AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Survival Analysis

Showing 11 to 20 of 303 articles

Clear Filters

CoxFNN: Interpretable machine learning method for survival analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Survival analysis plays a pivotal role in healthcare, particularly in analyzing time-to-event data such as in disease progression, treatment efficacy, and drug development. Traditional methods in survival analysis often face a trade-off: they either ...

Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.

Scientific reports
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...

Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.

Computer methods and programs in biomedicine
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 ...

Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.

Scientific reports
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
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 ...

Cross-modal alignment and contrastive learning for enhanced cancer survival prediction.

Computer methods and programs in biomedicine
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...

Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.

Journal of translational medicine
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...

Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer.

Scientific reports
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
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...

Deep Gated Neural Network With Self-Attention Mechanism for Survival Analysis.

IEEE journal of biomedical and health informatics
Survival analysis is commonly used to model the time distributions of the first occurrences of events of interest, and it has widespread medical applications. Many previous studies learned the relationship between risk and covariates by making strong...