{"id":3132,"date":"2020-08-05T00:00:00","date_gmt":"2020-08-05T04:00:00","guid":{"rendered":"https:\/\/icesontario.wpengine.com\/journal-articles\/predicting-acute-kidney-injury-a-machine-learning-approach-using-electronic-health-records\/"},"modified":"2023-06-14T20:10:05","modified_gmt":"2023-06-15T00:10:05","slug":"predicting-acute-kidney-injury-a-machine-learning-approach-using-electronic-health-records","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/predicting-acute-kidney-injury-a-machine-learning-approach-using-electronic-health-records\/","title":{"rendered":"Predicting acute kidney injury: a machine learning approach using electronic health records"},"content":{"rendered":"<p>Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine learning techniques to identify older patients who have a risk of readmission with AKI to the hospital or emergency department within 90 days after discharge. One million patients&#x2019; records are included in this study who visited the hospital or emergency department in Ontario between 2014 and 2016. The predictor variables include patient demographics, comorbid conditions, medications and diagnosis codes. We developed 31 prediction models based on different combinations of two sampling techniques, three ensemble methods, and eight classifiers. These models were evaluated through 10-fold cross-validation and compared based on the AUROC metric. The performances of these models were consistent, and the AUROC ranged between 0.61 and 0.88 for predicting AKI among 31 prediction models. In general, the performances of ensemble-based methods were higher than the cost-sensitive logistic regression. We also validated features that are most relevant in predicting AKI with a healthcare expert to improve the performance and reliability of the models. This study predicts the risk of AKI for a patient after being discharged, which provides healthcare providers enough time to intervene before the onset of AKI.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine [&hellip;]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[],"migration-helper-qa-sample-set":[],"class_list":["post-3132","journal_article","type-journal_article","status-publish","hentry"],"acf":{"citation":"Abdullah SS, Rostamzadeh N, Sedig K, Garg AX, McArthur E. <em>Information<\/em>. 2020; 11(8):386. Epub 2020 Aug 5.","source_url":"https:\/\/www.mdpi.com\/2078-2489\/11\/8\/386\/htm","ices_scientist":[1242,22472],"site":[6739],"research_program":[6743],"news_release":[],"journal_article":[],"atlas":[],"research_report":[],"infographic":[],"video":[],"downloads":null,"links":null,"sitecore_item_id":"578DB79E-C5AA-40C7-9E36-EBDE2175792C","sitecore_item_name":"Predicting-acute-kidney-injury-a-machine-learning-approach-using-electronic-health-records","sitecore_field_values":"{\n  \"Title\": \"Predicting acute kidney injury: a machine learning approach using electronic health records\",\n  \"Short title\": \"Predicting acute kidney injury\",\n  \"Summary\": \"This study used machine learning techniques to identify older patients who have a risk of readmission with acute kidney injury to the hospital or emergency department within 90 days after discharge.\",\n  \"Citation\": \"<p>Abdullah SS, Rostamzadeh N, Sedig K, Garg AX, McArthur E. <em>Information<\/em>. 2020; 11(8):386. Epub 2020 Aug 5. DOI: <a href=\"https:\/\/doi.org\/10.3390\/info11080386\" title=\"Opens external link\">https:\/\/doi.org\/10.3390\/info11080386<\/a><\/p>\",\n  \"Abstract\": \"<p>Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine learning techniques to identify older patients who have a risk of readmission with AKI to the hospital or emergency department within 90 days after discharge. One million patients&rsquo; records are included in this study who visited the hospital or emergency department in Ontario between 2014 and 2016. The predictor variables include patient demographics, comorbid conditions, medications and diagnosis codes. We developed 31 prediction models based on different combinations of two sampling techniques, three ensemble methods, and eight classifiers. These models were evaluated through 10-fold cross-validation and compared based on the AUROC metric. The performances of these models were consistent, and the AUROC ranged between 0.61 and 0.88 for predicting AKI among 31 prediction models. In general, the performances of ensemble-based methods were higher than the cost-sensitive logistic regression. We also validated features that are most relevant in predicting AKI with a healthcare expert to improve the performance and reliability of the models. This study predicts the risk of AKI for a patient after being discharged, which provides healthcare providers enough time to intervene before the onset of AKI.<\/p>n<p><a href=\"https:\/\/www.mdpi.com\/2078-2489\/11\/8\/386\/htm\" title=\"Opens external link\">View full text<\/a><\/p>\",\n  \"Research Programs\": \"{92CC48EB-79AA-49F6-8A80-16D185170261}\",\n  \"ICES Locations\": \"{3B4AF7E8-6835-410B-AEB8-360A79CA0ED8}\",\n  \"ICES Scientists\": \"{95F0CC45-E886-4729-A10C-DC4813D54710}\",\n  \"Posted Date\": \"20200805T000000\",\n  \"Show on Publications Landing Page\": \"1\"\n}","previous_url":"https:\/\/www.ices.on.ca\/Publications\/Journal-Articles\/2020\/August\/Predicting-acute-kidney-injury-a-machine-learning-approach-using-electronic-health-records"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ICES | Predicting acute kidney injury: a machine learning approach using electronic health 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