AIMC Topic: Survival Analysis

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Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data.

Scientific reports
Cervical cancer (CC) is a major cause of mortality in women, with stagnant survival rates, highlighting the need for improved prognostic models. This study aims to develop and compare machine learning models for predicting five-year cause-specific su...

Comparison of AI chatbot predicted and realworld survival outcomes in hepatocellular carcinoma.

Scientific reports
This study compares survival predictions made by an artificial intelligence (AI) based chatbot with real-world data in hepatocellular carcinoma (HCC) patients. It aims to evaluate the reliability and accuracy of AI technologies in HCC prognosis. A re...

Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.

Scientific reports
Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing...

The Role of PANoptosis-Related Genes in Predicting Breast Cancer Survival and Immune Prospect.

BioMed research international
The function of PANoptosis in breast cancer (BC) remains indistinct. We constructed a nomogram model to predict the prognosis of BC to identify high-risk patients and help them receive more accurate treatment. We used Cox regression and least absol...

Using interpretable survival analysis to assess hospital length of stay.

BMC health services research
Accurate in-hospital length of stay prediction is a vital quality metric for hospital leaders and health policy decision-makers. It assists with decision-making and informs hospital operations involving factors such as patient flow, elective cases, a...

Personalized therapeutic strategies and prognosis for advanced laryngeal squamous cell carcinoma: Insights from machine learning models.

American journal of otolaryngology
PURPOSE: Despite the development of diverse treatment options, there has been an increase in mortality rates for laryngeal squamous cell carcinoma (LSCC). Our research employed survival analysis and machine learning (ML) techniques to evaluate the im...

Data-driven survival modeling for breast cancer prognostics: A comparative study with machine learning and traditional survival modeling methods.

PloS one
Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML...

Hierarchical embedding attention for overall survival prediction in lung cancer from unstructured EHRs.

BMC medical informatics and decision making
The automated processing of Electronic Health Records (EHRs) poses a significant challenge due to their unstructured nature, rich in valuable, yet disorganized information. Natural Language Processing (NLP), particularly Named Entity Recognition (NER...

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...

Multimodal multi-instance evidence fusion neural networks for cancer survival prediction.

Scientific reports
Accurate cancer survival prediction plays a crucial role in assisting clinicians in formulating treatment plans. Multimodal data, such as histopathological images, genomic data, and clinical information, provide complementary and comprehensive inform...