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Lee CY, Chuang YS, Kor CT, Lin YT, Tsao YH, Lin PR, Hsieh HM, Shen MC, Wang YL, Fang TJ, Liu YT. Development of a Predictive Model for Potentially Inappropriate Medications in Older Patients with Cardiovascular Disease. Drugs Aging 2024:10.1007/s40266-024-01127-8. [PMID: 38937426 DOI: 10.1007/s40266-024-01127-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/27/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Older patients with cardiovascular disease (CVD) are highly susceptible to adverse drug reactions due to age-related physiological changes and the presence of multiple comorbidities, polypharmacy, and potentially inappropriate medications (PIMs). OBJECTIVE This study aimed to develop a predictive model to identify the use of PIMs in older patients with CVD. METHODS Data from 2012 to 2021 from the Changhua Christian Hospital Clinical Research Database (CCHRD) and the Kaohsiung Medical University Hospital Research Database (KMUHRD) were analyzed. Participants over the age of 65 years with CVD diagnoses were included. The CCHRD data were randomly divided into a training set (80% of the database) and an internal validation set (20% of the database), while the KMUHRD data served as an external validation set. The training set was used to construct the prediction models, and both validation sets were used to validate the proposed models. RESULTS A total of 48,569 patients were included. Comprehensive data analysis revealed significant associations between the use of PIMs and clinical factors such as total cholesterol, glycated hemoglobin (HbA1c), creatinine, and uric acid levels, as well as the presence of diabetes, hypertension, and cerebrovascular accidents. The predictive models demonstrated moderate power, indicating the importance of these factors in assessing the risk of PIMs. CONCLUSIONS This study developed predictive models that improve understanding of the use of PIMs in older patients with CVD. These models may assist clinicians in making informed decisions regarding medication safety.
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Affiliation(s)
- Chun-Ying Lee
- Department of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yun-Shiuan Chuang
- Department of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chew-Teng Kor
- Big Data Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Yi-Ting Lin
- Department of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yu-Hsiang Tsao
- Department of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- Division of Medical Statistics and Bioinformatics, Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Pei-Ru Lin
- Big Data Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Hui-Min Hsieh
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- Division of Medical Statistics and Bioinformatics, Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
- Department of Community Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Mei-Chiou Shen
- Department of Pharmacy, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ya-Ling Wang
- Department of Pharmacy, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Tzu-Jung Fang
- School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Division of Geriatrics and Gerontology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Yen-Tze Liu
- Big Data Center, Changhua Christian Hospital, Changhua, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
- Department of Family Medicine, Changhua Christian Hospital, Changhua, 500, Taiwan.
- Graduate Institute of Clinical Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
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Xiong Y, Liu YM, Hu JQ, Zhu BQ, Wei YK, Yang Y, Wu XW, Long EW. A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning. Front Pharmacol 2024; 14:1259596. [PMID: 38269284 PMCID: PMC10806526 DOI: 10.3389/fphar.2023.1259596] [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: 07/16/2023] [Accepted: 12/12/2023] [Indexed: 01/26/2024] Open
Abstract
Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.
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Affiliation(s)
- Yu Xiong
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu-Meng Liu
- Department of Pharmacy, Daping Hospital, Army Medical University, Chongqing, China
| | - Jia-Qiang Hu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Bao-Qiang Zhu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- School of Pharmacy, Southwest Medical University, Luzhou, Sichuan, China
| | - Yuan-Kui Wei
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Yan Yang
- Department of Endocrinology and Metabolism, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, Sichuan, China
| | - Xing-Wei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - En-Wu Long
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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Chalasani SH, Syed J, Ramesh M, Patil V, Pramod Kumar T. Artificial intelligence in the field of pharmacy practice: A literature review. EXPLORATORY RESEARCH IN CLINICAL AND SOCIAL PHARMACY 2023; 12:100346. [PMID: 37885437 PMCID: PMC10598710 DOI: 10.1016/j.rcsop.2023.100346] [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: 07/15/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023] Open
Abstract
Artificial intelligence (AI) is a transformative technology used in various industrial sectors including healthcare. In pharmacy practice, AI has the potential to significantly improve medication management and patient care. This review explores various AI applications in the field of pharmacy practice. The incorporation of AI technologies provides pharmacists with tools and systems that help them make accurate and evidence-based clinical decisions. By using AI algorithms and Machine Learning, pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations tailored to individual patient requirements. Various AI models have been developed to predict and detect adverse drug events, assist clinical decision support systems with medication-related decisions, automate dispensing processes in community pharmacies, optimize medication dosages, detect drug-drug interactions, improve adherence through smart technologies, detect and prevent medication errors, provide medication therapy management services, and support telemedicine initiatives. By incorporating AI into clinical practice, health care professionals can augment their decision-making processes and provide patients with personalized care. AI allows for greater collaboration between different healthcare services provided to a single patient. For patients, AI may be a useful tool for providing guidance on how and when to take a medication, aiding in patient education, and promoting medication adherence and AI may be used to know how and where to obtain the most cost-effective healthcare and how best to communicate with healthcare professionals, optimize the health monitoring using wearables devices, provide everyday lifestyle and health guidance, and integrate diet and exercise.
