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

Table 3 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	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%

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

ISSN: 1472-6947