AIMC Topic: Waiting Lists

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Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

Computer methods and programs in biomedicine
BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the wait...

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.

Scientific reports
A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, ...

[Prioritized appointment allocation in new patients, what is really decisive? : Comparative analysis of manual appointment allocation with automated and AI-assisted approaches].

Zeitschrift fur Rheumatologie
BACKGROUND: The timely allocation of appointments for new patients is a daily challenge in rheumatological practice, which can be supported by digital solutions. The question is to find the simplest and most effective possible method for prioritizati...

Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.

Health care management science
This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acu...

Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.

Health care management science
Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time....

A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals.

BMC medical informatics and decision making
BACKGROUND: This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured...

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Health care management science
Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be ...

Deep Learning-Based Survival Analysis for Receiving a Steatotic Donor Liver Versus Waiting for a Standard Liver.

Transplantation proceedings
BACKGROUND: An emerging strategy to expand the donor pool is the use of a steatotic donor liver (SDLs; ≥ 30% macrosteatosis on biopsy). With the obesity epidemic and prevalence of nonalcoholic fatty liver disease, SDLs have been reported in 59% of al...

Ranking patients on the kidney transplant waiting list based on fuzzy inference system.

BMC nephrology
BACKGROUND: Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed ...

Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Clinical decision-making in kidney transplant (KT) during the coronavirus disease 2019 (COVID-19) pandemic is understandably a conundrum: both candidates and recipients may face increased acquisition risks and case fatality rates (CFRs). Given our po...