Predicting Insulin Pump Therapy Settings

Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While existing reinforcement learning models can predict actual physiological values for these settings, they require iteration and can be slow. The primary contribution of this research is to present a pipeline capable of providing instant predictions of close to actual patient physiological ISF, ICR, and BR from 30 days worth of data. In theory, this reduces patient waiting periods from roughly 6-8 weeks for existing reinforcement learning models to 30 days. This can serve as an aide in recommending pump therapy settings. Data used in this study include 1,000 simulated multivariate insulin pump time series. These time series were generated by a proprietary simulator developed by Tandem Diabetes Care. This multivariate time series data also integrates simulated continuous glucose monitor (CGM) data. This research proposes a pipeline for predicting actual patient BR, ISF, and ICR. Feature engineering, a component of this pipeline, included contextual consensus time series motif analysis. Models in the pipeline include time series native techniques such as Deep Convolutional Neural Networks (DNN) with a Long Short Term Memory input layers (LSTM) and aggregation based models such as Ridge regression and Lasso. Aggregation based ridge regression showed the most promising results, outperforming a naive model and a DNN model. For the data evaluated and with a 20% holdout test set, aggregate based ridge regression predicted the following normalized patient pump settings: ISF with a Mean Absolute Error of roughly 9.0%, ICR with a Mean Absolute Error of roughly 5% and BR with a Mean Absolute Error of roughly 6%. This is likely due to the reduction that aggregation based methods perform on each patient time series, reducing each one into a single tuple. This makes aggregation based methods less susceptible to noise and sparse signals. One limitation in this study is that the simulated data assumes a constant value of ISF, ICR, and BR over 24 hour periods for people with diabetes. In practice, this is not the case; ISF, ICR and BR fluctuate throughout the course of a day. A future consideration would be to use simulated data with non constant 24-hour ISF, ICR, and BR profiles. Insulin pumps greatly improve management and outcomes for people with diabetes. Ideally, by instantly improving programmed values of ISF, ICR, and BR, people relying on insulin pumps can spend less time worrying about their pump working ineffectively, and sleep through the night knowing it is less likely they will suffer a diabetes related medical emergency. To this end, it is the hope of the researchers that the ideas, pipelines, and inference presented are further explored and tested

A stacked long short-term memory approach for predictive blood glucose monitoring in women with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. The objectives of this study are to (1) develop benchmark glucose prediction models with long short-term memory (LSTM) recurrent neural network models using time-series data collected from the GDm-Health platform, (2) compare the prediction accuracy with published results, and (3) suggest an optimized clinical review schedule with the potential to reduce the overall number of blood tests for mothers with stable and within-range glucose measurements. A total of 190,396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days and 14 days to predict 14 days. Our results show that the optimized BG schedule based on a 7-day observational window to predict the BG of the next 14 days achieved the accuracies of the root mean square error (RMSE) = 0.958 ± 0.007, 0.876 ± 0.003, 0.898 ± 0.003, 0.622 ± 0.003, 0.814 ± 0.009 and 0.845 ± 0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. This is the first machine learning study that suggested an optimized blood glucose monitoring frequency, which is 7 days to monitor the next 14 days based on the accuracy of blood glucose prediction. Moreover, the accuracy of our proposed model based on the fingerstick blood glucose test is on par with the prediction accuracies compared with the benchmark performance of one-hour prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the stacked LSTM model is a promising approach for capturing the patterns in time-series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes

Changes in attitudes to awareness of hypoglycaemia during a hypoglycaemia awareness restoration programme are associated with avoidance of further severe hypoglycaemia episodes within 24 months: the A2A in HypoCOMPaSS study

Aims/hypothesis: The aims of this study were to assess cognitions relating to hypoglycaemia in adults with type 1 diabetes and impaired awareness of hypoglycaemia before and after the multimodal HypoCOMPaSS intervention, and to determine cognitive predictors of incomplete response (one or more severe hypoglycaemic episodes over 24 months). Methods: This analysis included 91 adults with type 1 diabetes and impaired awareness of hypoglycaemia who completed the Attitudes to Awareness of Hypoglycaemia (A2A) questionnaire before, 24 weeks and 24 months after the intervention, which comprised a short psycho-educational programme with optimisation of insulin therapy and glucose monitoring. Results: The age and diabetes duration of the participants were 48±12 and 29±12 years, respectively (mean±SD). At baseline, 91% reported one or more severe hypoglycaemic episodes over the preceding 12 months; this decreased to <20% at 24 weeks and after 24 months (p=0.001). The attitudinal barrier hyperglycaemia avoidance prioritised (2p=0.250, p=0.001) decreased from baseline to 24 weeks, and this decrease was maintained at 24 months (mean±SD=5.3±0.3 vs 4.3±0.3 vs 4.0±0.3). The decrease in asymptomatic hypoglycaemia normalised from baseline (2p=0.113, p=0.045) was significant at 24 weeks (1.5±0.3 vs 0.8±0.2). Predictors of incomplete hypoglycaemia response (one or more further episodes of severe hypoglycaemia) were higher baseline rates of severe hypoglycaemia, higher baseline scores for asymptomatic hypoglycaemia normalised, reduced change in asymptomatic hypoglycaemia normalised scores at 24 weeks, and lower baseline hypoglycaemia concern minimised scores (all p<0.05). Conclusions/interpretation: Participation in the HypoCOMPaSS RCT was associated with improvements in hypoglycaemia-associated cognitions, with hyperglycaemia avoidance prioritised most prevalent. Incomplete prevention of subsequent severe hypoglycaemia episodes was associated with persistence of the cognition asymptomatic hypoglycaemia normalised. Understanding and addressing cognitive barriers to hypoglycaemia avoidance is important in individuals prone to severe hypoglycaemia episodes. Clinical trials registration: www.isrctn.org: ISRCTN52164803 and https://eudract.ema.europa.eu: EudraCT2009-015396-27. Graphical abstract: [Figure not available: see fulltext.]. (c) 2022, The Author(s)

