Computer, how long have I got left? Predicting life expectancy with a long short-term memory to aid in early identification of the palliative phase

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Life expectancy is a leading indicator when making decisions about end-of-life care, but good prognostication is notoriously challenging. Being overly optimistic about life expectancy, as doctors tend to be, greatly impedes the early identification of palliative patients and thereby delays appropriate care in the final phase of life. This research aimed to explore the feasibility of automatically predicting life expectancy based on electronic medical records, with the aid of machine learning and natural language processing techniques. We trained a neural network (long short-term memory) with 1107 medical records, and validated the model with 127 medical records. Using identical evaluation criteria as were used to evaluate doctors’ performance, our baseline model reached a level of accuracy similar to human accuracy. The inclusion of clinical narrative was enabled and optimized with the use of natural language processing techniques such as domain-specific spelling correction. The inclusion of keyword features improved the prediction accuracy with 9%, compared to both our baseline model and to the golden standard of human evaluation. Overall, we have shown that our approach for automatic prognostication is feasible and delivers promising results.
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