[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121419-en":3,"doc-seo-121419-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121419,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms","The opioid pandemic has driven substantial global harm, and prescription opioids are a major contributor to overdoses. Understanding why opioids are prescribed—especially for chronic conditions—requires analyzing interacting patient, lifestyle, and disease factors. This study investigates overprescribing among US nephrologists using unsupervised machine learning on Medicare Part D prescriber summary data. Clustering reveals gender differences and higher prescribing rates in specific states such as California. K-means and Gaussian mixture models produce consistent outcomes, supporting future deep-learning work.","Emerging Trends in Drugs, Addictions, and Health 5 (2025) 100165  \nContents lists available at ScienceDirect  \nEmerging Trends in Drugs, Addictions, and Health  \njournal [homepage: www.elsevier.com/locate/etdah](homepage: www.elsevier.com/locate/etdah)  \nExploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms  \nShivashankar Basapura Chandrashekarappaa,b, Sulaf Assic, Manoj Jayabalanb, Abdullah Al-Hamid d, Dhiya Al-Jumeily b,*  \na Oracle India Pvt. Ltd. Bangalore, India  \nb Computer Sciences and Mathematics, Liverpool John Moores University, Liverpool, UK c Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK  \nd Department of Pharmacy Practice, College of Clinical Pharmacy, King Faisal University, Al-Ahsa, Saudi Arabia  \nA R T I C L E I N F O  \nEdited by: Dr. Vicky Balasingam Kasinather  \nKeywords: Opioids Nephrologists Epidemic  \nMachine learning algorithms K-mean clustering Gaussian mixture models  \nA B S T R A C T  \nBackground 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.  \nDesign: Two types of unsupervised clustering were applied to the Medicare Provider Utilisation and Payment Data Part-D Prescriber Summary.  \nSetting: 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.  \nFindings: 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 indepth patterns in prescription and contributing factors related to over-prescribing.  \n1. Introduction  \nThe opioid pandemic has contributed to major morbidities and mortalities at global levels. In 2017, 40,600 out of 70,237 deaths aids due to opioid overdose (Hedegaard et al., 2021). The situation did not ease up after 2017 where the CDC reported 645,000 deaths linked to opioid overdose between 1999 and 2021 (CDC, 2021).  \nIn addition to non-prescription opioids, prescription opioids contribute to the risk of the opioid pandemic. Previous studies have identified that opioid prescriptions are among the major causes or opioid overdose (Guy et al., 2017; Nataraj et al.2019). Prescription opioids have been identified as a major cause for opioid abuse in the US  \n(Mallappallil, 2017). Hence, in 2017 35 % of opioid overdose death were linked to prescription opioids (Scholl et al., 2019). However, these guidelines did not prevent physicians from overprescribing who were still not sure how to act in chronic pain management (McCann-Pineoet al., 2021).  \nIn chronic diseases, e.g. kidney disease, opioids are prescribed for pain management; however, they can be toxic due their metabolites’accumulation in the kidney (Richards et al., 2018). Moreover, the increased prescription of opioids has contributed gr","cbCaiewJN3unY3hz","https://ap.wps.com/l/cbCaiewJN3unY3hz","pdf",5463214,1,7,"English","en",105,"# Abstract\n## Background and aims\n## Design\n## Setting\n## Findings and conclusions\n# Introduction","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses overprescribing of opioids among nephrologists in the United States and the broader opioid crisis linked to overdose deaths.\"},{\"question\":\"Which data and methods are used for analysis?\",\"answer\":\"It uses Medicare Provider Utilisation and Payment Data Part D Prescriber Summary with 50,134 records and applies univariate/bivariate analysis followed by unsupervised clustering using K-means and Gaussian Mixture Models.\"},{\"question\":\"What key findings emerge from the clustering results?\",\"answer\":\"Male patients receive opioids at about three times the rate of females, male nephrologists are higher prescribers than female nephrologists, and California shows the highest prescription rates. Both clustering methods yield the same overall outcomes.\"}]","Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms | PDF",1785735585,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"exploring-high-opioid-prescriptions-among-nephrologists-in-the-united-states-using-machine-learning-algorithms","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploring-high-opioid-prescriptions-among-nephrologists-in-the-united-states-using-machine-learning-algorithms/121419/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address?","Question",{"text":76,"@type":77},"The study addresses overprescribing of opioids among nephrologists in the United States and the broader opioid crisis linked to overdose deaths.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data and methods are used for analysis?",{"text":81,"@type":77},"It uses Medicare Provider Utilisation and Payment Data Part D Prescriber Summary with 50,134 records and applies univariate/bivariate analysis followed by unsupervised clustering using K-means and Gaussian Mixture Models.",{"name":83,"@type":74,"acceptedAnswer":84},"What key findings emerge from the clustering results?",{"text":85,"@type":77},"Male patients receive opioids at about three times the rate of females, male nephrologists are higher prescribers than female nephrologists, and California shows the highest prescription rates. 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