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Table 1 Number and proportion of papers according to the aim of study and number of patients analysed

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

 

Number of patients analysed

Aim of study

Number (%) of papers with this aima

< 100

100–1000

1000–10,000

10,000–100,000

100,000–1,000,000

Number not reported

Predicting complications

79 (30.6%)

23 (29.1%)

26 (32.9%)

17 (21.5%)

8 (10.1%)

3 (3.8%)

2 (2.5%)

Predicting mortality

70 (27.1%)

11 (15.7%)

19 (27.1%)

19 (27.1%)

18 (25.7%)

1 (1.4%)

2 (2.9%)

Improving prognostic models/risk scoring system

43 (16.7%)

8 (18.6%)

16 (37.2%)

8 (18.6%)

8 (18.6%)

2 (4.7%)

1 (2.3%)

Classifying sub-populations

29 (11.2%)

11 (37.9%)

8 (27.6%)

6 (20.7%)

2 (6.9%)

0 (0.0%)

2 (6.9%)

Alarm reduction

21 (8.14%)

9 (42.9%)

5 (23.8%)

7 (33.3%)

0 (0.0%)

0 (0.0%)

0 (0.0%)

Predicting length of stay

18 (6.98%)

3 (16.7%)

7 (38.9%)

5 (27.8%)

3 (16.7%)

0 (0.0%)

0 (0.0%)

Predicting health improvement

17 (6.59%)

5 (29.4%)

10 (58.8%)

2 (11.8%)

0 (0.0%)

0 (0.0%)

0 (0.0%)

Determining physiological thresholds

16 (6.20%)

10 (62.5%)

4 (25.0%)

1 (6.2%)

0 (0.0%)

0 (0.0%)

1 (6.2%)

Improving upon previous methods

5 (1.94%)

2 (40.0%)

1 (20.0%)

1 (20.0%)

1 (20.0%)

0 (0.0%)

0 (0.0%)

Detecting spurious recorded values

3 (1.16%)

1 (33.3%)

2 (66.7%)

0 (0.0%)

0 (0.0%)

0 (0.0%)

0 (0.0%)

Total (accounting for duplicates)

258

72 (27.9%)

84 (32.6%)

55 (21.3%)

35 (13.6%)

6 (2.33%)

6 (2.33%)

  1. aWhere papers had more than one aim, all aims were recorded, so percentages may total more than 100