS). However, there is a high inter-patient variability of TAC pharmacokinetics, thus therapeutic drug monitoring (TDM) is required. In this study, we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough concentration (C0/D) in Chinese children with refractory NS, and then develop and validate the TAC C0/D prediction models. Patients and Methods The association of 82 clinical variables and 244 single nucleotide polymorphisms (SNPs) with TAC C0/D in the third month since TAC treatment was examined in 171 children with refractory NS. Extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), extreme gradient boosting (XGBoost), and Lasso regression were carried out to establish and validate prediction models, respectively. The best prediction models were validated on a cohort of 30 refractory NS patients. Results GBDT algorithm performed best in the whole group (R2=0.444, MSE=591.032, MAE=20.782, MedAE=18.980) and CYP3A5 nonexpresser group (R2=0.264, MSE=477.948, MAE=18.119, MedAE=18.771), while ET algorithm performed best in the CYP3A5 expresser group (R2=0.380, MSE=1839.459, MAE=31.257, MedAE=19.399). These prediction models included 3 clinical variables (ALB0, AGE0, and gender) and 10 SNPs (ACTN4 rs3745859, ACTN4 rs56113315, ACTN4 rs62121818, CTLA4 rs4553808, CYP3A5 rs776746, IL2RA rs12722489, INF2 rs1128880, MAP3K11 rs7946115, MYH9 rs2239781, and MYH9 rs4821478). Conclusion The association between the clinical and genetic variables and TAC C0/D was described, and three TAC C0/D prediction models integrating clinical and genetic variables were developed and validated using machine learning, which may support individualized TAC dosing.
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Affiliation(s)
- Xiaolan Mo
- Department of Pharmacy, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623, People’s Republic of China
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China
| | - Xiujuan Chen
- Department of clinical Data Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, People’s Republic of China
| | - Xianggui Wang
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China
| | - Xiaoli Zhong
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China
| | - Huiying Liang
- Department of clinical Data Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, People’s Republic of China
| | - Yuanyi Wei
- Department of Pharmacy, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623, People’s Republic of China
| | - Houliang Deng
- Department of Pharmacy, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623, People’s Republic of China
| | - Rong Hu
- Department of Pharmacy, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623, People’s Republic of China
| | - Tao Zhang
- Department of Pharmacy, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623, People’s Republic of China
| | - Yilu Chen
- Department of Pharmacy, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623, People’s Republic of China
| | - Xia Gao
- Division of Nephrology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623, People’s Republic of China
| | - Min Huang
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China
| | - Jiali Li
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China
- Correspondence: Jiali Li; Min Huang, Tel +86-20-39943034; +86-20-39943011, Fax +86-20-39943004; +86-20-39943000, Email ;
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Fu Q, Jing Y, Liu Mr G, Jiang Mr X, Liu H, Kong Y, Hou X, Cao L, Deng P, Xiao P, Xiao J, Peng H, Wei X. Machine learning-based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients. J Clin Pharm Ther 2021; 47:600-608. [PMID: 34802160 DOI: 10.1111/jcpt.13579] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/28/2021] [Accepted: 11/11/2021] [Indexed: 11/26/2022]
Abstract
WHAT IS KNOWN AND OBJECTIVES Tacrolimus (TAC), a first-line immunosuppressant in solid-organ transplant, has a narrow therapeutic window and large inter-individual variability, which affects its use in clinical practice. Successful predictions using machine learning algorithms have been reported in several fields. However, a comparison of 10 machine learning model-based TAC pharmacogenetic and pharmacokinetic dosing algorithms for kidney transplant perioperative patients of Chinese descent has not been reported. The objective of this study was to screen and establish an appropriate machine learning method to predict the individualized dosages of TAC for perioperative kidney transplant patients. METHODS The records of 2551 patients were collected from three transplant centres, 80% of which were randomly selected as a 'derivation cohort' to develop the dose prediction algorithm, while the remaining 20% constituted a 'validation cohort' to validate the final algorithm selected. Important features were screened according to our previously established population pharmacokinetic model of tacrolimus. The performances of the algorithms were evaluated and compared using R-squared and the mean percentage in the remaining 20% of patients. RESULTS AND DISCUSSION This study identified several factors influencing TAC dosage, including CYP3A5 rs776746, CYP3A4 rs4646437, haematocrit, Wuzhi capsules, TAC daily dose, age, height, weight, post-operative time, nifedipine and the medication history of the patient. According to our results, among the 10 machine learning models, the extra trees regressor (ETR) algorithm showed the best performance in the training set (R-squared: 1, mean percentage within 20%: 100%) and test set (R-squared: 0.85, mean percentage within 20%: 92.77%) of the derivation cohort. The ETR model successfully predicted the ideal TAC dosage in 97.73% of patients, especially in the intermediate dosage range (>5 mg/day to <8 mg/day), whereby the ideal TAC dosage could be successfully predicted in 99% of the patients. WHAT IS NEW AND CONCLUSION The results indicated that the ETR algorithm, which was chosen to establish the dose prediction model, performed better than the other nine machine learning models. This study is the first to establish ETR algorithms to predict TAC dosage. This study will further promote the individualized medication of TAC in kidney transplant patients in the future, which has great significance in ensuring the safety and effectiveness of drug use.
