Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.

For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the effective delivery of insulin by Artificial Pancreas (AP) systems. Deep learning frameworks like Long Short-Term-Memory (LSTM) have been widely used to predict BG using continuous glucose monitor (CGM) data. However, these methods usually require large amounts of training data for personalized forecasts.

Food Records Show Daily Variation in Diet during Pregnancy: Results from the Temporal Research in Eating, Nutrition, and Diet during Pregnancy Study.

Background: Diet is critical for pregnant individuals and their offspring, but insight into diet during pregnancy mainly comes from questionnaires and recalls.

Objectives: To obtain detailed real-time dietary data during pregnancy to evaluate intra- and interindividual variation in intakes.

Methods: Pregnant individuals were recruited from a New York City health system December 2020-June 2023.

Perceived Penalties for Sharing Patient Beliefs with Health Care Providers.

Background: Health care interactions may require patients to share with a physician information they believe but is incorrect. While a key piece of physicians' work is educating their patients, people's concerns of being seen as uninformed or incompetent by physicians may lead them to think that sharing incorrect health beliefs comes with a penalty. We tested people's perceptions of patients who share incorrect information and how these perceptions vary by the reasonableness of the belief and its centrality to the patient's disease.

Transforming Big Data into AI-ready data for nutrition and obesity research.

Objective: Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline.

Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity.

Background: Accurately identifying eating patterns, specifically the timing, frequency, and distribution of eating occasions (EOs), is important for assessing eating behaviors, especially for preventing and managing obesity and type 2 diabetes (T2D). However, existing methods to study EOs rely on self-report, which may be prone to misreporting and bias and has a high user burden. Therefore, objective methods are needed.

Less is more: information needs, information wants, and what makes causal models useful.

Each day people make decisions about complex topics such as health and personal finances. Causal models of these domains have been created to aid decisions, but the resulting models are often complex and it is not known whether people can use them successfully. We investigate the trade-off between simplicity and complexity in decision making, testing diagrams tailored to target choices (Experiments 1 and 2), and with relevant causal paths highlighted (Experiment 3), finding that simplicity or directing attention to simple causal paths leads to better decisions.

Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.

Background: Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance.

Methods: To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties.

Machine learning modeling practices to support the principles of AI and ethics in nutrition research.

Background: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias.

Methods: Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models.

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.

Classification of Level of Consciousness in a Neurological ICU Using Physiological Data.

Background: Impaired consciousness is common in intensive care unit (ICU) patients, and an individual's degree of consciousness is crucial to determining their care and prognosis. However, there are no methods that continuously monitor consciousness and alert clinicians to changes. We investigated the use of physiological signals collected in the ICU to classify levels of consciousness in critically ill patients.

Health Information Sourcing and Health Knowledge Quality: Repeated Cross-sectional Survey.

Background: People's health-related knowledge influences health outcomes, as this knowledge may influence whether individuals follow advice from their doctors or public health agencies. Yet, little attention has been paid to where people obtain health information and how these information sources relate to the quality of knowledge.

Objective: We aim to discover what information sources people use to learn about health conditions, how these sources relate to the quality of their health knowledge, and how both the number of information sources and health knowledge change over time.

Hierarchical Information Criterion for Variable Abstraction.

Large biomedical datasets can contain thousands of variables, creating challenges for machine learning tasks such as causal inference and prediction. Feature selection and ranking methods have been developed to reduce the number of variables and determine which are most important. However in many cases, such as in classification from diagnosis codes, ontologies, and controlled vocabularies, we must choose not only which variables to include but also at what level of granularity.

Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data.

Managing a chronic disease like Type 1 diabetes (T1D) is both challenging and time consuming, but new technologies that allow continuous measurement of glucose and delivery of insulin have led to significant improvements. The development of an artificial pancreas (AP), which algorithmically determines insulin dosing and delivers insulin in a fully automated way, may transform T1D care but it is not yet widely available. Patient-led alternatives, like the Open Artificial Pancreas (OpenAPS), are being used by hundreds of individuals and have also led to a dramatic increase in the availability of patient generated health data (PGHD).

Lagged Correlations among Physiological Variables as Indicators of Consciousness in Stroke Patients.

Consciousness is a highly significant indicator of an ICU patient's condition but there is still no method to automatically measure it. Instead, time consuming and subjective assessments are used. However, many brain and physiologic variables are measured continuously in neurological ICU, and could be used as indicators for consciousness.

Automated Estimation of Food Type from Body-worn Audio and Motion Sensors in Free-Living Environments.

Nutrition is fundamental to maintaining health, managing chronic diseases, and preventing illness, but unlike physical activity there is not yet a way to unobtrusively and automatically measure nutrition. While recent work has shown that body-worn sensors can be used to identify meal times, to have an impact on health and fully replace manual food logs, we need to identify not only when someone is eating, but what they are consuming. However, it is challenging to collect labeled data in daily life, while lab data does not always generalize to reality.

Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series.

Increasingly large observational datasets from healthcare and social media may allow new types of causal inference. However, these data are often missing key variables, increasing the chance of finding spurious causal relationships due to confounding. While methods exist for causal inference with latent variables in static cases, temporal relationships are more challenging, as varying time lags make latent causes more difficult to uncover and approaches often have significantly higher computational complexity.

How causal information affects decisions.

Background: Causality is inherently linked to decision-making, as causes let us better predict the future and intervene to change it by showing which variables have the capacity to affect others. Recent advances in machine learning have made it possible to learn causal models from observational data. While these models have the potential to aid human decisions, it is not yet known whether the output of these algorithms improves decision-making.

Automated meal detection from continuous glucose monitor data through simulation and explanation.

Background: Artificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose.

Objective: We investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose.

Automated Identification of Causal Moderators in Time-Series Data.

Causal inference is often taken to mean finding links between individual variables. However in many real-world cases, such as in biological systems, relationships are more complex, with groups of factors needed to produce an effect, or some factors only modifying other relationships rather than producing outcomes alone. For instance, weight may alter the efficacy of a drug without causing side effects itself.

Replicability, Reproducibility, and Agent-based Simulation of Interventions.

Secondary use of medical data and use of observational data for causal inference has been growing. Yet these data bring many challenges such as confounding due to unobserved variables and variation in medical processes across settings. Further, while methods exist to handle some of these problems, researchers lack ground truth to evaluate these methods.

Using uncertain data from body-worn sensors to gain insight into type 1 diabetes.

The amount of observational data available for research is growing rapidly with the rise of electronic health records and patient-generated data. However, these data bring new challenges, as data collected outside controlled environments and generated for purposes other than research may be error-prone, biased, or systematically missing. Analysis of these data requires methods that are robust to such challenges, yet methods for causal inference currently only handle uncertainty at the level of causal relationships - rather than variables or specific observations.

Causal Structure of Brain Physiology after Brain Injury from Subarachnoid Hemorrhage.

High frequency physiologic data are routinely generated for intensive care patients. While massive amounts of data make it difficult for clinicians to extract meaningful signals, these data could provide insight into the state of critically ill patients and guide interventions. We develop uniquely customized computational methods to uncover the causal structure within systemic and brain physiologic measures recorded in a neurological intensive care unit after subarachnoid hemorrhage.

Causal Explanation Under Indeterminism: A Sampling Approach.

One of the key uses of causes is to explain why things happen. Explanations of specific events, like an individual's heart attack on Monday afternoon or a particular car accident, help assign responsibility and inform our future decisions. Computational methods for causal inference make use of the vast amounts of data collected by individuals to better understand their behavior and improve their health.

Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data.

Most clinical and biomedical data contain missing values. A patient's record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined.