lifeprediction

Life prediction, or lifeprediction, is the practice of estimating an individual's remaining lifespan or a population's life expectancy using data-driven methods. It sits at the intersection of actuarial science, epidemiology, biostatistics, and data science. Proponents build models that assign risk of death or disabling illness over time, using inputs such as age, sex, medical history, genetics, biomarkers, and lifestyle factors.

Common methods include traditional survival analysis, with Cox proportional hazards models and Kaplan–Meier estimates, as well as parametric survival models. More recently, machine learning and AI approaches such as gradient boosting, random forests, and survival-aware neural networks have been applied to large health datasets. Data sources range from longitudinal cohort studies and biobanks to electronic health records and wearable sensor data.

Biomarkers frequently used in lifeprediction include clinical risk scores, epigenetic clocks, telomere length, and measures of organ function. The aim is to estimate metrics such as expected remaining years of life, or probability of surviving to a future age, often with confidence intervals.

Applications span personal and public domains: personalized risk assessment for preventive care, retirement and pension planning, insurance underwriting (subject to ethical and legal constraints), and public health forecasting. Limitations include statistical uncertainty, potential biases, and limited transferability across populations. Privacy, consent, and fairness considerations are central, given the sensitive nature of prognostic information.

The field is evolving with richer data streams and advances in explainable AI, while debates continue about the appropriate use and implications of lifeprediction in society.