Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype

We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition’s effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data

Ensemble Kalman Methods With Constraints

Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the methodology which may be readily applied when computer code is available for the underlying state-space dynamics (for state estimation) or for the parameter-to-observable map (for parameter estimation). There are many applications in which it is desirable to enforce prior information in the form of equality or inequality constraints on the state or parameter. This paper establishes a general framework for doing so, describing a widely applicable methodology, a theory which justifies the methodology, and a set of numerical experiments exemplifying it

Enabling Personalized Decision Support with Patient-Generated Data and Attributable Components

Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems

Elements of attention in HIV-infected adults: Evaluation of an existing model

Because of the multifactorial nature of neuropsychological tests, attention remains poorly defined from a neuropsychological perspective, and conclusions made regarding attention across studies may be limited due to the different nature of the measures used. Thus, a more definitive schema for this neurocognitive domain is needed. We assessed the applicability of Mirsky and Duncan\u27s (2001) neuropsychological model of attention to a cohort of 104 HIV+ adults. Our analysis resulted in a five-factor structure similar to that of previous studies, which explained 74.5% of the variance. However, based on the psychometric characteristics of the measures comprising each factor, we offer an alternative interpretation of the factors. Findings also indicate that one factor, which is generally not assessed in clinical neuropsychology settings, may be more predictive of real-world behaviors (such as medication adherence) than those composed of traditional measures. Suggestions for further research in this important area are discussed