Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms
Background and aims: The opioid pandemic has contributed to deaths globally, and prescription opioids have played a crucial role in these deaths. Addressing overdose requires understanding the reasons behind prescription, especially in cases of chronic diseases. Several factors play a role in the in...
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| Language: | English |
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Elsevier
2025-12-01
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| Series: | Emerging Trends in Drugs, Addictions, and Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667118224000242 |
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| author | Shivashankar Basapura Chandrashekarappa Sulaf Assi Manoj Jayabalan Abdullah Al-Hamid Dhiya Al-Jumeily |
| author_facet | Shivashankar Basapura Chandrashekarappa Sulaf Assi Manoj Jayabalan Abdullah Al-Hamid Dhiya Al-Jumeily |
| author_sort | Shivashankar Basapura Chandrashekarappa |
| collection | DOAJ |
| description | Background and aims: The opioid pandemic has contributed to deaths globally, and prescription opioids have played a crucial role in these deaths. Addressing overdose requires understanding the reasons behind prescription, especially in cases of chronic diseases. Several factors play a role in the increased prescription of opioids, relating to patients’ lifestyle, characteristics, and disease. As these factors are complex in nature, understanding them requires machine learning approach. This study explored overprescribing opioids among nephrologists in the US using unsupervised machine learning algorithms. Design: Two types of unsupervised clustering were applied to the Medicare Provider Utilisation and Payment Data Part-D Prescriber Summary. Setting: The dataset had 50,134 records with 85 features relating to opioids prescription per US state. Univariate and bivariate analysis were applied first to gain understanding of the data followed by K-mean clustering and Gaussian Mixture Models. Findings: Unsupervised clustering showed that prescription issued to males were three times higher than those issued to females. Moreover, male nephrologists were higher prescribers than female nephrologists, and a third of male nephrologists were high prescribers of opioids. The highest rates of prescriptions were seen in California. Conclusions: Unsupervised machine learning algorithms enabled understanding of high opioid prescription across gender and US state by analysing multiple features. Both K-mean clustering and Gaussian Mixture Models achieved the same outcomes. Future work will benefit from applying deep learning in order to understand in-depth patterns in prescription and contributing factors related to over-prescribing. |
| format | Article |
| id | doaj-art-e67bb81666a542c6be030d27d99ecab1 |
| institution | DOAJ |
| issn | 2667-1182 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Emerging Trends in Drugs, Addictions, and Health |
| spelling | doaj-art-e67bb81666a542c6be030d27d99ecab12025-08-20T02:50:27ZengElsevierEmerging Trends in Drugs, Addictions, and Health2667-11822025-12-01510016510.1016/j.etdah.2024.100165Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithmsShivashankar Basapura Chandrashekarappa0Sulaf Assi1Manoj Jayabalan2Abdullah Al-Hamid3Dhiya Al-Jumeily4Oracle India Pvt. Ltd. Bangalore, India; Computer Sciences and Mathematics, Liverpool John Moores University, Liverpool, UKPharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UKComputer Sciences and Mathematics, Liverpool John Moores University, Liverpool, UKDepartment of Pharmacy Practice, College of Clinical Pharmacy, King Faisal University, Al-Ahsa, Saudi ArabiaComputer Sciences and Mathematics, Liverpool John Moores University, Liverpool, UK; Corresponding author.Background and aims: The opioid pandemic has contributed to deaths globally, and prescription opioids have played a crucial role in these deaths. Addressing overdose requires understanding the reasons behind prescription, especially in cases of chronic diseases. Several factors play a role in the increased prescription of opioids, relating to patients’ lifestyle, characteristics, and disease. As these factors are complex in nature, understanding them requires machine learning approach. This study explored overprescribing opioids among nephrologists in the US using unsupervised machine learning algorithms. Design: Two types of unsupervised clustering were applied to the Medicare Provider Utilisation and Payment Data Part-D Prescriber Summary. Setting: The dataset had 50,134 records with 85 features relating to opioids prescription per US state. Univariate and bivariate analysis were applied first to gain understanding of the data followed by K-mean clustering and Gaussian Mixture Models. Findings: Unsupervised clustering showed that prescription issued to males were three times higher than those issued to females. Moreover, male nephrologists were higher prescribers than female nephrologists, and a third of male nephrologists were high prescribers of opioids. The highest rates of prescriptions were seen in California. Conclusions: Unsupervised machine learning algorithms enabled understanding of high opioid prescription across gender and US state by analysing multiple features. Both K-mean clustering and Gaussian Mixture Models achieved the same outcomes. Future work will benefit from applying deep learning in order to understand in-depth patterns in prescription and contributing factors related to over-prescribing.http://www.sciencedirect.com/science/article/pii/S2667118224000242OpioidsNephrologistsEpidemicMachine learning algorithmsK-mean clusteringGaussian mixture models |
| spellingShingle | Shivashankar Basapura Chandrashekarappa Sulaf Assi Manoj Jayabalan Abdullah Al-Hamid Dhiya Al-Jumeily Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms Emerging Trends in Drugs, Addictions, and Health Opioids Nephrologists Epidemic Machine learning algorithms K-mean clustering Gaussian mixture models |
| title | Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms |
| title_full | Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms |
| title_fullStr | Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms |
| title_full_unstemmed | Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms |
| title_short | Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms |
| title_sort | exploring high opioid prescriptions among nephrologists in the united states using machine learning algorithms |
| topic | Opioids Nephrologists Epidemic Machine learning algorithms K-mean clustering Gaussian mixture models |
| url | http://www.sciencedirect.com/science/article/pii/S2667118224000242 |
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