Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients
Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients’ quality of life, which can vary over time and across individuals. The application of artificial intelligence and machin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/12/297 |
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| author | Maryem Rhanoui Mounia Mikram Kamelia Amazian Abderrahim Ait-Abderrahim Siham Yousfi Imane Toughrai |
| author_facet | Maryem Rhanoui Mounia Mikram Kamelia Amazian Abderrahim Ait-Abderrahim Siham Yousfi Imane Toughrai |
| author_sort | Maryem Rhanoui |
| collection | DOAJ |
| description | Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients’ quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications. |
| format | Article |
| id | doaj-art-bc3b95150f764cb9b9debd853f25f121 |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-bc3b95150f764cb9b9debd853f25f1212025-08-20T02:53:30ZengMDPI AGJournal of Imaging2313-433X2024-11-01101229710.3390/jimaging10120297Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer PatientsMaryem Rhanoui0Mounia Mikram1Kamelia Amazian2Abderrahim Ait-Abderrahim3Siham Yousfi4Imane Toughrai5Laboratory Health Systemic Process (P2S), UR4129, University Claude Bernard Lyon 1, University of Lyon, 69008 Lyon, FranceMeridian Team, LyRICA Laboratory, School of Information Sciences, Rabat 10100, MoroccoHigher Institute of Nursing Professions and Health Technology, Fez 30050, MoroccoGeneral Surgery Department, Hassan II University Hospital, Fez 30050, MoroccoMeridian Team, LyRICA Laboratory, School of Information Sciences, Rabat 10100, MoroccoGeneral Surgery Department, Hassan II University Hospital, Fez 30050, MoroccoColorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients’ quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications.https://www.mdpi.com/2313-433X/10/12/297multimodal learningmachine learningquality of life (QoL)colorectal cancer (CRC)healthcare analytics |
| spellingShingle | Maryem Rhanoui Mounia Mikram Kamelia Amazian Abderrahim Ait-Abderrahim Siham Yousfi Imane Toughrai Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients Journal of Imaging multimodal learning machine learning quality of life (QoL) colorectal cancer (CRC) healthcare analytics |
| title | Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients |
| title_full | Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients |
| title_fullStr | Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients |
| title_full_unstemmed | Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients |
| title_short | Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients |
| title_sort | multimodal machine learning for predicting post surgery quality of life in colorectal cancer patients |
| topic | multimodal learning machine learning quality of life (QoL) colorectal cancer (CRC) healthcare analytics |
| url | https://www.mdpi.com/2313-433X/10/12/297 |
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