Development and Validation of an Intelligent Risk Prediction Model for Lung Diseases Based on the NLST Cohort
Our research team intends to leverage advanced machine learning and deep learning techniques to create a robust and accurate model capable of predicting the risk of lung diseases, including lung cancer, among high-risk populations. The model will incorporate various predictive factors such as demographic characteristics, clinical parameters, and imaging findings. Utilizing a training dataset from NLST, we will explore multiple algorithms, such as logistic regression, random forests, and neural networks, etc, to identify the most suitable model architecture. The model will be developed to predict the risk of various lung diseases, with a primary focus on lung cancer.
Aim 1: Development of a Predictive Model for Lung Disease Risk
Objective: To develop a machine learning-based predictive model for lung disease risk, with a focus on lung cancer.
Approach: We will explore and compare different machine learning algorithms, such as logistic regression, random forests, and neural networks, to identify the most effective model. The model will be trained on a subset of the NLST data, utilizing a wide range of predictors including demographic data, smoking history, and LDCT imaging results.
Aim 2: Validation of the Predictive Model
Objective: To validate the developed predictive model using an independent subset of the NLST dataset.
Approach: The model’s performance will be evaluated using key metrics such as AUC-ROC, sensitivity, specificity, and calibration. We will ensure the model's robustness through cross-validation and will conduct subgroup analyses to assess its generalizability across different patient populations.
Aim 3: Clinical Utility and Comparison with Existing Tools
Objective: To assess the clinical utility of the predictive model in comparison to existing lung disease risk prediction tools.
Approach: We will evaluate the model's potential impact on clinical decision-making by comparing its predictive accuracy and practical utility against currently used models.
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