Breakthroughs, discoveries, and DIY tips sent six days a week. In Mary Shelley's, a mad scientist creates a monstrous creature with severed body parts. In certain film adaptations, a dismembered head is tacked onto the malformed body. Then, with the help of a lightning storm, a new life is born. From the first successful kidney transplant in 1954, modern organ transplantation has often been linked to the horrors of Frankenstein .

Numerous real-world systems, ranging from healthcare to energy grids, involve users competing for finite and potentially scarce resources.

Graft-versus-host disease (GVHD) is a rare but often fatal complication in liver transplantation, with a very high mortality rate. By harnessing multi-modal deep learning methods to integrate heterogeneous and imbalanced electronic health records (EHR), we aim to advance early prediction of GVHD, paving the way for timely intervention and improved patient outcomes. In this study, we analyzed pre-transplant electronic health records (EHR) spanning the period before surgery for 2,100 liver transplantation patients, including 42 cases of graft-versus-host disease (GVHD), from a cohort treated at Mayo Clinic between 1992 and 2025. The dataset comprised four major modalities: patient demographics, laboratory tests, diagnoses, and medications. We developed a multi-modal deep learning framework that dynamically fuses these modalities, handles irregular records with missing values, and addresses extreme class imbalance through AUC-based optimization. The developed framework outperforms all single-modal and multi-modal machine learning baselines, achieving an AUC of 0.836, an AUPRC of 0.157, a recall of 0.768, and a specificity of 0.803. It also demonstrates the effectiveness of our approach in capturing complementary information from different modalities, leading to improved performance. Our multi-modal deep learning framework substantially improves existing approaches for early GVHD prediction. By effectively addressing the challenges of heterogeneity and extreme class imbalance in real-world EHR, it achieves accurate early prediction. Our proposed multi-modal deep learning method demonstrates promising results for early prediction of a GVHD in liver transplantation, despite the challenge of extremely imbalanced EHR data.

The decision that a patient and a clinician jointly make when a donor organ is offered for transplantation is critical and carries serious consequences.

Thousands of patients worldwide are waiting for a potentially life-saving donor. Thousands of patients worldwide are waiting for a potentially life-saving donor. Doctors have developed an AI tool that could reduce wasted efforts to transplant organs by 60%. Thousands of patients worldwide are waiting for a potentially life-saving donor, and more candidates are stuck on waiting lists than there are available organs. Recently, in cases where people need a liver transplant, access has been expanded by using donors who die after cardiac arrest.

With the demand for human donor organs desperately outstripping supply, scientists are working to see if genetically edited pig organs can bridge the gap. Leonardo Riella, medical director for kidney transplantation at Massachusetts General Hospital, checks on Tim Andrews after his pig kidney transplant. Surgeons at Massachusetts General Hospital have removed a genetically engineered pig kidney from a 67-year-old New Hampshire man after a period of decreasing kidney function, the hospital confirmed to WIRED in a statement. The organ functioned for nearly nine months, longer than previous pig organ transplants, before it was removed on October 23. Tim Andrews received the pig kidney on January 25 after being on dialysis for more than two years due to end-stage kidney disease.

Numerous real-world systems, ranging from healthcare to energy grids, involve users competing for finite and potentially scarce resources.

The decision that a patient and a clinician jointly make when a donor organ is offered for transplantation is critical and carries serious consequences.