Peri-operative complications of sphenopalatine artery ligation: a 10-year series from two secondary care centres.

Background: There is currently limited evidence regarding the potential complications of sphenopalatine artery ligation. The post-operative outcomes at two secondary care centres over a 10-year period were reviewed.

Methods: A retrospective review was undertaken of patients undergoing emergency and elective sphenopalatine artery ligation between January 2011 and January 2021.

Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults.

Background: Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real time.

Correction to: A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling.

[This corrects the article DOI: 10.1007/s41666-018-0040-y.].

Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study.

This paper presents a novel method for hierarchical analysis of machine learning algorithms to improve predictions of at risk patients, thus further enabling prompt therapy. Specifically, we develop a multi-layer machine learning approach to analyze continuous, high-frequency data. We illustrate the capabilities of this approach for early identification of patients at risk of sepsis, a potentially life-threatening complication of an infection, using high-frequency (minute-by-minute) physiological data collected from bedside monitors.

A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier.

Purpose: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.

Methods: A data-set consisting of high-frequency physiological data from 1161 critically ill patients was analyzed.