Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant. 1 Introduction Organ transplant is a crucial therapeutic option for individuals with end-stage diseases, e.g., kidney failure [1], liver failure [2], liver cancer [3], etc.

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse features by combining the advantages of tree models and neural networks. A two-step debiasing method is tailored for this framework to enhance fairness. Experiments are conducted to analyze unfairness issues in existing models and demonstrate the superiority of our method in both prediction and fairness performance.

According to ARRS' American Journal of Roentgenology (AJR), machine learning models applied to presently underutilized imaging features could help construct more reliable criteria for organ allocation and liver transplant eligibility. "The findings suggest that machine learning-based models can predict recurrence before therapy allocation in patients with early-stage hepatocellular carcinoma (HCC) initially eligible for liver transplant," wrote corresponding author Julius Chapiro from the department of radiology and biomedical imaging at Yale University School of Medicine in New Haven, CT. Chapiro and colleagues' proof-of-concept study included 120 patients (88 men, 32 women; median age, 60 years) diagnosed with early-stage HCC between June 2005 and March 2018, who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation. Patients underwent pretreatment MRI and posttreatment imaging surveillance, and imaging features were extracted from postcontrast phases of pretreatment MRI examinations using a pretrained convolutional neural network (VGG-16). Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models--clinical, imaging, combined--for recurrence prediction within 1–6 years posttreatment. Ultimately, all three models predicted posttreatment recurrence for early-stage HCC from pretreatment clinical (AUC 0.60–0.78,

Scientists are tasking a language-processing Artificial Intelligence (AI) with detecting and diagnosing the early signs of language-associated cognitive impairments in people with failing livers. In their findings, the researchers report finding evidence that this cognitive function is likely to be restored following a liver transplant. In their paper, published in the journal npj Digital Medicine (formerly Nature Digital Medicine), the researchers explained how they used natural language processing (NPL) to evaluate electronic message samples from patients with chronic liver failure. This disease is associated with transient cognitive abnormalities. These include diminished attention spans, loss of memory, and a reduced ability for an individual to detect and respond to their surroundings.

Natural language processing, the technology that lets computers read, decipher, understand and make sense of human language, is the driving force behind internet search engines, email filters, digital assistants such as Amazon's Alexa and Apple's Siri, and language-to-language translation apps. Now, Johns Hopkins Medicine researchers say they have given this technology a new job as a clinical detective, diagnosing the early and subtle signs of language-associated cognitive impairments in patients with failing livers. They also report finding evidence that cognitive functioning is likely to be restored following a liver transplant. In a new paper in the journal npj Digital Medicine (formerly Nature Digital Medicine), the researchers describe how they used natural language processing, or NLP, to evaluate electronic message samples from patients with end-stage liver disease (ESLD), also known as chronic liver failure. ESLD has been associated with transient cognitive abnormalities such as diminished attention span, loss of memory and reduced psychomotor speed, an individual's ability to detect and respond to the world around them.

25% of people who received a liver transplant will go on to develop diabetes within the next 5 years. These thousands of individuals are at 2-fold higher risk of cardiovascular events, graft loss, infections, as well as lower long-term survival. This is partly due to the medication used during and/or after transplant that significantly impacts metabolic balance. To assess which medication best suits the patient's condition, clinicians need an accurate estimate of diabetes risk. Both patient's historical data and observations at the current visit are informative in predicting whether the patient will develop diabetes within the following year. In this work we compared a variety of time-to-event prediction models as well as classifiers predicting the likelihood of the event within a year from the current checkup. We are particularly interested in comparing two types of models: 1) standard time-to-event predictors where the historical measurements are merely concatenated, 2) incorporating Deep Markov Model to first obtain low-dimensional embedding of historical data and then using this embedding as an additional input into the model. We compared a variety of algorithms including standard and regularized Cox proportional-hazards model (CPH), mixed effect random forests, survival-forests and Weibull Time-To-Event Recurrent Neural Network (WTTE-RNN). The results show that although all methods' performances varied from year to year and there was no clear winner across all the time points, regularized CPH model that used 1 to 3 years of historical visits data on average achieved a high, clinically relevant Concordance Index of .863. We thus recommend this model for further prospective clinical validation and hopefully, an eventual use in the clinic to improve clinicians' ability to personalize post-operative care and reduce the incidence of new-onset diabetes post liver transplant.

Medical researchers have mimicked the way dating websites match lonely hearts to pair donor livers with suitable patients. Similar to the way dating sites analyse different characteristics to match partners, Melbourne researchers have employed artificial intelligence to predict whether a donor liver would die soon after being transplanted. The machine did much better than current clinical methods, leading the researchers on Thursday to say the matchmaking machine technique could decrease the number of potentially-viable organs being discarded.