Analysis data set of incidence characteristics of complications in hospitalized patients with psoriasis in China

This data set comes from a research project on the comorbidity burden of psoriasis patients, which was carried out by the air force characteristic medical center from 2022 to 2024. This study aims to systematically analyze the epidemiological characteristics and genetic association mechanism of complications in patients with psoriasis, and provide data support for clinical chronic disease management. 1.2 research purposeClinical analysis: quantify the comorbidity burden of patients with psoriasis and identify the high incidence of complicationsMechanism exploration: To explore the genetic basis shared by psoriasis and complicationsManagement support: provide evidence-based basis for formulating individualized chronic disease management plan 2、 Data file structure2.1 document list and naming rulesRaw data file:1\s01-s87-cases.xlsx - comorbidity frequency record2\comorbidity diagnosis.xlsx - comorbidity diagnosis name record3\ethics and origin patients.xlsx – ethnic and regional characteristics of patients4\data\u 6999patients\u ising.xlsx – record of comorbidities5\disgenet v25.1 (April 20, 2025 – May 10, 2025).Xlsx – the gene names of psoriasis and its comorbidities retrieved from the disgene database. Data processing file:Geographic distribution of hospitalised patients with psoriasis.r- visual drawing scriptIsing comorbidity network of psoriasis and eczema.r- visual drawing scriptIsing model (strength and between).R statistical analysis main processLogistic regression models and forest plots.r statistical analysis mainstreamR codes-Stability analysis of two ising network models (1993-2014and2015-2024).R Main process of statistical analysisR codes-Stability analysis of two ising network models (based on the cases vs the inpatients).R Main process of statistical analysisShap and auc.r- visual drawing scriptSix machine learning algorithms screening variables.r main process of statistical analysisValidation and evaluation of logistic regression models.r Result output file:Data - summarize raw data filesCode - summary data processing file 2.2 description of file naming rules<TEXT>[data type]_[content description] [Extension]