AIMC Topic: Tissue and Organ Procurement

Clear Filters Showing 11 to 20 of 27 articles

A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: We propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in ...

A machine learning approach for the prediction of overall deceased donor organ yield.

Surgery
BACKGROUND: Optimizing organ yield (number of organs transplanted per donor) is a potentially modifiable way to increase the number of organs available for transplant. Models to predict the expected deceased donor organ yield have been developed base...

Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation.

PloS one
Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a...

Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method.

BMC medical informatics and decision making
BACKGROUND: Kidney allocation is a multi-criteria and complex decision-making problem, which should also consider ethical issues in addition to the medical aspects. Leading countries in this field use a point scoring system to allocate kidneys. Hence...

Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database.

Journal of cardiac failure
BACKGROUND: Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensiona...

An efficient method for kidney allocation problem: a credibility-based fuzzy common weights data envelopment analysis approach.

Health care management science
Given the perennial imbalance and chronic scarcity between the demand for and supply of available organs, organ allocation is one of the most critical decisions in the management of organ transplantation networks. Organ allocation systems undergo rap...

Robotic-assisted kidney transplantation: our first case.

World journal of urology
PURPOSE: Kidney transplantation is the preferred treatment for patients with end-stage renal disease. In order to reduce the morbidity of the open surgery, a robotic-assisted approach has been recently introduced. According to the published literatur...

Accept/decline decision module for the liver simulated allocation model.

Health care management science
Simulated allocation models (SAMs) are used to evaluate organ allocation policies. An important component of SAMs is a module that decides whether each potential recipient will accept an offered organ. The objective of this study was to develop and t...

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.

BMJ open
OBJECTIVES: In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques t...