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Table 3 Evaluation of the quality of the predictions

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

Assessor Accuracy Overly pessimistic Overly optimistic
Human EMR data + patient consultation 20% 17% 63%
Baseline model structured data features 23% 58% 20%
Frequency model structured data features + frequency-based features (keywords) 29% 27% 44%
Entropy model structured data features + entropy-based features (keywords) 28% 46% 27%
Word2vec model structured data features + word2vec-based features (vector space dimensions) 38% 32% 31%
  1. Predictions were considered accurate if they deviate less than 33% from the actual life expectancy. 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