[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122075-en":3,"doc-seo-122075-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},122075,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Predicting 24-hour intraocular pressure peaks and averages with machine learning","Predicting 24-hour peak and average intraocular pressure (IOP) is crucial for diagnosing and managing glaucoma, where IOP varies substantially across the day and many peaks occur outside clinic hours. This retrospective study developed and evaluated machine learning models using electronic medical records from January 2014 to May 2024, incorporating 24-hour IOP monitoring data and patient characteristics. Five algorithms and multiple time-point combinations were tested, with feature importance interpreted using Shapley Additive Explanations.","OPEN ACCESS  \nEDITED BY  \nFlora Hui,  \nCentre for Eye Research Australia, Australia  \nREVIEWED BY  \nXianlong Zeng,  \nOhio University, United States Pan Sai,  \nChinese PLA General Hospital, China  \n*CORRESPONDENCE  \nYanlong Bi  \n [biyanlong@tongji.edu.cn](biyanlong@tongji.edu.cn)[ ](biyanlong@tongji.edu.cn)Haohao Zhu  \n [zhuhaohao@fudan.edu.cn](zhuhaohao@fudan.edu.cn)[ ](zhuhaohao@fudan.edu.cn)RECEIVED 04 July 2024 ACCEPTED 19 September 2024 PUBLISHED 07 October 2024  \nCITATION  \nChen R, Lei J, Liao Y, Jin Y, Wang X, Li X, Wu D, Li H, Bi Y and Zhu H (2024) Predicting 24-hour intraocular pressure peaks and averages with machine learning.  \nFront. Med. 11:1459629.  \ndoi: 10.3389/fmed.2024.1459629  \nCOPYRIGHT  \n© 2024 Chen, Lei, Liao, Jin, Wang, Li, Wu, Li, Bi and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 07 October 2024 DOI 10.3389/fmed.2024.1459629  \nPredicting 24-hour intraocular pressure peaks and averages with machine learning  \nRanran Chen 1, Jinming Lei 2, Yujie Liao 1, Yiping Jin 1, Xue Wang3, Xiaomei Li 1, Danping Wu 1, Hong Li 1, Yanlong Bi4* and Haohao Zhu 1*  \n1 Department of Ophthalmology, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China,  \n2Software Engineering, Shenzhen Yishi Huolala Technology Company Limited, Shenzhen, China,  \n3 Department of Ophthalmology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China, 4 Department of Ophthalmology, Tongji Eye Institute, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China  \nPurpose: Predicting 24-hour peak and average intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma. This study aimed to develop and assess a machine learning model for predicting 24-hour peak and average IOP, leveraging advanced techniques to enhance prediction accuracy. We also aimed to identify relevant features and provide insights into the prediction results to better inform clinical practice.  \nMethods: In this retrospective study, electronic medical records from January 2014 to May 2024 were analyzed, incorporating 24-hour IOP monitoring data and patient characteristics. Predictive models based on five machine learning algorithms were trained and evaluated. Five time points (10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM) were tested to optimize prediction accuracy using their combinations. The model with the highest performance was selected, and feature importance was assessed using Shapley Additive Explanations.  \nResults: This study included data from 517 patients (1,034 eyes) . For predicting 24-hour peak IOP, the Random Forest Regression (RFR) model utilizing IOP values at 10:00 AM, 12:00 PM, 2:00 PM, and 4:00 PM achieved optimal performance: MSE 5. 248, RMSE 2. 291, MAE 1.694, and R2 0.823. For predicting 24-hour average IOP, the RFR model using IOP values at 10:00 AM, 12:00 PM, 4:00 PM, and 6:00 PM performed best: MSE 1.374, RMSE 1. 172, MAE 0. 869, and R2 0.918.  \nConclusion: The study developed machine learning models that predict 24-hour peak and average IOP. Specific time point combinations and the RFR algorithm were identified, which improved the accuracy of predicting 24-hour peak and average intraocular pressure. These findings provide the potential for more effective management and treatment strategies for glaucoma patients.  \nKEYWORDS  \nintraocular pressure, 24-hour, measurement, nocturnal, machine learning, glaucoma  \nFrontiers in Medicine 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nGlaucoma, a leading cause of global blindness ( ","cbCailBlNHiGT9DO","https://ap.wps.com/l/cbCailBlNHiGT9DO","pdf",1789844,1,13,"English","en",105,"# Introduction\n## Methods\n## Results\n## Conclusion\n## Keywords","[{\"question\":\"What problem does the study address in glaucoma care?\",\"answer\":\"The study targets prediction of 24-hour IOP peak and average values, which are important for diagnosis and management of glaucoma where IOP fluctuates over the day.\"},{\"question\":\"How were the machine learning models built and evaluated?\",\"answer\":\"Models were trained and tested in a retrospective dataset using 24-hour IOP monitoring data and patient characteristics, comparing five machine learning algorithms and multiple combinations of five time points.\"},{\"question\":\"Which model performed best for predicting peak and average IOP?\",\"answer\":\"For 24-hour peak IOP, the Random Forest Regression model using IOP at 10:00 AM, 12:00 PM, 2:00 PM, and 4:00 PM performed best. For 24-hour average IOP, the best result came from the Random Forest Regression model using IOP at 10:00 AM, 12:00 PM, 4:00 PM, and 6:00 PM.\"}]","Predicting 24-hour intraocular pressure peaks and averages with machine learning | PDF",1785808700,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-24-hour-intraocular-pressure-peaks-and-averages-with-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-24-hour-intraocular-pressure-peaks-and-averages-with-machine-learning/122075/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in glaucoma care?","Question",{"text":75,"@type":76},"The study targets prediction of 24-hour IOP peak and average values, which are important for diagnosis and management of glaucoma where IOP fluctuates over the day.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models built and evaluated?",{"text":80,"@type":76},"Models were trained and tested in a retrospective dataset using 24-hour IOP monitoring data and patient characteristics, comparing five machine learning algorithms and multiple combinations of five time points.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best for predicting peak and average IOP?",{"text":84,"@type":76},"For 24-hour peak IOP, the Random Forest Regression model using IOP at 10:00 AM, 12:00 PM, 2:00 PM, and 4:00 PM performed best. 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