[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125668-en":3,"doc-seo-125668-105":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},125668,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",7,"Healthcare","A Machine Learning Approach for Early Diagnosis of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients","Transthyretin Amyloid Cardiomyopathy (ATTR-CM) is a rare, progressive, fatal condition frequently affected by underdiagnosis and misdiagnosis. Diagnosis delay shows a weighted mean of 6.1 years in wild-type ATTR-CM, driven by low awareness, reliance on invasive procedures, and limited treatment options. Non-invasive nuclear scintigraphy with 99mTC-PYP and Tafamidis shift diagnosis timing toward earlier intervention, improving life expectancy. Patient records from North American organizations using the EHR system “TrixNetX” support statistical analyses and machine learning models to identify key phenotypes and predict ATTR-CM early in heart failure patients.","Graduate Theses, Dissertations, and Problem Reports  \n2023  \nA Machine Learning Approach for Early Diagnosis of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients  \nTanjim Ahmed  \nWest Virginia University, [ta00024@mix.wvu.edu](ta00024@mix.wvu.edu)  \nFollow this and additional works at: [https://researchrepository.wvu.edu/etd](https://researchrepository.wvu.edu/etd)  \n Part of the Cardiovascular Diseases Commons, Disease Modeling Commons, and the Industrial Engineering Commons  \nRecommended Citation  \nAhmed, Tanjim, \"A Machine Learning Approach for Early Diagnosis of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients\" (2023) . Graduate Theses, Dissertations, and Problem Reports. 12028.  \n[https://researchrepository.wvu.edu/etd/12028](https://researchrepository.wvu.edu/etd/12028)  \nThis Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s) . You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [researchrepository@mail.wvu.edu](researchrepository@mail.wvu.edu).  \nA Machine Learning Approach for Early Diagnosis of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients  \nTanjim Ahmed  \nThesis submitted to the  \nCollege of Engineering and Mineral Resources at West Virginia University  \nin partial fulfillment of the requirements for the degree of Master of Science  \nin Industrial Engineering  \nImtiaz Ahmed, Ph.D., Chair  \nAbdullah Al-Mamun, Ph.D.  \nAvishek Choudhury, Ph.D.  \nDepartment of Industrial and Management Systems Engineering  \nMorgantown, West Virginia  \n2023  \nKeywords: ATTR-CM, Diagnosis Delay, Machine Learning, Statistical Analysis.  \nCopyright 2023 Tanjim Ahmed  \nABSTRACT  \nA Machine Learning Approach for Early Diagnosis of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients  \nTanjim Ahmed  \nTransthyretin Amyloid Cardiomyopathy (ATTR-CM) is a rare, progressive, and fatal disease. Prevalence of ATTR-CM ranges from 4 to 17 per 100000 cases where the mean survival time is less than 4 years. It has a history of being underdiagnosed and misdiagnosed. The diagnosis delay has a weighted mean of 6.1 years for wild-type ATTR-CM. Low awareness, the necessity of invasive procedures, and lack of treatment are the key reasons for delayed diagnosis. But, with the introduction of non-invasive tests like nuclear scintigraphy with 99mTC-PYP and the disease modifying drug Tafamidis, the diagnosis delay signifies a missed opportunity to increase life expectancy by early treatment. Studies show that mean life expectancy can be increased by 5.46 years by early treatment if the 6.1 years of diagnosis delay can be eliminated, whereas the current mean survival time is less than 4 years. Though there is no definitive symptom for it, studies have found out some key prognostic flags: symptoms and comorbidities that are co-existent with ATTRCM. A prediction model can be developed using the electronic health records (EHR) information in hand to diagnose it early and aid to increase the mean life expectancy. This study aims to identify the top phenotypes that can be used for early diagnosis of ATTR-CM and to predict ATTR-CM using machine learning models among heart failure patients. Patient records from North American healthcare organizations were derived from an EHR system‘TrixNetX’ for this study. Several statistical analyses (e.g., logistic regression, forward and backward elimination, LASSO, and Survival ana","cbCaivYpIq1vqrzB","https://ap.wps.com/l/cbCaivYpIq1vqrzB","pdf",1832617,1,62,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n## Research Works on Prevalence of ATTR-CM\n## Research Works on Diagnosis Delay for ATTR-CM\n## Research Works on Machine Learning Based Approaches in ATTR-CM Study\n# Data Description\n# Methodology\n## Logistic Regression","[{\"question\":\"What problem does the study address for ATTR-CM patients?\",\"answer\":\"The study focuses on delayed and inaccurate diagnosis of Transthyretin Amyloid Cardiomyopathy, including underdiagnosis and misdiagnosis in clinical practice.\"},{\"question\":\"What data source is used to build and evaluate the prediction model?\",\"answer\":\"Patient records from North American healthcare organizations are derived from the EHR system “TrixNetX” for statistical analysis and model training.\"},{\"question\":\"Which machine learning approaches are used to predict ATTR-CM early?\",\"answer\":\"The study trains models such as XGBoost and Random Forest using key diagnostic factors identified through statistical analyses.\"}]","A Machine Learning Approach for Early Diagnosis of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients | 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