Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.

ABSTRACT:

Objective

To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.

Materials and methods

PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted.

Results

The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is

SUBMITTER: Yan MY 

PROVIDER: S-EPMC8800516 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature