Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records

Table 4 Evaluation of the quality of the predictions

Assessor	Accuracy	Overly pessimistic	Overly optimistic
Human EMR data + patient consultation	20%	17%	63%
Baseline model structured data features	20%	68%	12%
Keyword model structured data features + word2vec-based features	29%	52%	19%

Predictions were considered accurate if they deviate less than 33% from the actual life expectancy. The human results were adopted from [15]. Note: the doctors in [15] estimated life expectancy for a different group of patients than our models do in this the current research

ISSN: 1472-6947