A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors

• URL: http://arxiv.org/abs/2304.02780v2
• Date: Fri, 1 Mar 2024 05:59:29 GMT
• Title: A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors
• Authors: Can Li, Xiaoqian Jiang, Kai Zhang
• Abstract summary: Liver transplantation is a life-saving procedure for patients with end-stage liver disease. Current scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. Post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., are common complications after transplant.
• Score: 19.00784227522497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, we proposed a deep-learning model to predict multiple risk factors after a liver transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained to simultaneously predict the five post-transplant risks and achieve equal good performance by exploiting task-balancing techniques. We also proposed a novel fairness-achieving algorithm to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The model's performance was evaluated using various performance metrics such as AUROC and AUPRC. Our experiment results highlighted the success of our multitask model in achieving task balance while maintaining accuracy. The model significantly reduced the task discrepancy by 39%. Further application of the fairness-achieving algorithm substantially reduced fairness disparity among all sensitive attributes (gender, age group, and race/ethnicity) in each risk factor.

Related papers

• Predicting Long-Term Allograft Survival in Liver Transplant Recipients [11.680219281917076]
  Liver allograft failure occurs in approximately 20% of liver transplant recipients within five years post-transplant. We introduce the Model for Allograft Survival (MAS), a simple linear risk score that outperforms other advanced survival models.
  arXiv  Detail & Related papers  (2024-08-10T04:44:36Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Penalties and Rewards for Fair Learning in Paired Kidney Exchange Programs [4.963350442999301]
  A kidney exchange program, also called a kidney paired donation program, can be viewed as a repeated, dynamic trading and allocation mechanism. We confirm this hypothesis using a full scale simulation of the Canadian Kidney Paired Donation Program. We find that the most critical factor in determining the performance of a kidney exchange program is not the judicious assignment of positive weights (rewards) to patient-donor pairs.
  arXiv  Detail & Related papers  (2023-09-23T16:25:49Z)
• Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant [65.85767739748901]
  Post-transplant cause of death provides powerful tool for clinical decision making. Traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis. We propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly.
  arXiv  Detail & Related papers  (2023-03-30T01:31:49Z)
• Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning [61.30094367351618]
  Liver transplant is an essential therapy performed for severe liver diseases. Machine learning models could be unfair and trigger bias against certain groups of people. This work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
  arXiv  Detail & Related papers  (2023-02-18T18:24:58Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• Learning $\mathbf{\mathit{Matching}}$ Representations for Individualized Organ Transplantation Allocation [98.43063331640538]
  We formulate the problem of learning data-driven rules for organ matching using observational data for organ allocations and transplant outcomes. We propose a model based on representation learning to predict donor-recipient compatibility. Our model outperforms state-of-art allocation methods and policies executed by human experts.
  arXiv  Detail & Related papers  (2021-01-28T01:33:21Z)
• On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models [0.0]
  The Surgical Outcome Risk Tool (SORT) is one of the tools developed to predict mortality risk throughout the entire period for major elective in-patient surgeries in the UK. In this study, we enhance the original SORT prediction model (SORT) by addressing the class imbalance within the dataset. Our proposed method investigates the application of diversity-based selection on top of common re-sampling techniques.
  arXiv  Detail & Related papers  (2020-12-15T09:45:35Z)
• A predictive model for kidney transplant graft survival using machine learning [1.514049362441354]
  A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated. The random forest predicted significantly more successful and longer-surviving transplants than the risk index.
  arXiv  Detail & Related papers  (2020-12-07T15:29:51Z)
• Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan [49.209225484926634]
  We propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification. Our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.
  arXiv  Detail & Related papers  (2020-05-07T12:16:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.