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Affiliation(s)
- Qun Fu
- School of Pharmacy, Nanchang University, Nanchang, China.,Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yan Jing
- School of Pharmacy, Nanchang University, Nanchang, China.,Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | | | - Xuehui Jiang Mr
- School of Pharmacy, Nanchang University, Nanchang, China.,Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hong Liu
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ying Kong
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiongjun Hou
- Department of Clinical Pharmacology, Jiangxi Provincial People's Hospital, Nanchang, China
| | - Lei Cao
- Department of Information, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pei Deng
- Department of Information, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pin Xiao
- Department of Pharmacy, Hospital of Jiangxi Provincial Armed Police Corps, Nanchang, China
| | - Jiansheng Xiao
- Department of Transplantation, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hongwei Peng
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaohua Wei
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Institute of Clinical Pharmacology, Jiangxi Institute of Clinical Medical Sciences, Nanchang, China
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Balch JA, Delitto D, Tighe PJ, Zarrinpar A, Efron PA, Rashidi P, Upchurch GR, Bihorac A, Loftus TJ. Machine Learning Applications in Solid Organ Transplantation and Related Complications. Front Immunol 2021; 12:739728. [PMID: 34603324 PMCID: PMC8481939 DOI: 10.3389/fimmu.2021.739728] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/25/2021] [Indexed: 11/13/2022] Open
Abstract
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.
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Affiliation(s)
- Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Daniel Delitto
- Department of Surgery, Johns Hopkins University, Baltimore, MD, United States
| | - Patrick J Tighe
- Department of Anesthesiology, University of Florida Health, Gainesville, FL, United States.,Department of Orthopedics, University of Florida Health, Gainesville, FL, United States.,Department of Information Systems/Operations Management, University of Florida Health, Gainesville, FL, United States
| | - Ali Zarrinpar
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Philip A Efron
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.,Department of Computer and Information Science and Engineering University of Florida, Gainesville, FL, United States.,Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.,Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Azra Bihorac
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States.,Department of Medicine, University of Florida Health, Gainesville, FL, United States
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States.,Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
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34
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Mo X, Chen X, Ieong C, Gao X, Li Y, Liao X, Yang H, Li H, He F, He Y, Chen Y, Liang H, Huang M, Li J. Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning. Front Pharmacol 2021; 12:638724. [PMID: 34512318 PMCID: PMC8430214 DOI: 10.3389/fphar.2021.638724] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/10/2021] [Indexed: 01/10/2023] Open
Abstract
Background and Aims: Tacrolimus(TAC)-induced nephrotoxicity, which has a large individual variation, may lead to treatment failure or even the end-stage renal disease. However, there is still a lack of effective models for the early prediction of TAC-induced nephrotoxicity, especially in nephrotic syndrome(NS). We aimed to develop and validate a predictive model of TAC-induced tubular toxicity in children with NS using machine learning based on comprehensive clinical and genetic variables. Materials and Methods: A retrospective cohort of 218 children with NS admitted between June 2013 and December 2018 was used to establish the models, and 11 children were prospectively enrolled for external validation. We screened 47 clinical features and 244 genetic variables. The changes in urine N- acetyl- β-D- glucosaminidase(NAG) levels before and after administration was used as an indicator of renal tubular toxicity. Results: Five machine learning algorithms, including extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), extremely random trees (ET), random forest (RF), and logistic regression (LR) were used for model generation and validation. Four genetic variables, including TRPC6 rs3824934_GG, HSD11B1 rs846910_AG, MAP2K6 rs17823202_GG, and SCARB2 rs6823680_CC were incorporated into the final model. The XGBoost model has the best performance: sensitivity 75%, specificity 77.8%, accuracy 77.3%, and AUC 78.9%. Conclusion: A pre-administration model with good performance for predicting TAC-induced nephrotoxicity in NS was developed and validated using machine learning based on genetic factors. Physicians can estimate the possibility of nephrotoxicity in NS patients using this simple and accurate model to optimize treatment regimen before administration or to intervene in time after administration to avoid kidney damage.
