Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network
Cloud computing plays a vital role in healthcare as it can store a large amount of data known as big data. In the current emerging era of computing technology, big data analysis and prediction is a challenging task in the healthcare industry. Healthcare data are very crucial for the patient as well...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/4/2391 |
| _version_ | 1797622543521153024 |
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| author | Abdullah Alourani Kinza Tariq Muhammad Tahir Muhammad Sardaraz |
| author_facet | Abdullah Alourani Kinza Tariq Muhammad Tahir Muhammad Sardaraz |
| author_sort | Abdullah Alourani |
| collection | DOAJ |
| description | Cloud computing plays a vital role in healthcare as it can store a large amount of data known as big data. In the current emerging era of computing technology, big data analysis and prediction is a challenging task in the healthcare industry. Healthcare data are very crucial for the patient as well as for the respective healthcare services provider. Several healthcare industries adopted cloud computing for data storage and analysis. Incredible progress has been achieved in making combined health records available to data scientists and clinicians for healthcare research. However, big data in health cloud informatics demand more robust and scalable solutions to accurately analyze it. The increasing number of patients is putting high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. Predicting mortality among patients with a variety of symptoms and complications is difficult, resulting inaccurate and slow prediction of the disease. This article presents a deep learning based model for the prediction of patient mortality using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Different parameters are used to analyze the proposed model, i.e., accuracy, F1 score, recall, precision, and execution time. The results obtained are compared with state-of-the-art models to test and validate the proposed model. Moreover, this research suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate. |
| first_indexed | 2024-03-11T09:11:47Z |
| format | Article |
| id | doaj.art-31b548f8ee6440ed911852e535703e4b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:11:47Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-31b548f8ee6440ed911852e535703e4b2023-11-16T18:55:05ZengMDPI AGApplied Sciences2076-34172023-02-01134239110.3390/app13042391Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural NetworkAbdullah Alourani0Kinza Tariq1Muhammad Tahir2Muhammad Sardaraz3Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, PakistanCloud computing plays a vital role in healthcare as it can store a large amount of data known as big data. In the current emerging era of computing technology, big data analysis and prediction is a challenging task in the healthcare industry. Healthcare data are very crucial for the patient as well as for the respective healthcare services provider. Several healthcare industries adopted cloud computing for data storage and analysis. Incredible progress has been achieved in making combined health records available to data scientists and clinicians for healthcare research. However, big data in health cloud informatics demand more robust and scalable solutions to accurately analyze it. The increasing number of patients is putting high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. Predicting mortality among patients with a variety of symptoms and complications is difficult, resulting inaccurate and slow prediction of the disease. This article presents a deep learning based model for the prediction of patient mortality using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Different parameters are used to analyze the proposed model, i.e., accuracy, F1 score, recall, precision, and execution time. The results obtained are compared with state-of-the-art models to test and validate the proposed model. Moreover, this research suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.https://www.mdpi.com/2076-3417/13/4/2391machine learningdeep neural networkmortality predictionaccuracye-healthcloud computing |
| spellingShingle | Abdullah Alourani Kinza Tariq Muhammad Tahir Muhammad Sardaraz Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network Applied Sciences machine learning deep neural network mortality prediction accuracy e-health cloud computing |
| title | Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network |
| title_full | Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network |
| title_fullStr | Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network |
| title_full_unstemmed | Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network |
| title_short | Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network |
| title_sort | patient mortality prediction and analysis of health cloud data using a deep neural network |
| topic | machine learning deep neural network mortality prediction accuracy e-health cloud computing |
| url | https://www.mdpi.com/2076-3417/13/4/2391 |
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