Departments of Population Health and Radiology Center for Data Science New Y ork University Langone Medical Center Abstract As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a more powerful machine learning model, which has access to a larger variable set and trained on 1.6 million patients' EHR data. We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to MLdriven models. 1 Introduction Data-driven models have become more common in the U.S. healthcare field as their use in clinical operations and diagnosing procedures have expanded exponentially. The ever-increasing processing power of machine-learning algorithms allows automatic analysis of huge quantities of data, theoretically maximizing the efficiency and accuracy of the medical diagnosing process. Predictions from machine-learning models already drive important healthcare decisions for over 70 million people across the United States.
Mount Sinai Health System on Friday announced an exclusive multiyear license and partnership with RenalytixAI, an artificial-intelligence startup with offices in New York and the United Kingdom. The goal is to reduce the $98 billion in preventable kidney disease and dialysis costs by predicting which patients are at the greatest risk of advanced kidney disease and taking steps to treat them early on. The venture will draw from the more than 3 million electronic health records in Mount Sinai's system, plus an additional 43,000 patient records in Mount Sinai's BioMe BioBank, part of the Charles Bronfman Institute for Personalized Medicine. The bank collects DNA and blood serum from a diverse patient base to identify biomarkers, substances that can indicate disease, infection or environmental exposure. All data will be de-identified to protect patient privacy "We can look at relationships that we could never look at before," said RenalytixAI CEO James McCullough.
Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management. However, many complex chronic diseases exhibit large degrees of heterogeneity, and furthermore there is not always a single readily available biomarker to quantify disease severity. Even when such a clinical variable exists, there are often additional related biomarkers routinely measured for patients that may better inform the predictions of their future disease state. To this end, we propose a novel probabilistic generative model for multivariate longitudinal data that captures dependencies between multivariate trajectories. We use a Gaussian process based regression model for each individual trajectory, and build off ideas from latent class models to induce dependence between their mean functions. We fit our method using a scalable variational inference algorithm to a large dataset of longitudinal electronic patient health records, and find that it improves dynamic predictions compared to a recent state of the art method. Our local accountable care organization then uses the model predictions during chart reviews of high risk patients with chronic kidney disease.
The role of artificial intelligence (AI) in healthcare continues to rise. According to a June 2018 ABI Research report, the number of patient monitoring devices, which also includes AI for home-based preventative healthcare, that use data to train AI models for predictive analytics will be 3.1 million in 2021, up from 53,000 in 2017. That connectivity is predicted to save hospitals around $52 billion in 2021. "We now have exponential increases in digital healthcare data due to the internet, electronic health records, personal health records, cellphones, wearable devices, digital medical devices, sensors and many other factors," said Drew Gantt. "This data will fuel algorithmic solutions, clinical decision support tools, and visual tools in the near term."
The burgeoning need for kidney transplantation mandates immediate attention. Mismatch of deceased donor-recipient kidney leads to post-transplant death. To ensure ideal kidney donor-recipient match and minimize post-transplant deaths, the paper develops a prediction model that identifies factors that determine the probability of success of renal transplantation, that is, if the kidney procured from the deceased donor can be transplanted or discarded. The paper conducts a study enveloping data for 584 imported kidneys collected from 12 transplant centers associated with an organ procurement organization located in New York City, NY. The predicting model yielding best performance measures can be beneficial to the healthcare industry. Transplant centers and organ procurement organizations can take advantage of the prediction model to efficiently predict the outcome of kidney transplantation. Consequently, it will reduce the mortality rate caused by mismatching of donor-recipient kidney transplantation during the surgery.