Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital
In this research, we address the urgent need for accurate prediction of in-hospital survival periods for patients diagnosed with pancreatic cancer (PC), a disease notorious for its late-stage diagnosis and dismal survival rates. Utilizing machine learning (ML) technologies, we focus on the applicati...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10379091/ |
| _version_ | 1797361463434674176 |
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| author | Yuan Wang Chenbi Li Zeheng Wang |
| author_facet | Yuan Wang Chenbi Li Zeheng Wang |
| author_sort | Yuan Wang |
| collection | DOAJ |
| description | In this research, we address the urgent need for accurate prediction of in-hospital survival periods for patients diagnosed with pancreatic cancer (PC), a disease notorious for its late-stage diagnosis and dismal survival rates. Utilizing machine learning (ML) technologies, we focus on the application of Variational Autoencoders (VAE) for data augmentation and ensemble learning techniques for enhancing predictive accuracy. Our dataset comprises biochemical blood test (BBT) results from stage II/III PC patients, which is limited in size, making VAE’s capability for data augmentation particularly valuable. The study employs several ML models, including Elastic Net (EN), Decision Trees (DT), and Radial Basis Function Support Vector Machine (RBF-SVM), and evaluates their performance using metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE). Our findings reveal that EN, DT, and RBF-SVM are the most effective models within a VAE-augmented framework, showing substantial improvements in predictive accuracy. An ensemble learning approach further optimized the results, reducing the MAE to approximately 10 days. These advancements hold significant implications for the field of precision medicine, enabling more targeted therapeutic interventions and optimizing healthcare resource allocation. The study can also serve as a foundational step towards more personalized and effective healthcare solutions for PC patients. |
| first_indexed | 2024-03-08T15:55:03Z |
| format | Article |
| id | doaj.art-dfd9e55b1a3f4a858aa4e6f1a1ad7647 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T15:55:03Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dfd9e55b1a3f4a858aa4e6f1a1ad76472024-01-09T00:04:16ZengIEEEIEEE Access2169-35362024-01-01123428343610.1109/ACCESS.2023.334881010379091Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local HospitalYuan Wang0Chenbi Li1Zeheng Wang2https://orcid.org/0000-0002-6994-1234Department of Oncology, 3201 Hospital, Hanzhong, ChinaDepartment of ICU, 3201 Hospital, Hanzhong, ChinaData61, CSIRO, Clayton, VIC, AustraliaIn this research, we address the urgent need for accurate prediction of in-hospital survival periods for patients diagnosed with pancreatic cancer (PC), a disease notorious for its late-stage diagnosis and dismal survival rates. Utilizing machine learning (ML) technologies, we focus on the application of Variational Autoencoders (VAE) for data augmentation and ensemble learning techniques for enhancing predictive accuracy. Our dataset comprises biochemical blood test (BBT) results from stage II/III PC patients, which is limited in size, making VAE’s capability for data augmentation particularly valuable. The study employs several ML models, including Elastic Net (EN), Decision Trees (DT), and Radial Basis Function Support Vector Machine (RBF-SVM), and evaluates their performance using metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE). Our findings reveal that EN, DT, and RBF-SVM are the most effective models within a VAE-augmented framework, showing substantial improvements in predictive accuracy. An ensemble learning approach further optimized the results, reducing the MAE to approximately 10 days. These advancements hold significant implications for the field of precision medicine, enabling more targeted therapeutic interventions and optimizing healthcare resource allocation. The study can also serve as a foundational step towards more personalized and effective healthcare solutions for PC patients.https://ieeexplore.ieee.org/document/10379091/Pancreatic cancermachine learningbioinformaticssmall-scale datavariational auto-encoder |
| spellingShingle | Yuan Wang Chenbi Li Zeheng Wang Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital IEEE Access Pancreatic cancer machine learning bioinformatics small-scale data variational auto-encoder |
| title | Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital |
| title_full | Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital |
| title_fullStr | Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital |
| title_full_unstemmed | Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital |
| title_short | Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital |
| title_sort | advancing precision medicine vae enhanced predictions of pancreatic cancer patient survival in local hospital |
| topic | Pancreatic cancer machine learning bioinformatics small-scale data variational auto-encoder |
| url | https://ieeexplore.ieee.org/document/10379091/ |
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