AI-Driven Prediction of Biological Age With EHR

Predicting Biological Age Using Electronic Health Records: An AI-Based Approach

This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.

Biological age prediction is crucial for assessing overall health, determining the risk of age-related diseases, and providing personalized healthcare. While chronological age is a key factor, it does not always reflect an individual's true biological aging process. Early identification of accelerated biological aging and associated health risks can significantly impact early interventions and long-term health outcomes. In clinical practice, healthcare providers integrate a wide range of patient data, including medical history, laboratory test results, and clinical observations, to understand an individual's health status and predict potential future risks. As precision medicine becomes more important, the ability to predict biological age and personalize care plans is essential. Recent advancements in artificial intelligence and data analysis techniques have shown promise in enhancing the accuracy of biological age predictions. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, laboratory results, clinical observations, and patient demographics. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized healthcare for patients by predicting biological age, identifying at-risk individuals, and improving health outcomes.

Inclusion Criteria:

1. Patients with comprehensive and accessible EHR data, including medical history, laboratory results, treatment data, imaging data (if available), and lifestyle factors (e.g., smoking, physical activity, diet).
2. Patients with no significant cognitive impairments that would prevent them from providing informed consent or participating in the study.
3. All participants must provide informed consent for the use of their medical data for research purposes.

Exclusion Criteria:

1. Patients with incomplete or missing critical EHR data such as medical history, laboratory results, or treatment data that are necessary for predicting biological age.
2. atients with severe cognitive disorders (e.g., dementia, significant mental disabilities) who are unable to provide informed consent or participate meaningfully in the study.
3. Patients with terminal illnesses or those with limited life expectancy where biological age predictions may not be relevant for the purposes of the study.