Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem. At present, most of the models used for predicting the progression of CKD are static models. We aim to develop a dynamic kidney failure prediction model based on deep learning (KFDeep) for CKD patients, utilizing all available data on common clinical indicators from real-world Electronic Health Records (EHRs) to provide real-time predictions. Findings: A retrospective cohort of 4,587 patients from EHRs of Yinzhou, China, is used as the development dataset (2,752 patients for training, 917 patients for validation) and internal validation dataset (917 patients), while a prospective cohort of 934 patients from the Peking University First Hospital CKD cohort (PKUFH cohort) is used as the external validation dataset. The AUROC of the KFDeep model reaches 0.946 (95\% CI: 0.922-0.970) on the internal validation dataset and 0.805 (95\% CI: 0.763-0.847) on the external validation dataset, both surpassing existing models. The KFDeep model demonstrates stable performance in simulated dynamic scenarios, with the AUROC progressively increasing over time. Both the calibration curve and decision curve analyses confirm that the model is unbiased and safe for practical use, while the SHAP analysis and hidden layer clustering results align with established medical knowledge. Interpretation: The KFDeep model built from real-world EHRs enhances the prediction accuracy of kidney failure without increasing clinical examination costs and can be easily integrated into existing hospital systems, providing physicians with a continuously updated decision-support tool due to its dynamic design.

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

Machine learning, a form of artificial intelligence, may help improve care for patients with kidney failure. Chronic kidney disease is a life-long condition in which the kidneys can gradually stop working over a period of months or years. A significant number of patients with the condition are either on dialysis or have had a kidney transplant. The findings on how machine learning may improve kidney patient care come from a study that are being presented this week at ASN Kidney Week 2019 that takes place from November 5 – November 10 at the Walter E. Washington Convention Center in Washington. For the study, researcher, Ollie Fielding, and his colleagues deployed a machine learning model to identify patients at risk of requiring kidney replacement therapy, such as dialysis or kidney transplantation.

AI has enormous potential when it comes to the healthcare field, capable of improving diagnoses and finding new, more effective drugs. However, as a piece in Scientific American recently discussed, the speed with which AI is penetrating the healthcare field also opens up many new challenges and risks. Over the course of the past five years, the US Food and Drug Administration has approved over 40 different AI products. However, as reported by Scientific American, none of the products cleared for sale in the US have had their performance evaluated in randomized controlled clinical trials. Many AI medical tools don't even require approval by the FDA.

Experts agree that we're headed toward a future where global leadership in artificial intelligence will translate into economic and military dominance. Unfortunately, authoritarian regimes, such as China, have inherent advantages in research and development. The training of A.I. systems requires data -- lots of it. Big data is the oil of the Digital Age and whoever has the most of it -- at the highest quality and at the lowest cost -- will have a comparative advantage. Assembling and using big data sets in developed countries, however, can be complicated, for privacy and legal reasons. For example, the European Union is considering rules giving each individual the right to control how their facial data can be used in facial recognition technology -- which will (probably) slow development.

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[7].

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.

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.

What if technology could predict a hereditary disease you could stop from progressing? What if a visit to your primary physician for carpal tunnel syndrome ended with a suggestion to get tested for a rare illness? As Komodo Health's artificial intelligence algorithms crunch a decade of data about health conditions across several hundred million Americans, many what-if scenarios are becoming pathways for the next clinical assessment to be taken. Founded in 2014, Komodo, funded by Felicis Ventures and McKesson Ventures, has mapped out 300 million individual health identities across the country to find patterns signaling the presence of disease, years before they're ever diagnosed. At a time when chronic conditions account for 75 percent of the $3 trillion US healthcare spend annually, identifying when symptoms occur earliest or recognizing patterns of activity that are often a precursor to the manifestation of diseases is vital in preventing those economically, physically and mentally crippling illnesses to either exist or progress.

You might say that when it comes to delivery, Amazon is flooding the zone. With Walmart and Target nipping at its heels, the e-commerce giant is determined to win the delivery wars by dropping off packages wherever, and whenever, a shopper might want to receive them. For the first time, this year, Amazon delivered items to Coachella. On a business trip but didn't pack your tie? Hilton Hotels are among the locations where you can have one shipped to an Amazon locker. In Snohomish County, Washington, a robot may bring packages to your door.