Article

Development of machine learning models for the prediction of complications after colorectal and small intestine surgery in psychiatric and non-psychiatric patient collectives (P-Study)

2022-02-04

Abstract excerpt

<h4>Introduction</h4> Psychiatric and psychosomatic diseases are an increasingly cumbersome burden for the medical system. Indeed, hospital costs associated with mental health conditions have been constantly on the rise in recent years. Moreover, psychiatric conditions are likely to have a negative effect on the treatment of other medical conditions and surgical outcomes, in addition to their direct effects on the...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: b086fafa-4a4d-52b1-a32d-9b00338bc4c4
DOI: 10.1101/2022.02.02.22269622

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Development of machine learning models for the prediction of complications after colorectal and small intestine surgery in psychiatric and non-psychiatric patient collectives (P-Study)DOI 10.1101/2022.02.02.22269622Read

Machine learning-based prediction of postoperative mortality in emergency colorectal surgery: A retrospective, multicenter cohort study using Tokushukai Medical DatabaseSemantic proximity Read

Development and validation of a machine learning-based risk model for prolonged length of stay after laparoscopic gastrointestinal surgery in women: A secondary analysis of the FDP-PONV trailSemantic proximity Read

Machine learning based preoperative analytics for the prediction of anastomotic insufficiency in colorectal surgery: a single-centre pilot studySemantic proximity Read

Construction and Validation of a Machine Learning-Based Prediction Model for Social Isolation in Patients with Colorectal Cancer after Stoma SurgerySemantic proximity Read

Construction and validation of a risk prediction model for postoperative ICU admission in patients with colorectal cancer: Clinical prediction model studySemantic proximity Read

Contextual validation of the prediction of postoperative complications of colorectal surgery by the " ACS NSQIP ® Risk Calculator " (Sousse, Tunisia, 2015-2019)Semantic proximity Read

Predicting High-Risk Colorectal Polyps Using Pre-Colonoscopy Features: Machine Learning Model Development and ValidationSemantic proximity Read

Early Postoperative Complications in Colorectal Cancer Patients Following Colorectal Surgery: A Prospective StudySemantic proximity Read

A Three-stage model for hospital length of Stay prediction using Machine and deep learning AlgorithmsSemantic proximity Read

Clinical Prediction Models: Foundational ConceptsSemantic proximity Read

Heterogeneity and risk prediction study of self-reported outcomes in patients with abdominal wall stoma for bladder cancer based on latent profile analysis and machine learning algorithmsSemantic proximity Read

Development and Validation of a Nomogram Combined with Albumin and Neutrophil-to-Lymphocyte Ratio Predicting Postoperative Complications after Resection of Colon CancerSemantic proximity Read