Essays in Financial Economics

In my dissertation, I dive into three specific areas in financial economics. Chapter 1 of my Ph.D. dissertation studies how the boom in off-exchange trading at the market close affects the close price discovery. In recent years, investment banks like Goldman Sachs have started a guaranteed close business where investors looking to buy or sell shares of a certain stock can get a guarantee from the bank to execute their orders at the close price set on the primary exchange. Using the TAQ data and a quasi-experimental shock from NYSE fee cut, we find that when the fraction of trades through guaranteed close increases, the informativeness of close price increases. We develop a model where investors choose which venue to trade in. A bank conducting guaranteed close business competes with the exchange on transaction fees, and gains profit from trading strategically utilizing the order flow information. The bank\u27s trading activity concentrates the price-relevant information into the exchange. Consequently, the guaranteed close improves price discovery at the market close. Chapter 2 of my Ph.D. dissertation studies the long-term effects of experiencing high levels of job demands on the aging and mortality of CEOs. The estimation exploits variation in industry crises and takeover protection. First, we apply neural-network based ML techniques to assess visible signs of aging in pictures of CEOs. We estimate that exposure to a distress shock during the Great Recession increases CEOs\u27 apparent age by one year over the next decade. Second, using hand-collected data on the dates of birth and death for 1,605 CEOs of large, publicly-listed U.S. firms, we estimate the resulting changes in mortality. The hazard estimates indicate that CEOs\u27 lifespandecreases by 1.5 years in response to an industry-wide downturn, and increases by two years when insulated from market discipline via anti-takeover laws. Our findings imply significant health costs of managerial stress, also relative to known health risks. Chapter 3 of my Ph.D. dissertation provides an economically interpretable and easy-to-calculate approximation to optimal portfolio choice over the life cycle. The standard literature that solves the numerical optimal portfolio policy requires complicated backward induction, making it hard to apply for providing financial advice. Real-world financial advisors, on the other hand, tend to neglect the risky nature of human capital and offer advice that is not truly optimal. We bridge the gap by first using a reduced-form regression to predict discount rates of future incomes over an agent\u27s life. Our prediction method achieves an R-squared of more than 90% over a wide range of simulations. Furthermore, by plugging the discount rates we predict into Merton (1969) formula, we obtain an approximate solution that has an average difference within 2% when compared to optimal solution solved through backward induction

The dawn phenomenon in type 2 diabetes: How to assess it in clinical practice?

International audienceAIM : The study was aimed at determining whether the dawn phenomenon in type 2 diabetes (T2D) can be predicted and quantified using simple and easily accessible glucose determinations.METHODS : A total of 210 non-insulin-treated persons with T2D underwent continuous glucose monitoring (CGM). The dawn phenomenon was quantified as the absolute increment from the nocturnal glucose nadir to the pre-breakfast value (Δdawn, mg/dL). Pre-lunch (preL) and pre-dinner (preD) glucose, and their averaged values (preLD), were compared with the nocturnal nadir. These pre-meal values were subtracted from the pre-breakfast values. The differences obtained (Δpre-mealL, Δpre-meal D and Δpre-meal LD) were correlated with Δdawn values. The receiver operating characteristic (ROC) curve was used to select the optimal Δpre-meal value that best predicted a dawn phenomenon, set at a threshold of 20mg/dL.RESULTS : All pre-meal glucose levels and differences from pre-breakfast values (Δpre-meal) significantly correlated (P<0.0001) with the nocturnal nadir and Δdawn values, respectively. The strongest correlations were observed for the parameters averaged at preL and preD time points: r=0.83 for preLD and r=0.58 for Δpre-meal LD. ROC curve analysis indicated that the dawn phenomenon at a threshold of 20mg/dL can be significantly predicted by a Δpre-meal LD cut off value of 10mg/dL. The relationship between Δdawn (Y, mg/dL) and Δpre-meal LD (X, mg/dL) was Y=0.49 X+15.CONCLUSION : The self-monitoring of preprandial glucose values at the three main mealtimes can predict the presence/absence of the dawn phenomenon, and permits reliable assessment of its magnitude without requiring continuous overnight glucose monitoring