Cohort profile: the Nanjing Diabetes Cohort database - a population-based surveillance cohort.

Purpose: To study epidemiology, complications, risk factors, clinical course and treatment patterns of diabetes, the Nanjing Diabetes Cohort (NDC) was established using anonymised electronic health records from 650 hospitals and primary care since 2020. This cohort provides valuable data for researchers and policy-makers focused on diabetes management and public health strategies.

Participants: Diabetes was defined as having inpatient or outpatient encounters with a diagnosis of diabetes International Classification of Diseases-9/10 codes, any use of insulin or oral hypoglycaemic drugs, or one encounter with haemoglobin A1C >4.

Characterizing Brain Tumor Regions Using Texture Analysis in Magnetic Resonance Imaging.

Purpose: To extract texture features from magnetic resonance imaging (MRI) scans of patients with brain tumors and use them to train a classification model for supporting an early diagnosis.

Methods: Two groups of regions (control and tumor) were selected from MRI scans of 40 patients with meningioma or glioma. These regions were analyzed to obtain texture features.

A predictive model for respiratory distress in patients with COVID-19: a retrospective study.

Background: Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis. We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19.

Methods: We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission.

Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system.

Automated electrocardiogram (ECG) diagnosis could be a useful aid for clinical use. We applied a deep learning method to build a system for automated detection and classification of ECG signals. We first trained a convolutional neural network (CNN) to detect cardiovascular disease in ECG signals using a training data set of 259,789 ECG signals collected from the cardiac function rooms of a tertiary care hospital.

Analysis of treatment pathways for three chronic diseases using OMOP CDM.

The present study examined treatment pathways (the ordered sequence of medications that a patient is prescribed) for three chronic diseases (hypertension, type 2 diabetes, and depression), compared the pathways with recommendations from guidelines, discussed differences and standardization of medications in different medical institutions, explored population diversification and changes of clinical treatment, and provided clinical big data analysis-based data support for the development and study of drugs in China. In order to run the "Treatment Pathways in Chronic Disease" protocol in Chinese data sources,we have built a large data research and analysis platform for Chinese clinical medical data. Data sourced from the Clinical Data Repository (CDR) of the First Affiliated Hospital of Nanjing Medical University was extracted, transformed, and loaded into an observational medical outcomes partnership common data model (OMOP CDM) Ver.