Skip to main content

Table 2 Number and proportion of papers according to the type of machine learning used and number of patients analysed (for prediction studies only)

From: Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

 

Number of patients analysed

Type of machine learning

Number (%) of papers with this typea

< 100

100–1000

1000–10,000

10,000–100,000

100,000–1,000,000

Number not reported

Neural network

72 (42.6%)

14 (19.4%)

27 (37.5%)

20 (27.8%)

9 (12.5%)

2 (2.8%)

0 (0.0%)

Support vector machine

40 (23.7%)

12 (30.0%)

15 (37.5%)

8 (20.0%)

4 (10.0%)

1 (2.5%)

0 (0.0%)

Classification/decision trees

35 (20.7%)

6 (17.1%)

11 (31.4%)

10 (28.6%)

5 (14.3%)

1 (2.9%)

2 (5.7%)

Random forest

21 (12.4%)

1 (4.8%)

9 (42.9%)

5 (23.8%)

4 (19.0%)

2 (9.5%)

0 (0.0%)

Naive Bayes/Bayesian networks

19 (11.2%)

4 (21.1%)

5 (26.3%)

6 (31.6%)

2 (10.5%)

1 (5.3%)

1 (5.3%)

Fuzzy logic/rough set

12 (7.1%)

3 (25.0%)

5 (41.7%)

2 (16.7%)

1 (8.3%)

0 (0.0%)

1 (8.3%)

Other techniquesb

28 (16.7%)

2 (7.1%)

10 (35.7%)

8 (28.6%)

7 (25.0%)

1 (3.6%)

0 (0.0%)

Total (accounting for duplicates)

169

37 (21.9%)

56 (33.1%)

42 (24.9%)

26 (15.4%)

4 (2.37%)

4 (2.37%)

  1. aPapers can have more than one approach—percentages may total more than 100
  2. bOther techniques (number of studies): causal phenotype discovery (1), elastic net (1), factor analysis (1), Gaussian process (2), genetic algorithm (1), hidden Markov models (1), InSight (4); JITL-ELM (1), k-nearest neighbour (3), Markov decision process (1), particle swarm optimization (1), PhysiScore (1), radial domain folding (1), sequential contrast patterns (1), Superlearner (4), switching linear dynamical system (1), Weibull-Cox proportional hazards model (1), method not described (2)