Bayesian robust symmetric regression for medical data with heavy-tailed errors and censoring

Dataset Descriptions: 1.Hospital Stay and Clinical Features This dataset contains patient information related to hospital stays and associated clinical features. The data is structured to support exploratory analysis and predictive modeling for healthcare outcomes, particularly the duration of hospital stays. It includes various demographic, clinical, and laboratory variables collected at the time of hospital admission. Key Features: Age: Patient’s age in years Gender: Categorical variable indicating the sex of the patient (Male/Female) Diagnosis: Primary diagnosis upon admission Comorbidities: Presence of chronic conditions such as diabetes, hypertension, etc. Admission Type: Emergency, elective, or other Length of Stay: Duration of hospitalization in days (target variable for regression) Lab Results: Includes WBC, RBC, Hemoglobin, Platelet counts, etc. Outcome: Patient outcome (e.g., discharged, deceased) Purpose:This dataset is intended for use in statistical modeling and machine learning applications focused on predicting hospital stay duration, understanding contributing factors, and potentially optimizing resource allocation in healthcare settings. File Format: Excel spreadsheet with separate columns for each variable. Missing values may be present and should be handled during preprocessing. 2. Lung Cancer Survival Dataset from R 'survival' Package Description: This dataset contains clinical and survival information for 228 patients with advanced lung cancer. It is a standard dataset included in the 'survival' R package. Variables include survival time, censoring status, age, sex, ECOG performance status, and other clinical features. Original Source: R 'survival' package (https://cran.r-project.org/package=survival)License: Public domain / CC0Prepared for use in Bayesian robust regression applications.