Enabling personalized decision support with patient-generated data and attributable components

Mitchell, Elliot G.; Tabak, Esteban G.; Levine, Matthew E.; Mamykina, Lena; Albers, David J.

doi:10.1016/j.jbi.2020.103639

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…Because self-monitoring data are manually entered by users, there are often a small number of data points that are prone to include errors and outliers. These characteristics pose challenges for ML, and ACA has advantages over other methods like linear regression because it is able to capture non-linear relationships, is less sensitive to erroneous data points, and more effectively estimates uncertainty [73].…”

Section: Machine Learningmentioning

confidence: 99%

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations

Mitchell

Heitkemper

Burgermaster

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.

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Section: Machine Learningmentioning

confidence: 99%

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations

Mitchell

Heitkemper

Burgermaster

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Self Cite

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Intelligent Decision Support in Personal Health: Personalized Health Coaching in Type 2 Diabetes

Mamykina,

Mitchell,

Desai

et al. 2024

Cognitive Informatics in Biomedicine and Healthcare

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A simple modeling framework for prediction in the human glucose–insulin system

Sirlanci

Levine

Wang

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic management. BG dynamics are nonlinear, complex, and nonstationary, which can be represented by nonlinear models. However, the sparsity of routinely collected data creates parameter identifiability issues when high-fidelity complex models are used, thereby resulting in inaccurate forecasts. One can use models with reduced physiological fidelity for robust and accurate parameter estimation and forecasting with sparse data. For this purpose, we approximate the nonlinear dynamics of BG regulation by a linear stochastic differential equation: we develop a linear stochastic model, which can be specialized to different settings: type 2 diabetes mellitus (T2DM) and intensive care unit (ICU), with different choices of appropriate model functions. The model includes deterministic terms quantifying glucose removal from the bloodstream through the glycemic regulation system and representing the effect of nutrition and externally delivered insulin. The stochastic term encapsulates the BG oscillations. The model output is in the form of an expected value accompanied by a band around this value. The model parameters are estimated patient-specifically, leading to personalized models. The forecasts consist of values for BG mean and variation, quantifying possible high and low BG levels. Such predictions have potential use for glycemic management as part of control systems. We present experimental results on parameter estimation and forecasting in T2DM and ICU settings. We compare the model’s predictive capability with two different nonlinear models built for T2DM and ICU contexts to have a sense of the level of prediction achieved by this model.

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Enabling personalized decision support with patient-generated data and attributable components

Cited by 5 publications

References 36 publications

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations

Intelligent Decision Support in Personal Health: Personalized Health Coaching in Type 2 Diabetes

A simple modeling framework for prediction in the human glucose–insulin system

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