Comparison of correctly and incorrectly classified patients for in-hospital mortality prediction in the intensive care unit

被引：0
作者：
Eline Stenwig
Giampiero Salvi
Pierluigi Salvo Rossi
Nils Kristian Skjærvold
机构：
[1] Department of Circulation and Medical Imaging,
[2] The Norwegian University of Science and Technology,undefined
[3] Department of Electronic Systems,undefined
[4] The Norwegian University of Science and Technology,undefined
[5] KTH,undefined
[6] Royal Institute of Technology,undefined
[7] EECS,undefined
[8] Clinic of Anaesthesia and Intensive Care Medicine,undefined
[9] St. Olav’s University Hospital,undefined
来源：
BMC Medical Research Methodology | / 23卷
关键词：
Machine learning; Explainability; Mortality prediction; eICU; SHAP values;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 36 条
  • [1] Strand K(2008)Severity scoring in the ICU: a review Acta Anaesthesiol Scand. 52 467-478
  • [2] Flaatten H(2020)Bridging the implementation gap of machine learning in healthcare BMJ Innov. 6 45-47
  • [3] Seneviratne MG(2020)What we talk about when we talk about trust: Theory of trust for AI in healthcare Intell-Based Med. 1–2 154-161
  • [4] Shah NH(2022)Intentional machines: A defence of trust in medical artificial intelligence Bioethics. 36 1-13
  • [5] Chu L(2018)The eICU collaborative research database, a freely available multi-center database for critical care research Sci Data. 5 1297-1310
  • [6] Gille F(2006)Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients Crit Care Med. 34 32-35
  • [7] Jobin A(2022)COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits PLoS ONE. 17 2825-2830
  • [8] Ienca M(1950)Index for Rating Diagnostic Tests Cancer. 3 90-95
  • [9] Starke G(2011)Scikit-learn: Machine Learning in Python J Mach Learn Res. 12 undefined-undefined
  • [10] Brule R(2007)Matplotlib: A 2D graphics environment Comput Sci Eng. 9 undefined-undefined
1234