Table 1 Framing structures in related studies.

From: The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards

Paper

Population

Target

Framing structure (1)

Alignment

Van Wyk, 201814

Intensive care unit

Sepsis

Sliding window

Right

Scherpf, 20198

Intensive care unit

Sepsis

Sequential problem

Right

Moor, 20199

Intensive care unit

Sepsis

Sequential problem

Right

Futoma I, 201713

Mixed ward

Sepsis

Sequential problem (modified)

Right

Futoma II, 201716

Mixed ward

Sepsis

Fixed time to onset + matching

Right

Lauritsen I, 202019

Mixed ward

Sepsis

Sequential problem

Right

Lauritsen II, 20206

Mixed ward

Sepsis

Fixed time to onset

Right

Nemati, 201815

Intensive care unit

Sepsis

Sliding window

Right

Delahanty, 201920

Emergency department

Sepsis

On clinical demand (modified)

Right

Khojandi, 201817

In hospital

Sepsis

Fixed time to onset, fixed time

Left & right

Khoshnevisan, 201818

In hospital

Septic shock

Fixed time to onset (modified)

Left & right

Thiel, 201921

In hospital

Septic shock

Fixed time to onset (modified)

Right

Van Wyk I, 201822

Intensive care unit

Sepsis

Sliding window

Right

Wang, 201823

Intensive care unit

Sepsis

Fixed time to onset

Right

Kam, 201724

Intensive care unit

Sepsis

Sliding window

Right

Moss, 201625

Intensive care unit

Severe sepsis

Sliding window

Right

Guillen, 201526

Intensive care unit

Severe sepsis

Fixed time to onset

Right

Mao, 201727

Mixed ward

Sepsis, severe sepsis, septic shock

Fixed time to onset

Right

Barton, 20197

Mixed ward

Sepsis

Fixed time to onset

Right

  1. (1) Details on framing structures are given in “framing of the prediction model” section and in the supplementary information.