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Affiliation(s)
- Xiaolan Mo
- Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xiujuan Chen
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Chifong Ieong
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xia Gao
- Division of Nephrology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yingjie Li
- Division of Nephrology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Xin Liao
- Division of Nephrology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Huabin Yang
- Division of Nephrology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Huiyi Li
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China.,Department of Pharmacy, Guangzhou Institute of Dermatology, Guangzhou, China
| | - Fan He
- Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yanling He
- Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yilu Chen
- Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Huiying Liang
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Min Huang
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Jiali Li
- Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
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35
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 264] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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36
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Connor KL, O'Sullivan ED, Marson LP, Wigmore SJ, Harrison EM. The Future Role of Machine Learning in Clinical Transplantation. Transplantation 2021; 105:723-735. [PMID: 32826798 DOI: 10.1097/tp.0000000000003424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.
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Affiliation(s)
- Katie L Connor
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Eoin D O'Sullivan
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Lorna P Marson
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen J Wigmore
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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37
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Lu J, Deng K, Zhang X, Liu G, Guan Y. Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens. iScience 2021; 24:102804. [PMID: 34308294 PMCID: PMC8283337 DOI: 10.1016/j.isci.2021.102804] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/07/2021] [Accepted: 06/24/2021] [Indexed: 12/17/2022] Open
Abstract
Forecasting pharmacokinetics (PK) for individual patients is a fundamental problem in clinical pharmacology. One key challenge is that PK models constructed using data from one dosing regimen must predict PK data for different dosing regimen(s). We propose a deep learning approach based on neural ordinary differential equations (neural-ODE) and tested its generalizability against a variety of alternative models. Specifically, we used the PK data from two different treatment regimens of trastuzumab emtansine. The models performed similarly when the training and the test sets come from the same dosing regimen. However, for predicting a new treatment regimen, the neural-ODE model showed substantially better performance. To date, neural-ODE is the most accurate PK model in predicting untested treatment regimens. This study represents the first time neural-ODE has been applied to PK modeling and the results suggest it is a widely applicable algorithm with the potential to impact future studies.
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Affiliation(s)
- James Lu
- Modeling & Simulation/Clinical Pharmacology, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Kaiwen Deng
- Ann Arbor Algorithms Inc, 3001 Plymouth Road, Ann Arbor, MI 48105, USA
| | - Xinyuan Zhang
- Ann Arbor Algorithms Inc, 3001 Plymouth Road, Ann Arbor, MI 48105, USA
| | - Gengbo Liu
- Modeling & Simulation/Clinical Pharmacology, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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38
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Seyahi N, Ozcan SG. Artificial intelligence and kidney transplantation. World J Transplant 2021; 11:277-289. [PMID: 34316452 PMCID: PMC8290997 DOI: 10.5500/wjt.v11.i7.277] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/17/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.
