[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124567-en":3,"doc-seo-124567-105":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},124567,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine Learning Applications Towards Risk Prediction and Cost Forecasting in Healthcare - Master of Science in Engineering Report","The United States incurs substantial healthcare expenditures, with a significant share driven by surgery and postoperative recovery. This report applies machine learning to estimate the likelihood that an individual undergoing elective spinal surgery will face high-cost recovery. Two complementary models are developed: a time-series approach to forecast future costs across an unknown horizon and a multi-class classifier to produce risk predictions. Model architectures are compared, and the strongest methods are stacked into an ensemble learner.","Copyright by  \nKristian Singh 2022  \ni  \nThe Report Committee for Kristian Singh Certifies that this is the approved version of the following Report  \nMachine Learning Applications Towards Risk Prediction and Cost  \nForecasting in Healthcare  \nAPPROVED BY  \nSUPERVISING COMMITTEE:  \nJohn Hasenbein, Supervisor  \nOmid Nohadani  \nMachine Learning Applications Towards Risk Prediction and Cost  \nForecasting in Healthcare  \nby  \nKristian Singh  \nReport  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nMaster of Science in Engineering  \nThe University of Texas at Austin August 2022  \nDedication  \nTo my parents for their never-ending love and support throughout all my endeavors. Tomy friends for their kindness, humor, and relentless optimism. To my professor for their wisdom and guidance.  \nAbstract  \nMachine Learning Applications Towards Risk Prediction and Cost  \nForecasting in Healthcare  \nKristian Singh, M.S.E  \nThe University of Texas at Austin, 2022  \nSupervisor: John Hasenbein  \nThe United States spends a considerable amount on healthcare and health related expenditures. A sizeable portion of these costs are created by the performing of and recovery from surgery. In this report machine learning methodologies are used to predict the potential risk an individual undergoing elective spinal surgery has for a high-cost recovery. Two models are built in this report, a time-series model to forecast future costs forward over an unknown time horizon, and a multi-class classifier to create the risk predictions. The results of these different model architectures are compared to one another, and the best are then stacked to create a final ensemble learner.  \nTable of Contents  \n1 : Introduction ...................................................................................................................... 1  \n2: Problem Overview ..........................................................................................................2  \nThe Landscape Today .................................................................................................2  \nThe Problem at Hand ..................................................................................................2  \n3: The Data..........................................................................................................................4  \nDefining Claims Data .................................................................................................4  \nThe Data Processing Pipeline .....................................................................................5  \nOur Population ................................................................................................5  \nSurgical Timelines ..........................................................................................5  \nCentering Patient's Claims ..............................................................................7  \nFeature Scaling................................................................................................8  \nFeature Engineering ........................................................................................9  \nFeature Imputation ........................................................................................ 10  \nCreating the Target ....................................................................................... 11  \n4: Exploratory Data Analysis ............................................................................................ 12  \nCorrelations............................................................................................................... 12  \nNon-Linear Trends.................................................................................................... 14  \nGender, Age and Pregnancy .........................................................................14  \nObesity ..............................","cbCait5EhmmtHlZy","https://ap.wps.com/l/cbCait5EhmmtHlZy","pdf",4014731,1,47,"English","en",105,"# Introduction\n# Problem Overview\n## The Landscape Today\n## The Problem at Hand\n# The Data\n## Defining Claims Data\n## The Data Processing Pipeline\n## Our Population\n## Surgical Timelines\n## Centering Patient's Claims\n## Feature Scaling\n## Feature Engineering\n## Feature Imputation\n## Creating the Target\n# Exploratory Data Analysis\n## Correlations\n## Non-Linear Trends\n## Gender, Age and Pregnancy\n## Obesity\n# Risk Prediction\n## Boosted Classifier\n## Hyperparameter Optimization\n## Accuracy & Feature Importance\n## Metrics\n## Shapley Values\n# Time Series Model\n## LSTM Model\n## Overview\n## Data\n## Architecture and Training\n## Convolutional Neural Network\n## Autoregressive Model\n## Results\n# Appendix\n# Bibliography","[{\"question\":\"What problem does the report address in healthcare?\",\"answer\":\"The report targets the high costs associated with surgery and recovery, focusing on predicting the risk of high-cost recovery after elective spinal surgery.\"},{\"question\":\"What machine learning models are built in the report?\",\"answer\":\"It builds a time-series model to forecast future costs over an unknown time horizon and a multi-class classifier to generate risk predictions.\"},{\"question\":\"How are the different models combined to improve performance?\",\"answer\":\"The results from different model architectures are compared, and the best-performing ones are stacked to create a final ensemble learner.\"}]","Machine Learning Applications Towards Risk Prediction and Cost Forecasting in Healthcare - 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