Development and validation of an ensemble learning risk model for sepsis after abdominal surgery.

Introduction: Although their importance has gained attention, the clinical applications of methods for screening patients at high risk of sepsis after abdominal surgery have been restricted. Therefore, we aimed to develop and validate models for screening patients at high risk of sepsis after abdominal surgery based on machine learning with routine variables.

Material And Methods: The whole dataset was composed of three representative academic hospitals in China and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging.

Background: Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing.

Effect of lidocaine or dexmedetomidine on postoperative recovery in elder patients undergoing colorectal surgery.

Background: Opioid sparing techniques have been shown to promote gastrointestinal recovery, shorten length of stay (LOS), and reduce opioid-related complications. We investigated whether intraoperative intravenous lidocaine or dexmedetomidine infusion could improve gastrointestinal recovery in elderly patients undergoing laparoscopic colorectal surgery.

Methods: Ninety-six patients aged 65 years or older who underwent elective laparoscopic colorectal resection were randomly allocated into the following three groups: the control group (N.

Two-stage hemoglobin prediction based on prior causality.

Introduction: Perioperative hemoglobin (Hb) levels can influence tissue metabolism. For clinical physicians, precise Hb concentration greatly contributes to intraoperative blood transfusion. The reduction in Hb during an operation weakens blood's oxygen-carrying capacity and poses threats to multiple systems and organs of the whole body.

Optimization of anesthetic decision-making in ERAS using Bayesian network.

Enhanced recovery after surgery (ERAS) can accelerate patient recovery. However, little research has been done on optimizing the ERAS-related measures and how the measures interact with each other. The Bayesian network (BN) is a graphical model that describes the dependencies between variables and is also a model for uncertainty reasoning.