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Affiliation(s)
- Nurhan Seyahi
- Department of Nephrology, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
| | - Seyda Gul Ozcan
- Department of Internal Medicine, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
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39
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Zhan M, Chen Z, Ding C, Qu Q, Wang G, Liu S, Wen F. Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning. Int J Hematol 2021; 114:483-493. [PMID: 34170480 DOI: 10.1007/s12185-021-03184-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/21/2021] [Accepted: 06/21/2021] [Indexed: 10/21/2022]
Abstract
This study aimed to establish a predictive model to identify children with hematologic malignancy at high risk for delayed clearance of high-dose methotrexate (HD-MTX) based on machine learning. A total of 205 patients were recruited. Five variables (hematocrit, risk classification, dose, SLC19A1 rs2838958, sex) and three variables (SLC19A1 rs2838958, sex, dose) were statistically significant in univariable analysis and, separately, multivariate logistic regression. The data was randomly split into a "training cohort" and a "validation cohort". A nomogram for prediction of delayed HD-MTX clearance was constructed using the three variables in the training dataset and validated in the validation dataset. Five machine learning algorithms (cart classification and regression trees, naïve Bayes, support vector machine, random forest, C5.0 decision tree) combined with different resampling methods were used for model building with five or three variables. When developed machine learning models were evaluated in the validation dataset, the C5.0 decision tree combined with the synthetic minority oversampling technique (SMOTE) using five variables had the highest area under the receiver operating characteristic curve (AUC 0.807 [95% CI 0.724-0.889]), a better performance than the nomogram (AUC 0.69 [95% CI 0.594-0.787]). The results support potential clinical application of machine learning for patient risk classification.
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Affiliation(s)
- Min Zhan
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, 518036, People's Republic of China
| | - Zebin Chen
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, 518036, People's Republic of China
| | - Changcai Ding
- Department of Research and Development, Shenzhen Advanced Precision Medical CO., LTD, Shenzhen, 518000, People's Republic of China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital Central South University, Changsha, 410008, People's Republic of China
| | - Guoqiang Wang
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, 518036, People's Republic of China
| | - Sixi Liu
- Department of Hematology/Oncology, Shenzhen Children's Hospital, Shenzhen, 518036, People's Republic of China
| | - Feiqiu Wen
- Department of Hematology/Oncology, Shenzhen Children's Hospital, Shenzhen, 518036, People's Republic of China.
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40
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Huang X, Yu Z, Wei X, Shi J, Wang Y, Wang Z, Chen J, Bu S, Li L, Gao F, Zhang J, Xu A. Prediction of vancomycin dose on high-dimensional data using machine learning techniques. Expert Rev Clin Pharmacol 2021; 14:761-771. [PMID: 33835879 DOI: 10.1080/17512433.2021.1911642] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVES Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of individual factors and extensibility is insufficient. We aimed to develop an optimal dosing algorithm for vancomycin based on the high-dimensional data using the proposed variable engineering and machine-learning methods. METHODS This study proposed a variable engineering process that automatically generates second-order variable interactions. We performed an initial examination of independent variables and interactive variables using eXtreme Gradient Boosting. The vancomycin dose prediction model was established based on the derived variables. RESULTS Based on the evaluation of the model performance in the validation cohort, our algorithm accounted for 67.5% of variations in the vancomycin doses. Subgroup analysis showed better performance in patients with medium and high body weight (with the ideal predictive percentage of 72.7% and 73.7%), and low and medium levels of serum creatinine (with the ideal predictive percentage of 77.8% and 73.1%) than in other groups. CONCLUSION The new vancomycin dose prediction model is potentially useful for patients whose population profiles are similar to those of our patients and yielded desired reference of clinical indicators with specific breakpoints.
