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

Table 2 Deviation in months between actual life expectancy and predicted life expectancy for different keyword models

Selection method	Hidden units	Root mean square deviation			Mean deviation
Selection method	Hidden units	100 features	200 features	300 features	100 features	200 features	300 features
Frequency	50	17.6	17.2	17.0	4.5	5.0	5.8
	100	17.5	17.4	16.9	2.1	1.2	1.7
	200	17.7	17.8	17.8	1.6	1.3	1.0
Entropy	50	17.4	17.8	17.8	5.1	5.6	5.4
	100	17.2	16.9	17.8	2.5	2.3	1.6
	200	17.7	17.5	17.7	2.3	2.0	1.3
Word2vec	50	17.8	18.2	18.2	−3.4	−4.3	−3.7
	100	18.1	17.8	17.8	−4.2	−4.1	−4.8
	200	18.3	18.3	18.4	−3.75	−4.4	− 4.4

The models differ from each other in terms of selection method and number of included keywords. The best models are defined by two criteria: 1) having a relatively low root mean square, followed by 2) having a low mean deviation. Note: the first criterion is leading, the second criterion is only used as a tie breaker. For each selection method, the results of the best-performing model are marked with boldface, based on these criteria

ISSN: 1472-6947