当前位置： SCI文献检索 > Applied Clinical Informatics期刊下所有文献 > A Review of Predictive Analytics Solutions for Sepsis Patients.

A Review of Predictive Analytics Solutions for Sepsis Patients.

Abstract：

BACKGROUND:Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis. OBJECTIVE:To understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality. METHODS AND RESULTS:We performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression (n = 2), logistic regression (n = 5), support vector machines (n = 4), and Markov models (n = 4), as well as population (range: 24-198,833) and feature size (range: 2-285). Many of the studies used local data sets of varying sizes and locations while others used the publicly available Medical Information Mart for Intensive Care data. Additionally, vital signs or laboratory test results were commonly used as features for training and testing purposes; however, a few used more unique features including gene expression data from blood plasma and unstructured text and data from clinician notes. CONCLUSION:Overall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.

journal_name

Appl Clin Inform

journal_title

Applied clinical informatics

authors

Teng AK,Wilcox AB

doi

10.1055/s-0040-1710525

subject

Has Abstract

pub_date

2020-05-01 00:00:00

pages

387-398

issue

issn

1869-0327

journal_volume

pub_type

杂志文章

在线工具

A Review of Predictive Analytics Solutions for Sepsis Patients.