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Affiliation(s)
- Xiaohui Huang
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ze Yu
- Beijing Medicinovo Technology Co. Ltd., Beijing, China
| | - Xin Wei
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Junfeng Shi
- Department of Nephrology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yu Wang
- Beijing Medicinovo Technology Co. Ltd., Beijing, China
| | - Zeyuan Wang
- Beijing Medicinovo Technology Co. Ltd., Beijing, China.,School of Computer Science, The University of Sydney, Sydney, Australia
| | - Jihui Chen
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Shuhong Bu
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Lixia Li
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Fei Gao
- Beijing Medicinovo Technology Co. Ltd., Beijing, China
| | - Jian Zhang
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ajing Xu
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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41
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Woillard J, Labriffe M, Debord J, Marquet P. Mycophenolic Acid Exposure Prediction Using Machine Learning. Clin Pharmacol Ther 2021; 110:370-379. [DOI: 10.1002/cpt.2216] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 02/01/2021] [Indexed: 01/28/2023]
Affiliation(s)
- Jean‐Baptiste Woillard
- Pharmacology and Transplantation UMR1248 INSERM Université de Limoges Limoges France
- Department of Pharmacology Toxicology and Pharmacovigilance University Hospital of Limoges Limoges France
| | - Marc Labriffe
- Pharmacology and Transplantation UMR1248 INSERM Université de Limoges Limoges France
- Department of Pharmacology Toxicology and Pharmacovigilance University Hospital of Limoges Limoges France
| | - Jean Debord
- Pharmacology and Transplantation UMR1248 INSERM Université de Limoges Limoges France
- Department of Pharmacology Toxicology and Pharmacovigilance University Hospital of Limoges Limoges France
| | - Pierre Marquet
- Pharmacology and Transplantation UMR1248 INSERM Université de Limoges Limoges France
- Department of Pharmacology Toxicology and Pharmacovigilance University Hospital of Limoges Limoges France
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42
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Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence. TRANSPLANTOLOGY 2021. [DOI: 10.3390/transplantology2020012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field.
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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. A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters. Sci Rep 2021; 11:5568. [PMID: 33692435 PMCID: PMC7946912 DOI: 10.1038/s41598-021-85157-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 02/23/2021] [Indexed: 12/11/2022] Open
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.
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Affiliation(s)
- Xiuqing Zhu
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Wencan Huang
- Department of Pharmacy, Guangzhou Bureau of Civil Affairs Psychiatric Hospital, Guangzhou, 510430, China
| | - Haoyang Lu
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Zhanzhang Wang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Xiaojia Ni
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Jinqing Hu
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Shuhua Deng
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Yaqian Tan
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Lu Li
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Ming Zhang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Chang Qiu
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Yayan Luo
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China
| | - Hongzhen Chen
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China
| | - Shanqing Huang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China
| | - Tao Xiao
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China
| | - Dewei Shang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China. .,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China.
| | - Yuguan Wen
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China. .,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China.
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McComb M, Bies R, Ramanathan M. Machine learning in pharmacometrics: Opportunities and challenges. Br J Clin Pharmacol 2021; 88:1482-1499. [PMID: 33634893 DOI: 10.1111/bcp.14801] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 02/08/2021] [Accepted: 02/12/2021] [Indexed: 12/13/2022] Open
Abstract
The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.
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Affiliation(s)
- Mason McComb
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Bies
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Institute for Computational Data Science, University at Buffalo, NY, USA
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Department of Neurology, University at Buffalo, State University of New York, Buffalo, NY, USA
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Artificial neural network and bioavailability of the immunosuppression drug. Curr Opin Organ Transplant 2021; 25:435-441. [PMID: 32452906 DOI: 10.1097/mot.0000000000000770] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE OF REVIEW The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees (CART) can handle multiple independent variables and predict the dependent variables by deducing the complex nonlinear relationships between variables. RECENT FINDINGS In the last two decades, several researchers employed these tools to identify donor-recipient matching pairs, to optimize immunosuppressant doses, to predict allograft survival and to minimize adverse drug reactions. These models showed better performance characteristics than the empirical dosing strategies in terms of sensitivity, specificity, overall accuracy, or area under the curve of receiver-operating characteristic curves. The performance of the models was dependent directly on the input variables. Recent studies identified protein biomarkers and pharmacogenetic determinants of immunosuppressants as additional variables that increase the precision in prediction. Accessibility of medical records, proper follow-up of transplant cases, deep understanding of pharmacokinetic and pharmacodynamic pathways of immunosuppressant drugs coupled with genomic and proteomic markers are essential in developing an effective artificial intelligence platform for transplantation. SUMMARY Artificial intelligence has a greater clinical utility both in pretransplantation and posttransplantation periods to get favourable clinical outcomes, thus ensuring successful graft survival.
