Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

被引：24

作者：

Mlodzinski, Eric ^{[1
]}

Stone, David J. ^{[2
,3
,4
]}

Celi, Leo A. ^{[2
,5
]}

机构：

[1] Beth Israel Deaconess Med Ctr, Dept Internal Med, Boston, MA 02215 USA

[2] MIT, Harvard MIT Hlth Sci & Technol, Lab Comp Physiol, MIT Crit Data, Cambridge, MA 02139 USA

[3] Univ Virginia, Dept Anesthesiol & Neurosurg, Sch Med, Charlottesville, VA 22908 USA

[4] Univ Virginia, Sch Med, Ctr Adv Med Anal, Charlottesville, VA 22908 USA

[5] Beth Israel Deaconess Med Ctr, Div Pulm Crit Care & Sleep Med, Boston, MA 02215 USA

来源：

PULMONARY THERAPY | 2020年 / 6卷 / 01期

关键词：

Artificial intelligence; Chronic obstructive pulmonary disease; Computed tomography; Critical care; Machine learning; Mechanical ventilation; Neural networks; Pulmonary; Sepsis; PREDICTION; SEPSIS; MORTALITY;

D O I：

10.1007/s41030-020-00110-z

中图分类号：

R56 [呼吸系及胸部疾病];

学科分类号：

摘要：

Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.

引用

页码：67 / 77

页数：11

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