On-device research index

arXiv research

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.

169,291 papers · 148 categories

Trend · papers per month

count % of corpus

4 results for “hypoglycemia”

Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Techniquestat.ML

Paper predicts daytime hypoglycemia using CGM data and CART.

problem Accurately predicting daytime hypoglycemia to prevent dangerous situations.

method Classification and Regression Tree (CART) applied to CGM data.

result Model detects almost 80% of hypoglycemic events 15 min in advance.

hypoglycemia continuous glucose monitoring classification regression tree predictive analytics

2017-04-27abs ↗pdf ↗

Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement Learningstat.ML

Reinforcement Learning improves insulin bolus decisions for type-I diabetes patients.

problem Optimal insulin bolus decisions for type-I diabetes patients are not well-established.

method Applied Reinforcement Learning to simulated T1DM data.

result Optimal bolus rule differs from standard advisors and can prevent hypoglycemia.

Reinforcement Learning Type-I Diabetes Insulin Bolus Hypoglycemia Reinforcement

2020-07-23abs ↗pdf ↗

Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Dataeess.SP

Study finds RNNs predict STBG better than ARIMA, useful for diabetes patients.

problem Improving short-term blood glucose prediction for diabetes management.

method Investigated Recurrent Neural Networks (RNNs) and compared them to ARIMA for STBG prediction.

result Population-based RNN model outperforms ARIMA across various prediction horizons.

Diabetes Continuous Glucose Monitoring Recurrent Neural Networks Short Term Blood Glucose Prediction Autoregressive Integrated Moving Average

2020-02-06abs ↗pdf ↗

The Challenge of Predicting Meal-to-meal Blood Glucose Concentrations for Patients with Type I Diabetescs.LG

Study aims to predict meal-to-meal blood glucose for T1D patients.

problem Accurately predict future blood glucose levels from current features.

method Applied machine learning algorithms to a new T1D dataset.

result Most accurate predictor achieved an errL1 loss of 2.70 mmol/L.

Type I Diabetes Blood Glucose Prediction Machine Learning Gaussian Process Regression Diabetes Diary

2019-03-29abs ↗pdf ↗