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Sessa M, Liang D, Khan AR, Kulahci M, Andersen M. Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 2-Comparison of the Performance of Artificial Intelligence and Traditional Pharmacoepidemiological Techniques. Front Pharmacol 2021; 11:568659. [PMID: 33519433 PMCID: PMC7841344 DOI: 10.3389/fphar.2020.568659] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 12/07/2020] [Indexed: 01/14/2023] Open
Abstract
Aim: To summarize the evidence on the performance of artificial intelligence vs. traditional pharmacoepidemiological techniques. Methods: Ovid MEDLINE (01/1950 to 05/2019) was searched to identify observational studies, meta-analyses, and clinical trials using artificial intelligence techniques having a drug as the exposure or the outcome of the study. Only studies with an available full text in the English language were evaluated. Results: In all, 72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods. In total, 44 comparisons have been performed in articles that aimed at 1) predicting the needed dosage given the patient’s characteristics (31.8%), 2) predicting the clinical response following a pharmacological treatment (29.5%), 3) predicting the occurrence/severity of adverse drug reactions (20.5%), 4) predicting the propensity score (9.1%), 5) identifying subpopulation more at risk of drug inefficacy (4.5%), 6) predicting drug consumption (2.3%), and 7) predicting drug-induced lengths of stay in hospital (2.3%). In 22 out of 44 (50.0%) comparisons, artificial intelligence performed better than traditional pharmacoepidemiological techniques. Random forest (seven out of 11 comparisons; 63.6%) and artificial neural network (six out of 10 comparisons; 60.0%) were the techniques that in most of the comparisons outperformed traditional pharmacoepidemiological methods. Conclusion: Only a small fraction of articles compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods and not all artificial intelligence techniques have been compared in a Pharmacoepidemiological setting. However, in 50% of comparisons, artificial intelligence performed better than pharmacoepidemiological techniques.
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Affiliation(s)
- Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - David Liang
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Abdul Rauf Khan
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Murat Kulahci
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.,Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå, Sweden
| | - Morten Andersen
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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Yao L, Zhang H, Zhang M, Chen X, Zhang J, Huang J, Zhang L. Application of artificial intelligence in renal disease. CLINICAL EHEALTH 2021. [DOI: 10.1016/j.ceh.2021.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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Laszczyńska O, Severo M, Correia S, Azevedo A. Estimation of Missing Baseline Serum Creatinine for Acute Kidney Injury Diagnosis in Hospitalized Patients. Nephron Clin Pract 2020; 145:123-132. [PMID: 33341804 DOI: 10.1159/000512080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 09/30/2020] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION In hospitalized patients, information on preadmission kidney function is often missing, impeding timely and accurate acute kidney injury (AKI) detection and affecting results of AKI-related studies. OBJECTIVE In this study, we provided estimates of preadmission serum creatinine (SCr), based on a multivariate linear regression (Model 1) and random forest model (Model 2) built with different parametrizations. Their accuracy for AKI diagnosis was compared with the accuracy of commonly used surrogate methods: (i) SCr at hospital admission (first SCr) and (ii) SCr back-calculated from the assumed estimated glomerular filtration rate of 75 mL/min/1.73 m2 (eGFR 75). METHODS From 44,670 unique adult admissions to a tertiary referral centre between 2013 and 2015, we analysed 8,540 patients with preadmission SCr available. To control for differences in characteristics of patients with and without SCr, we used an inverse probability weighting technique. RESULTS Estimates of SCr were likely to be higher than true preadmission SCr in a low Cr concentration and undervalued in high concentrations although for Model 2 Complete-SCr these differences were smallest. The true cumulative incidence of AKI was 14.8%. Model 2 Complete-SCr had the best agreement for AKI diagnosis (kappa 0.811, 95% CI 0.787-0.835), while surrogate methods resulted in the lowest agreement: (kappa 0.553, 0.516-0.590) and (0.648, 0.620-0.676) for first SCr and eGFR 75, respectively. CONCLUSIONS Multivariable imputation of preadmission SCr, taking into account elementary admission data, improved accuracy in AKI diagnosis over commonly used surrogate methods. Random forest-based models can serve as an effective tool in research.
