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

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

Selection method Hidden units Root mean square deviation Mean deviation
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
  1. 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