AIMC Topic: Survival Analysis

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External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Preoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG ML 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease were pre...

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.

Cardiovascular and interventional radiology
INTRODUCTION: To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool...

A Cancer Survival Prediction Method Based on Graph Convolutional Network.

IEEE transactions on nanobioscience
BACKGROUND AND OBJECTIVE: Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and health. The high mortality of cancer is largely due to the complexity of cancer and the significant...

Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
INTRODUCTION: To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for individual patients with adenocarcinoma of the esophagus and esophagogastric junction-information not a...

Documentation of Palliative and End-of-Life Care Process Measures Among Young Adults Who Died of Cancer: A Natural Language Processing Approach.

Journal of adolescent and young adult oncology
Few studies have investigated palliative and end-of-life care processes among young adults (YAs), aged 18-34 years, who died of cancer. This retrospective study used a natural language processing algorithm to identify documentation and timing of four...

Perioperative mortality and morbidity of outpatient versus inpatient robot-assisted radical prostatectomy: A propensity matched analysis.

Urologic oncology
OBJECTIVES: To compare the early (≤30 days) postoperative mortality and morbidity in patients who underwent robot-assisted radical prostatectomy (RARP) and were discharged the same surgery day to a propensity score matched patient population of RARP ...

Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks.

IEEE journal of biomedical and health informatics
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability t...

Block Forests: random forests for blocks of clinical and omics covariate data.

BMC bioinformatics
BACKGROUND: In the last years more and more multi-omics data are becoming available, that is, data featuring measurements of several types of omics data for each patient. Using multi-omics data as covariate data in outcome prediction is both promisin...

A deep survival analysis method based on ranking.

Artificial intelligence in medicine
Survival analyses of populations and the establishment of prognoses for individual patients are important activities in the practice of medicine. Standard survival models, such as the Cox proportional hazards model, require extensive feature engineer...