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Affiliation(s)
- Olga Laszczyńska
- EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal,
| | - Milton Severo
- EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Sofia Correia
- EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Ana Azevedo
- EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal.,Centro de Epidemiologia Hospitalar, Centro Hospitalar Universitário de São João, EPE, Porto, Portugal
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Kan J, Li A, Zou H, Chen L, Du J. A Machine Learning Based Dose Prediction of Lutein Supplements for Individuals With Eye Fatigue. Front Nutr 2020; 7:577923. [PMID: 33304916 PMCID: PMC7691662 DOI: 10.3389/fnut.2020.577923] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/27/2020] [Indexed: 01/10/2023] Open
Abstract
Purpose: Nutritional intervention was always implemented based on "one-size-fits-all" recommendation instead of personalized strategy. We aimed to develop a machine learning based model to predict the optimal dose of a botanical combination of lutein ester, zeaxanthin, extracts of black currant, chrysanthemum, and goji berry for individuals with eye fatigue. Methods: 504 features, including demographic, anthropometrics, eye-related indexes, blood biomarkers, and dietary habits, were collected at baseline from 303 subjects in a randomized controlled trial. An aggregated score of visual health (VHS) was developed from total score of eye fatigue symptoms, visuognosis persistence, macular pigment optical density, and Schirmer test to represent an overall eye fatigue level. VHS at 45 days after intervention was predicted by XGBoost algorithm using all features at baseline to show the eye fatigue improvement. Optimal dose of the combination was chosen based on the predicted VHS. Results: After feature selection and parameter optimization, a model was trained and optimized with a Pearson's correlation coefficient of 0.649, 0.638, and 0.685 in training, test and validation set, respectively. After removing the features collected by invasive blood test and costly optical coherence tomography, the model remained good performance. Among 58 subjects in test and validation sets, 39 should take the highest dose as the optimal option, 17 might take a lower dose, while 2 could not benefit from the combination. Conclusion: We applied XGBoost algorithm to develop a model which could predict optimized dose of the combination to provide personalized nutrition solution for individuals with eye fatigue.
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Affiliation(s)
- Juntao Kan
- Nutrilite Health Institute, Shanghai, China
| | - Ao Li
- Department of Bioinformatics, WuXi NextCODE Genomics, Shanghai, China
| | - Hong Zou
- Department of Bioinformatics, WuXi NextCODE Genomics, Shanghai, China
| | - Liang Chen
- Nutrilite Health Institute, Shanghai, China
| | - Jun Du
- Nutrilite Health Institute, Shanghai, China
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Kawakita S, Beaumont JL, Jucaud V, Everly MJ. Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning. Sci Rep 2020; 10:18409. [PMID: 33110142 PMCID: PMC7591492 DOI: 10.1038/s41598-020-75473-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 10/15/2020] [Indexed: 02/06/2023] Open
Abstract
Machine learning (ML) has shown its potential to improve patient care over the last decade. In organ transplantation, delayed graft function (DGF) remains a major concern in deceased donor kidney transplantation (DDKT). To this end, we harnessed ML to build personalized prognostic models to predict DGF. Registry data were obtained on adult DDKT recipients for model development (n = 55,044) and validation (n = 6176). Incidence rates of DGF were 25.1% and 26.3% for the development and validation sets, respectively. Twenty-six predictors were identified via recursive feature elimination with random forest. Five widely-used ML algorithms-logistic regression (LR), elastic net, random forest, artificial neural network (ANN), and extreme gradient boosting (XGB) were trained and compared with a baseline LR model fitted with previously identified risk factors. The new ML models, particularly ANN with the area under the receiver operating characteristic curve (ROC-AUC) of 0.732 and XGB with ROC-AUC of 0.735, exhibited superior performance to the baseline model (ROC-AUC = 0.705). This study demonstrates the use of ML as a viable strategy to enable personalized risk quantification for medical applications. If successfully implemented, our models may aid in both risk quantification for DGF prevention clinical trials and personalized clinical decision making.
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Affiliation(s)
| | | | - Vadim Jucaud
- Terasaki Research Institute, Los Angeles, CA, USA
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