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Affiliation(s)
- Sri Harsha Chalasani
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Jehath Syed
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Madhan Ramesh
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Vikram Patil
- Dept. of Radiology, JSS Medical College & Hospital, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
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林 工, 滕 飞, 胡 巧, 金 朝, 徐 珽, Haibo Z. [Knowledge Graph-Based Prediction of Potentially Inappropriate Medication]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:884-891. [PMID: 37866942 PMCID: PMC10579076 DOI: 10.12182/20230960108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Indexed: 10/24/2023]
Abstract
Objective To improve the accuracy of potentially inappropriate medication (PIM) prediction, a PIM prediction model that combines knowledge graph and machine learning was proposed. Methods Firstly, based on Beers criteria 2019 and using the knowledge graph as the basic structure, a PIM knowledge representation framework with logical expression capabilities was constructed, and a PIM inference process was implemented from patient information nodes to PIM nodes. Secondly, a machine learning prediction model for each PIM label was established based on the classifier chain algorithm, to learn the potential feature associations from the data. Finally, based on a threshold of sample size, a portion of reasoning results from the knowledge graph was used as output labels on the classifier chain to enhance the reliability of the prediction results of low-frequency PIMs. Results 11 741 prescriptions from 9 medical institutions in Chengdu were used to evaluate the effectiveness of the model. Experimental results show that the accuracy of the model for PIM quantity prediction is 98.10%, the F1 is 93.66%, the Hamming loss for PIM multi-label prediction is 0.06%, and the macroF1 is 66.09%, which has higher prediction accuracy than the existing models. Conclusion The method proposed has better prediction performance for potentially inappropriate medication and significantly improves the recognition of low-frequency PIM labels.
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Affiliation(s)
- 工钞 林
- 西南交通大学计算机与人工智能学院 (成都 611756)School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
| | - 飞 滕
- 西南交通大学计算机与人工智能学院 (成都 611756)School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
| | - 巧织 胡
- 西南交通大学计算机与人工智能学院 (成都 611756)School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
| | - 朝辉 金
- 西南交通大学计算机与人工智能学院 (成都 611756)School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
| | - 珽 徐
- 西南交通大学计算机与人工智能学院 (成都 611756)School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
| | - Zhang Haibo
- 西南交通大学计算机与人工智能学院 (成都 611756)School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
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Shirazibeheshti A, Ettefaghian A, Khanizadeh F, Wilson G, Radwan T, Luca C. Automated Detection of Patients at High Risk of Polypharmacy including Anticholinergic and Sedative Medications. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6178. [PMID: 37372763 DOI: 10.3390/ijerph20126178] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/01/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023]
Abstract
Ensuring that medicines are prescribed safely is fundamental to the role of healthcare professionals who need to be vigilant about the risks associated with drugs and their interactions with other medicines (polypharmacy). One aspect of preventative healthcare is to use artificial intelligence to identify patients at risk using big data analytics. This will improve patient outcomes by enabling pre-emptive changes to medication on the identified cohort before symptoms present. This paper presents a mean-shift clustering technique used to identify groups of patients at the highest risk of polypharmacy. A weighted anticholinergic risk score and a weighted drug interaction risk score were calculated for each of 300,000 patient records registered with a major regional UK-based healthcare provider. The two measures were input into the mean-shift clustering algorithm and this grouped patients into clusters reflecting different levels of polypharmaceutical risk. Firstly, the results showed that, for most of the data, the average scores are not correlated and, secondly, the high risk outliers have high scores for one measure but not for both. These suggest that any systematic recognition of high-risk groups should consider both anticholinergic and drug-drug interaction risks to avoid missing high-risk patients. The technique was implemented in a healthcare management system and easily and automatically identifies groups at risk far faster than the manual inspection of patient records. This is much less labour-intensive for healthcare professionals who can focus their assessment only on patients within the high-risk group(s), enabling more timely clinical interventions where necessary.
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Affiliation(s)
| | | | - Farbod Khanizadeh
- Operation & Information Management, Aston Business School, Birmingham B4 7UP, UK
| | - George Wilson
- School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK
| | | | - Cristina Luca
- School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK
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