Death O'Clock in Finland
looorg writes:
Deep Learning Death Prediction Accurate
https://www.nature.com/articles/s43587-024-00657-5
Deep learning and population data from the entire population of Finland creates accurate model predicting death with one year.
Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either chronological age or longer-term mortality. Aging clocks predicting short-term mortality are lacking and their algorithmic fairness remains unexamined. We developed a deep learning model to predict 1-year mortality using nationwide longitudinal data from the Finnish population (FinRegistry; n=5.4 million), incorporating more than 8,000 features spanning up to 50 years. We achieved an area under the curve (AUC) of 0.944, outperforming a baseline model that included only age and sex (AUC=0.897). The model generalized well to different causes of death (AUC>0.800 for 45 of 50 causes), including coronavirus disease 2019, which was absent in the training data. Performance varied among demographics, with young females exhibiting the best and older males the worst results. Extensive prediction fairness analyses highlighted disparities among disadvantaged groups, posing challenges to equitable integration into public health interventions. Our model accurately identified short-term mortality risk, potentially serving as a population-wide aging marker.
Our study aimed to accurately predict 1-year mortality for every Finnish resident by using comprehensive, nationwide, multi-category information and to evaluate how prediction accuracy varies within different groups defined according to health, geographical location and socioeconomic characteristics. To achieve this objective, we developed a state-of-the-art DL model.
Should be interesting to see if it translates to other countries. Could or should be comparable to other nordic countries.
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