AIMC Topic: Survival Analysis
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Survival analysis using machine learning in transplantation: a practical introduction.
BMC medical informatics and decision making
Mar 21, 2025
BACKGROUND: Survival analysis is a critical tool in transplantation studies. The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making...
Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer.
Scientific reports
Mar 19, 2025
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers known to humans. However, not all patients fare equally poor survival, and a minority of patients even survives advanced disease for months or years. Thus, there is a clinical ...
Factor enhanced DeepSurv: A deep learning approach for predicting survival probabilities in cirrhosis data.
Computers in biology and medicine
Mar 3, 2025
BACKGROUND: Over the years, various models, including both traditional and machine learning models, have been employed to predict survival probabilities for diverse survival datasets. The objective is to obtain models that provide more accurate estim...
Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.
Scientific reports
Feb 18, 2025
Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective study at Emtiaz Hospital analyzed 3,180 traumatic brain injury (TBI) patients. Nineteen variables were...
A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records.
BMC medical informatics and decision making
Feb 17, 2025
BACKGROUND: The clinical information housed within unstructured electronic health records (EHRs) has the potential to promote cancer research. The National Cancer Center Hospital (NCCH) is widely recognized as a leading institution for the treatment ...
Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.
Journal of translational medicine
Feb 16, 2025
BACKGROUND: Predicting long-term outcomes in liver transplantation remain a challenging endeavor. This research aims to harness the power of deep learning to develop an advanced prognostic model for assessing long-term outcomes, with a specific focus...
Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.
Computer methods and programs in biomedicine
Feb 15, 2025
BACKGROUND AND OBJECTIVE: Scalable, flexible and highly interpretable tools for predicting mortality in residential aged care facilities for the purpose of informing and optimizing palliative care decisions, do not exist. This study is the first and ...
Cross-modal alignment and contrastive learning for enhanced cancer survival prediction.
Computer methods and programs in biomedicine
Feb 7, 2025
BACKGROUND AND OBJECTIVE: Integrating multimodal data, such as pathology images and genomics, is crucial for understanding cancer heterogeneity, personalized treatment complexity, and enhancing survival prediction. However, most current prognostic me...
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis.
IEEE transactions on medical imaging
Feb 4, 2025
Recently, we have witnessed impressive achievements in cancer survival analysis by integrating multimodal data, e.g., pathology images and genomic profiles. However, the heterogeneity and high dimensionality of these modalities pose significant chall...
Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.
Scientific reports
Jan 29, 2025
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasi...
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