The Center for Disease Control estimates that over 37 million US adults suffer from chronic kidney disease (CKD), yet 9 out of 10 of these individuals are unaware of their condition due to the absence of symptoms in the early stages. It has a significant impact on patients' quality of life, particularly when it progresses to the need for dialysis. Early prediction of dialysis is crucial as it can significantly improve patient outcomes and assist healthcare providers in making timely and informed decisions. However, developing an effective machine learning (ML)-based Clinical Decision Support System (CDSS) for early dialysis prediction poses a key challenge due to the imbalanced nature of data. To address this challenge, this study evaluates various data augmentation techniques to understand their effectiveness on real-world datasets. We propose a new approach named Binary Gaussian Copula Synthesis (BGCS). BGCS is tailored for binary medical datasets and excels in generating synthetic minority data that mirrors the distribution of the original data. BGCS enhances early dialysis prediction by outperforming traditional methods in detecting dialysis patients. For the best ML model, Random Forest, BCGS achieved a 72% improvement, surpassing the state-of-the-art augmentation approaches. Also, we present a ML-based CDSS, designed to aid clinicians in making informed decisions. CDSS, which utilizes decision tree models, is developed to improve patient outcomes, identify critical variables, and thereby enable clinicians to make proactive decisions, and strategize treatment plans effectively for CKD patients who are more likely to require dialysis in the near future. Through comprehensive feature analysis and meticulous data preparation, we ensure that the CDSS's dialysis predictions are not only accurate but also actionable, providing a valuable tool in the management and treatment of CKD.

Sepsis is a life-threatening condition triggered by an extreme infection response. Our objective is to forecast sepsis patient outcomes using their medical history and treatments, while learning interpretable state representations to assess patients' risks in developing various adverse outcomes. While neural networks excel in outcome prediction, their limited interpretability remains a key issue. In this work, we use knowledge distillation via constrained variational inference to distill the knowledge of a powerful "teacher" neural network model with high predictive power to train a "student" latent variable model to learn interpretable hidden state representations to achieve high predictive performance for sepsis outcome prediction. Using real-world data from the MIMIC-IV database, we trained an LSTM as the "teacher" model to predict mortality for sepsis patients, given information about their recent history of vital signs, lab values and treatments. For our student model, we use an autoregressive hidden Markov model (AR-HMM) to learn interpretable hidden states from patients' clinical time series, and use the posterior distribution of the learned state representations to predict various downstream outcomes, including hospital mortality, pulmonary edema, need for diuretics, dialysis, and mechanical ventilation. Our results show that our approach successfully incorporates the constraint to achieve high predictive power similar to the teacher model, while maintaining the generative performance.

Objective: Peritoneal Dialysis (PD) is one of the most widely used life-supporting therapies for patients with End-Stage Renal Disease (ESRD). Predicting mortality risk and identifying modifiable risk factors based on the Electronic Medical Records (EMR) collected along with the follow-up visits are of great importance for personalized medicine and early intervention. Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare. Method and Materials: Our proposed model consists of a multi-channel feature extraction module and an adaptive feature importance recalibration module. AICare explicitly identifies the key features that strongly indicate the outcome prediction for each patient to build the health status embedding individually. This study has collected 13,091 clinical follow-up visits and demographic data of 656 PD patients. To verify the application universality, this study has also collected 4,789 visits of 1,363 hemodialysis dialysis (HD) as an additional experiment dataset to test the prediction performance, which will be discussed in the Appendix. Results: 1) Experiment results show that AICare achieves 81.6%/74.3% AUROC and 47.2%/32.5% AUPRC for the 1-year mortality prediction task on PD/HD dataset respectively, which outperforms the state-of-the-art comparative deep learning models. 2) This study first provides a comprehensive elucidation of the relationship between the causes of mortality in patients with PD and clinical features based on an end-to-end deep learning model. 3) This study first reveals the pattern of variation in the importance of each feature in the mortality prediction based on built-in interpretability. 4) We develop a practical AI-Doctor interaction system to visualize the trajectory of patients' health status and risk indicators.

Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data. To evaluate the model, we studied approximately 3 million Medicare beneficiaries for which we made over 8 million predictions. We showed that the model can identify at risk patients with over 90% sensitivity and specificity. Although additional work is required before this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcomes.

This article is adapted from Voices in the Code: A Story About People, Their Values, and the Algorithm They Made, out Sept. 8 from Russell Sage Foundation Press. In May 2021, I got a call I never expected. I was working on a book about A.I. ethics, focused on the algorithm that gives out kidneys to transplant patients in the United States. Darren Stewart--a data scientist from UNOS, the nonprofit that runs the kidney allocation process--was calling to get my take: How many decimal places should they include when calculating each patient's allocation score? The score is an incredibly important number, given it determines which patient will get first chance at each donated organ.

THE ROAD TO WELLVILLE?: AI-powered health apps are proliferating. The mental stresses of the pandemic have fueled a boom in wellness devices that track speech, facial expressions and even eye blinks to assess emotional states. This kind of "affective computing" can replicate therapy or detect depression when in-person care isn't available -- and even remotely monitor workers and children. But the burst of interest is heightening concerns about whether there's enough government oversight of the technology, known as "emotion AI". Future Pulse spoke about the tension points with Alexandrine Royer, a doctoral candidate studying the field and the digital economy at the University of Cambridge and a student fellow at the Leverhulme Centre for the Future of Intelligence.

Renal transplantation is widely regarded as the best treatment for patients with end-stage kidney disease. Over the past 15 years, demand in the UK for kidney transplants has been rising, resulting in more elderly deceased donors being considered. The problem with elderly donors is that kidney function deteriorates with age. Kidney transplants from elderly donors are associated with higher risks of early failure. Early failure of a kidney graft is a disastrous outcome for the recipient.

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.

Washington, DC (November 7, 2019) -- Machine learning, a form of artificial intelligence, may help improve care for patients with kidney failure. The findings come from a study that will be presented at ASN Kidney Week 2019 November 5-November 10 at the Walter E. Washington Convention Center in Washington, DC. For the study, Ollie Fielding (pulseData, in New York) and his colleagues deployed a machine learning model to identify patients at risk of requiring kidney replacement therapy, such as dialysis or kidney transplantation. An electronic health record database of 110,998 patients was used to create a machine learning model to predict progression to kidney failure. The system calculates weekly risk scores for patients, and for those with high risk scores, an alert is sent so that treatment discussions can be made by a multidisciplinary team of clinicians.