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1. Semiparametric regression analysis for alternating recurrent event data

   https://doi.org/10.1002/sim.7563  

   Lee, Chi Hyun; Huang, Chiung-Yu; Xu, Gongjun; Luo, Xianghua (March 2018, Statistics in Medicine)
2. Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

   https://doi.org/10.1186/s12911-021-01462-5  

   Rabby, Md Fazle; Tu, Yazhou; Hossen, Md Imran; Lee, Insup; Maida, Anthony S.; Hei, Xiali (December 2021, BMC Medical Informatics and Decision Making)

   Abstract Background Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used. Methods In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error. Results For the OhioT1DM (2018) dataset, containing eight weeks’ data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 min and 60 min of prediction horizon (PH), respectively. Conclusions To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings—the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management. 

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3. Detection as Regression: Certified Object Detection with Median Smoothing

   Chiang, Ping-Yeh; Curry, Michael; Abdelkader, Ahmed; Kumar, Aounon; Dickerson, John; Goldstein, Tom (December 2020, Advances in neural information processing systems)

   null (Ed.)
4. Smoothing and Interpolating Noisy GPS Data with Smoothing Splines

   https://doi.org/10.1175/JTECH-D-19-0087.1  

   Early, Jeffrey J.; Sykulski, Adam M. (March 2020, Journal of Atmospheric and Oceanic Technology)

   A comprehensive method is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen a priori from physical reasoning. We also show how to allow for non-Gaussian noise and outliers that are typical in global positioning system (GPS) signals. We demonstrate the effectiveness of our methods on GPS trajectory data obtained from oceanographic floating instruments known as drifters. 

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5. Unmixing biological fluorescence image data with sparse and low-rank Poisson regression

   https://doi.org/10.1093/bioinformatics/btad159  

   Wang, Ruogu; Lemus, Alex A.; Henneberry, Colin M.; Ying, Yiming; Feng, Yunlong; Valm, Alex M.; Peng, ed., Hanchuan (March 2023, Bioinformatics)

   Abstract MotivationMultispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (i) as the number of fluorophores used in any experiment increases and (ii) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance. ResultsWe propose a regularized sparse and low-rank Poisson regression unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. First, SL-PRU implements multipenalty terms when pursuing sparseness and spatial correlation of the resulting abundances in small neighborhoods simultaneously. Second, SL-PRU makes use of Poisson regression for unmixing instead of least squares regression to better estimate photon abundance. Third, we propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge of the ground truth abundance information in a recorded image. By validating on simulated and real-world images, we show that our proposed method leads to improved accuracy in unmixing fluorophores with highly overlapping spectra. Availability and implementationThe source code used for this article was written in MATLAB and is available with the test data at https://github.com/WANGRUOGU/SL-PRU. 

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