A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Reinforcement Learning improves insulin bolus decisions for type-I diabetes patients.
Patients with Type I Diabetes (T1D) must take insulin injections to prevent the serious long term effects of hyperglycemia - high blood glucose (BG). Patients must also be careful not to inject too much insulin because this could induce hypoglycemia (low BG), which can potentially be fatal. Patients therefore follow a …
This paper provides a ML framework for diabetes prediction and care management.
Deep learning detects diabetic retinopathy stages from single fundus photos.
Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening. This labor-intensive task could greatly benefit from automatic detection using deep learnin…
Health professionals can use natural language processing (NLP) technologies when reviewing electronic health records (EHR). Machine learning free-text classifiers can help them identify problems and make critical decisions. We aim to develop deep learning neural network algorithms that identify EHR progress notes perta…
CopulaSMOTE addresses class imbalance in diabetes prediction models.
New method interprets deep embeddings for diabetes patient clustering.
According to the American Diabetes Association(ADA), 30.3 million people in the United States have diabetes, but only 7.2 million may be undiagnosed and unaware of their condition. Type 2 diabetes is usually diagnosed for most patients later on in life whereas the less common Type 1 diabetes is diagnosed early on in li…
Novel framework detects CKD in diabetic patients using sparse EHR representations.
Machine learning detects subtle glucose changes for early diabetes diagnosis.
The paper examines how calibration affects the interpretability of ML models in diabetes screening.
Paper develops NN models for diabetes screening using NHANES data.
Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves. Early prediction in such …
Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate…
Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a larg…
HAD-Net forecasts glucose levels with insights into insulin and carbs diffusion.
Deep neural network predicts diabetic readmission with high accuracy.
Prediction of disease onset from patient survey and lifestyle data is quickly becoming an important tool for diagnosing a disease before it progresses. In this study, data from the National Health and Nutrition Examination Survey (NHANES) questionnaire is used to predict the onset of type II diabetes. An ensemble model…
Deep learning has been successfully applied to a variety of image classification tasks. There has been keen interest to apply deep learning in the medical domain, particularly specialties that heavily utilize imaging, such as ophthalmology. One issue that may hinder application of deep learning to the medical domain is…
A method models continuous-time glucose distributions in children with diabetes.
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health re…
CNNs help diagnose diabetic retinopathy by localizing lesions.
Neural system optimizes glucose levels in diabetics.
Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning…
Ricci flows with bounded scalar curvature cannot develop Type I singular points.
Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to translate RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach …
Hybrid model learns interpretable meal-level glycemic control.
OTRE uses OT to improve retinal images, outperforming existing methods.
New model predicts blood glucose in diabetics with improved accuracy.
We study Lie algebras of type I, that is, a Lie algebra where all the eigenvalues of the operator ad are imaginary for all . We prove that the Morse-Novikov cohomology of a Lie algebra of type I is trivial for any closed -form. We focus on locally conformal symplectic structures…
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection…
25% of people who received a liver transplant will go on to develop diabetes within the next 5 years. These thousands of individuals are at 2-fold higher risk of cardiovascular events, graft loss, infections, as well as lower long-term survival. This is partly due to the medication used during and/or after transplant t…
Dual-edge spatial Jacobian image graph for interpretable diabetic retinopathy grading
Ancient solutions of Ricci flow with Type I growth are classified.
Study discovers patterns in insulin needs for T1D patients.
A new algorithm discovers causal factors between T2DM and bone mineral density.
RETINA Benchmark evaluates Bayesian deep learning on diabetic retinopathy detection.
RETAIN model improves glucose forecasting for diabetics, offering both accuracy and interpretability.
3D Ricci flows have bounded diameter before Type I singularities.
Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-…
The paper extends Ricci flow theory with Type-I scalar curvature bounds, proving entropy convergence and characterizing singular sets.
Objective: To evaluate unsupervised clustering methods for identifying individual-level behavioral-clinical phenotypes that relate personal biomarkers and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials and Methods: We used hierarchical clustering (HC) to identify groups of meals with simila…
Study on ancient Ricci flows with positive curvature, proving noncollapsedness.
Combines cost-sensitive and Neyman-Pearson paradigms for better binary classification.
Ricci flow singularities on compact Kähler surfaces are of Type I.
In this paper, we show that if the mean curvature of a closed smooth embedded mean curvature flow in R^3 is of type-I, then the rescaled flow at the first finite singular time converges smoothly to a self-shrinker flow with multiplicity one. This result confirms Ilmanen's multiplicity-one conjecture under the assumptio…
Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed us…