[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122108-en":3,"doc-seo-122108-105":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},122108,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","The Use of Cloud Based Machine Learning To Predict Outcome in Intracerebral Haemorrhage Without Explicit Programming Expertise - Research","Machine learning models typically require both clinical domain knowledge and new programming skills to produce clinically useful prediction tools. This study evaluates a cloud-based ML workflow that eliminates programming expertise, enabling clinicians to develop, validate, and deploy a prognostic model for intracerebral haemorrhage. Patient data from a hospital stroke registry (2015–2019) was split for training, validation, and testing, then compared using ML and logistic regression across outcome definitions and performance metrics.","The Texas Medical Center Library  \nDigitalCommons@TMC  \n\n| Faculty and Staff Publications | Baylor College of Medicine |\n| --- | --- |\n| 12-3-2024\u003Cbr>The Use of Cloud Based Machine Learning To Predict Outcome in Intracerebral Haemorrhage Without Explicit Programming Expertise\u003Cbr>Ajay Hegde Deepu Vijaysenan Pitchaiah Mandava Girish Menon\u003Cbr>Follow this and additional works at: [https://digitalcommons.library.tmc.edu/baylor_docs](https://digitalcommons.library.tmc.edu/baylor_docs)\u003Cbr> Part of the Medical Sciences Commons, Mental and Social Health Commons, and the Neurology Commons |  |\n\nRecommended Citation  \nHegde, Ajay; Vijaysenan, Deepu; Mandava, Pitchaiah; and Menon, Girish, \"The Use of Cloud Based Machine Learning To Predict Outcome in Intracerebral Haemorrhage Without Explicit Programming Expertise\" (2024) . Faculty and Staff Publications. 2452.  \n[https://digitalcommons.library.tmc.edu/baylor_docs/2452](https://digitalcommons.library.tmc.edu/baylor_docs/2452)  \nThis Article is brought to you for free and open access by the Baylor College of Medicine at  \nDigitalCommons@TMC. It has been accepted for inclusion in Faculty and Staff Publications by an authorized administrator of DigitalCommons@TMC. For more information, please contact [digcommons@library.tmc.edu](digcommons@library.tmc.edu).  \nNeurosurgical Review (2024) 47:883  \n[https://doi.org/10.1007/s10143-024-03115-3](https://doi.org/10.1007/s10143-024-03115-3)  \nRESEARCH  \nThe use of cloud based machine learning to predict outcome in intracerebral haemorrhage without explicit programming expertise  \nAjay Hegde1,2 · Deepu Vijaysenan3 · Pitchaiah Mandava4 · Girish Menon5  \nReceived: 18 January 2024 / Revised: 6 October 2024 / Accepted: 14 November 2024 © The Author(s) 2024  \nAbstract  \nMachine Learning (ML) techniques require novel computer programming skills along with clinical domain knowledge to produce a useful model. We demonstrate the use of a cloud-based ML tool that does not require any programming expertise to develop, validate and deploy a prognostic model for Intracerebral Haemorrhage (ICH) . The data of patients admitted with Spontaneous Intracerebral haemorrhage from January 2015 to December 2019 was accessed from our prospectively maintained hospital stroke registry. 80% of the dataset was used for training, 10% for validation, and 10% for testing. Seventeen input variables were used to predict the dichotomized outcomes (Good outcome mRS 0–3/ Bad outcome mRS 4–6), using machine learning (ML) and logistic regression (LR) models. The two different approaches were evaluated using Area Under the Curve (AUC) for Receiver Operating Characteristic (ROC), Precision recall and accuracy. Our data set comprised of a cohort of 1000 patients. The data was split 8:1 for training & testing respectively. The AUC ROC of the ML model was 0.86 with an accuracy of 75.7% . With LR AUC ROC was 0.74 with an accuracy of 73.8% . Feature importance chart showed that Glasgow coma score (GCS) at presentation had the highest relative importance, followed by hematoma volume and age in both approaches. Machine learning models perform better when compared to logistic regression. Models can be developed by clinicians possessing domain expertise and no programming experience using cloud based tools. The models so developed lend themselves to be incorporated into clinical workflow.  \nKeywords Intracerebral haemorrhage · Outcome · Machine learning · Google AutoML  \n􀀍 Girish Menon[girish.menon@manipal.edu](girish.menon@manipal.edu)  \nAjay Hegde  \n[dr.ajayhegde@gmail.com](dr.ajayhegde@gmail.com)  \nDeepu Vijaysenan  \n[deepu.senan@gmail.com](deepu.senan@gmail.com)  \nPitchaiah Mandava  \n[pmandava@bcm.edu](pmandava@bcm.edu)  \n1 Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, 576104 Manipal, India  \n2 Neurosurgery, Manipal Hospitals, Bangalore, India  \n3 Department of Electronics and Communication Engineering, National Institute of Technology, Surathkal, Karnataka, India","cbCaimpdPYDrNbIP","https://ap.wps.com/l/cbCaimpdPYDrNbIP","pdf",1762455,1,10,"English","en",105,"# Abstract\n## Introduction\n## Methods and Data Overview\n## Model Development and Evaluation\n## Results and Clinical Implications","[{\"question\":\"How does the study address the need for programming skills in machine learning?\",\"answer\":\"It uses a cloud-based machine learning tool intended to develop, validate, and deploy a prognostic model without requiring programming expertise.\"},{\"question\":\"What dataset and split strategy were used for training, validation, and testing?\",\"answer\":\"Patient data with spontaneous intracerebral haemorrhage from January 2015 to December 2019 was accessed from a prospectively maintained hospital stroke registry, then split into training, validation, and testing subsets.\"},{\"question\":\"How did the machine learning model perform compared with logistic regression?\",\"answer\":\"The study reports higher performance for the machine learning approach, with greater AUC and accuracy than logistic regression, using metrics such as AUC for ROC, precision-recall, and accuracy.\"}]","The Use of Cloud Based Machine Learning To Predict Outcome in Intracerebral Haemorrhage Without Explicit Programming Expertise - Research | PDF",1785808861,25,{"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},"the-use-of-cloud-based-machine-learning-to-predict-outcome-in-intracerebral-haemorrhage-without-explicit-programming-expertise-research","",{"@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/the-use-of-cloud-based-machine-learning-to-predict-outcome-in-intracerebral-haemorrhage-without-explicit-programming-expertise-research/122108/",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},"How does the study address the need for programming skills in machine learning?","Question",{"text":75,"@type":76},"It uses a cloud-based machine learning tool intended to develop, validate, and deploy a prognostic model without requiring programming expertise.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and split strategy were used for training, validation, and testing?",{"text":80,"@type":76},"Patient data with spontaneous intracerebral haemorrhage from January 2015 to December 2019 was accessed from a prospectively maintained hospital stroke registry, then split into training, validation, and testing subsets.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the machine learning model perform compared with logistic regression?",{"text":84,"@type":76},"The study reports higher performance for the machine learning approach, with greater AUC and accuracy than logistic regression, using metrics such as AUC for ROC, precision-recall, and accuracy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]