Two new artificial intelligence apps use your smartphone camera to screen for urinary tract infections (UTIs) or possible signs of chronic kidney disease. Designed to cater for people in lockdown, the apps from Israel-based health technology company Healthy.io With the UTI app, called Velieve, users order a UTI test kit to be delivered to their home, submit their results through the app, and then receive an in-app diagnosis within 30 minutes. The kidney disease app, meanwhile, will be'prescribed' by GPs to patients who are at high risk of chronic kidney disease. This test, which detects albumin to creatinine ratio (ACR) – a key marker of kidney disease – in urine samples, will also be delivered via post and analysed through the app, with the result delivered to directly to the GP.
Introduction: Readmission and death in cirrhosis are common, expensive, and difficult to predict. Our aim was to evaluate the abilities of multiple artificial intelligence (AI) techniques to predict clinical outcomes based on variables collected at admission, during hospitalization, and at discharge. Methods: We used the multicenter North American Consortium for the Study of End-Stage Liver Disease (NACSELD) cohort of cirrhotic inpatients who are followed up through 90-days postdischarge for readmission and death. We used statistical methods to select variables that are significant for readmission and death and trained 3 AI models, including logistic regression (LR), kernel support vector machine (SVM), and random forest classifiers (RFC), to predict readmission and death. We used the area under the receiver operating characteristic curve (AUC) from 10-fold crossvalidation for evaluation to compare sexes.
Kidneys were flown a record 10 miles across the Nevada desert by drone earlier this month, setting a record for unmanned aerial organ delivery. It comes after the first-ever successful drone delivery of an organ was completed last year, when a 44-year-old's new kidney over two miles in an unmanned drone from the Living Legacy Foundation organ distribution center to the University of Maryland Medical Center (both in Baltimore) on April 19. The latest drone organ delivery - completed by a MissionGo device - surpasses that historic flight by traveling five-times further. It was the second of two human tissue drone flights completed the same day, September 17. MissionGO and the Nevada Donor Network flew corneas two miles by drone, from one hospital to another, then flew research kidneys 10 miles from a remote airport to a town in the middle of the state's desert.
Sign on the back of a vehicle pleading for someone to donate a kidney to a sick man in Mississauga, ... [ ] Ontario, Canada. In March 2020, there were more than 110,000 people on the national transplant waiting list. According to the U.S. Government Information on Organ Donation and Transplantation, 20 people die every day while waiting for an organ donor transplant. And every nine minutes, someone is added to the national transplant list. In 2019, 39,718 transplant operations were performed, which was a record high for the seventh consecutive year.
Alizadehsani, Roohallah, Roshanzamir, Mohamad, Hussain, Sadiq, Khosravi, Abbas, Koohestani, Afsaneh, Zangooei, Mohammad Hossein, Abdar, Moloud, Beykikhoshk, Adham, Shoeibi, Afshin, Zare, Assef, Panahiazar, Maryam, Nahavandi, Saeid, Srinivasan, Dipti, Atiya, Amir F., Acharya, U. Rajendra
Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased.
Renalytix AI plc announced a collaboration with AstraZeneca to develop and launch precision medicine strategies for cardiovascular, renal and metabolic diseases. The first stage in the collaboration will use KidneyIntelX, an artificial intelligence-enabled in vitro diagnostic platform, to examine further improving outcomes for patients with chronic kidney disease (CKD) and its complications, in coordination with the Mount Sinai Health System. The goal of the first stage is to help improve guideline-based standard-of-care for optimal utilization of existing and novel therapeutics using the KidneyIntelX testing platform and proprietary care management software. An estimated 700 million patients worldwide have CKD,1 which is also associated with an increased risk of metabolic and hematologic complications, such as hyperkalemia (elevated levels of potassium in the blood) and anemia.2,3 The first stage will assess the impact of AI-enabled in vitro diagnostic solutions to optimize utilization of therapeutics in CKD under current standard of care protocols.
For patients with type 2 diabetes or the APOL1-HR genotype, a machine learning test integrating biomarkers and electronic health record data demonstrated improved prediction of kidney failure compared with commonly used clinical models. According to Kinsuk Chauhan, MD, MPH, of Icahn School of Medicine at Mount Sinai, and colleagues, diabetic kidney disease from type 2 diabetes accounts for 44% of all patients with end-stage kidney disease, with the APOL1 high-risk genotypes also associated with increased risk for chronic kidney disease progression and eGFR decline that may ultimately result in kidney failure. "Even though these populations are on average higher risk than the general population, accurate prediction of who will have rapid kidney function decline (RKFD) and worse kidney outcomes is lacking," the researchers wrote, noting that the current standard of using the kidney failure risk equation to predict ESKD has only been validated in patients who already have kidney disease and not in those with preserved kidney function at baseline. "Widespread electronic health records (EHR) usage provides the potential to leverage thousands of clinical features," the researchers added. "Standard statistical approaches are inadequate to leverage this data due to feature volume, unaligned nature of data and correlation structure."
Motivated by kidney exchange, we study a stochastic cycle and chain packing problem, where we aim to identify structures in a directed graph to maximize the expectation of matched edge weights. All edges are subject to failure, and the failures can have nonidentical probabilities. To the best of our knowledge, the state-of-the-art approaches are only tractable when failure probabilities are identical. We formulate a relevant non-convex optimization problem and propose a tractable mixed-integer linear programming reformulation to solve it. In addition, we propose a model that integrates both risks and the expected utilities of the matching by incorporating conditional value at risk (CVaR) into the objective function, providing a robust formulation for this problem. Subsequently, we propose a sample-average-approximation (SAA) based approach to solve this problem. We test our approaches on data from the United Network for Organ Sharing (UNOS) and compare against state-of-the-art approaches. Our model provides better performance with the same running time as a leading deterministic approach (PICEF). Our CVaR extensions with an SAA-based method improves the $\alpha \times 100\%$ ($0<\alpha\leqslant 1$) worst-case performance substantially compared to existing models.
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 algorithms increasingly make decisions that impact entire groups of humans. Since humans tend to hold varying and even conflicting preferences, AI algorithms responsible for making decisions on behalf of such groups encounter the problem of preference aggregation: combining inconsistent and sometimes contradictory individual preferences into a representative aggregate. In this paper, we address this problem in a real-world public health context: kidney exchange. The algorithms that allocate kidneys from living donors to patients needing transplants in kidney exchange matching markets should prioritize patients in a way that aligns with the values of the community they serve, but allocation preferences vary widely across individuals. In this paper, we propose, implement and evaluate a methodology for prioritizing patients based on such heterogeneous moral preferences. Instead of selecting a single static set of patient weights, we learn a distribution over preference functions based on human subject responses to allocation dilemmas, then sample from this distribution to dynamically determine patient weights during matching. We find that this methodology increases the average rank of matched patients in the sampled preference ordering, indicating better satisfaction of group preferences. We hope that this work will suggest a roadmap for future automated moral decision making on behalf of heterogeneous groups.