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1. A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills

   https://doi.org/10.1093/biosci/biae089  

   Lofton, Mary_E; Moore, Tadhg_N; Woelmer, Whitney_M; Thomas, R_Quinn; Carey, Cayelan_C (October 2024, BioScience)

   Abstract Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates. 

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2. Improving Students' Daily Life Stress Forecasting using LSTM Neural Networks

   Umematsu, T; Sano, A; Taylor, S; Picard, R. (January 2019, IEEE-EMBS Biomedical and Health Informatics 2019)

   Accurately forecasting stress may enable people to make behavioral changes that could improve their future health. For example, accurate stress forecasting might inspire people to make changes to their schedule to get more sleep or exercise, in order to reduce excessive stress tomorrow night. In this paper, we examine how accurately the previous N-days of multi-modal data can forecast tomorrow evening’s high/low binary stress levels using long short-term memory neural network models (LSTM), logistic regression (LR), and support vector machines (SVM). Using a total of 2,276 days, with 1,231 overlapping 8-day sequences of data from 142 participants (including physiological signals, mobile phone usage, location, and behavioral surveys), we find the LSTM significantly outperforms LR and SVM with the best results reaching 83.6% using 7 days of prior data. Using time-series models improves the forecasting of stress even when considering only subsets of the multi-modal data set, e.g., using only physiology data. In particular, the LSTM model reaches 81.4% accuracy using only objective and passive data, i.e., not including subjective reports from a daily survey. 

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3. Selecting and Composing Learning Rate Policies for Deep Neural Networks

   https://doi.org/10.1145/3570508  

   Wu, Yanzhao; Liu, Ling (April 2023, ACM Transactions on Intelligent Systems and Technology)

   The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training time of Deep Neural Networks (DNNs). This article presents a systematic approach to selecting and composing an LR policy for effective DNN training to meet desired target accuracy and reduce training time within the pre-defined training iterations. It makes three original contributions. First, we develop an LR tuning mechanism for auto-verification of a given LR policy with respect to the desired accuracy goal under the pre-defined training time constraint. Second, we develop an LR policy recommendation system (LRBench) to select and compose good LR policies from the same and/or different LR functions through dynamic tuning, and avoid bad choices, for a given learning task, DNN model, and dataset. Third, we extend LRBench by supporting different DNN optimizers and show the significant mutual impact of different LR policies and different optimizers. Evaluated using popular benchmark datasets and different DNN models (LeNet, CNN3, ResNet), we show that our approach can effectively deliver high DNN test accuracy, outperform the existing recommended default LR policies, and reduce the DNN training time by 1.6-6.7× to meet a targeted model accuracy. 

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4. Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification

   https://doi.org/10.1109/bhi50953.2021.9508527  

   Le, Minh Duc; Singh Rathour, Vidhiwar; Truong, Quang Sang; Mai, Quan; Brijesh, Patel; Le, Ngan (July 2021, 2021 IEEE International Conference on Biomedical and Health Informatics (BHI))

   The automatic classification of electrocardiogram (ECG) signals has played an important role in cardiovascular diseases diagnosis and prediction. Deep neural networks (DNNs), particularly Convolutional Neural Networks (CNNs), have excelled in a variety of intelligent tasks including biomedical and health informatics. Most the existing approaches either partition the ECG time series into a set of segments and apply 1D-CNNs or divide the ECG signal into a set of spectrogram images and apply 2D-CNNs. These studies, however, suffer from the limitation that temporal dependencies between 1D segments or 2D spectrograms are not considered during network construction. Furthermore, meta-data including gender and age has not been well studied in these researches. To address those limitations, we propose a multi-module Recurrent Convolutional Neural Networks (RCNNs) consisting of both CNNs to learn spatial representation and Recurrent Neural Networks (RNNs) to model the temporal relationship. Our multi-module RCNNs architecture is designed as an end-to-end deep framework with four modules: (i) timeseries module by 1D RCNNs which extracts spatio-temporal information of ECG time series; (ii) spectrogram module by 2D RCNNs which learns visual-temporal representation of ECG spectrogram ; (iii) metadata module which vectorizes age and gender information; (iv) fusion module which semantically fuses the information from three above modules by a transformer encoder. Ten-fold cross validation was used to evaluate the approach on the MIT-BIH arrhythmia database (MIT-BIH) under different network configurations. The experimental results have proved that our proposed multi-module RCNNs with transformer encoder achieves the state-of-the-art with 99.14% F1 score and 98.29% accuracy. 

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5. Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022–2023 mpox epidemic

   https://doi.org/10.1098/rsos.240248  

   Bleichrodt, Amanda; Luo, Ruiyan; Kirpich, Alexander; Chowell, Gerardo (July 2024, Royal Society Open Science)

   During the 2022–2023 unprecedented mpox epidemic, near real-time short-term forecasts of the epidemic’s trajectory were essential in intervention implementation and guiding policy. However, as case levels have significantly decreased, evaluating model performance is vital to advancing the field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention and Our World in Data teams, we generated retrospective sequential weekly forecasts for Brazil, Canada, France, Germany, Spain, the United Kingdom, the United States and at the global scale using an auto-regressive integrated moving average (ARIMA) model, generalized additive model, simple linear regression, Facebook’s Prophet model, as well as the sub-epidemic wave andn-sub-epidemic modelling frameworks. We assessed forecast performance using average mean squared error, mean absolute error, weighted interval scores, 95% prediction interval coverage, skill scores and Winkler scores. Overall, then-sub-epidemic modelling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best most frequently. Then-sub-epidemic and spatial-wave frameworks considerably improved in average forecasting performance relative to the ARIMA model (greater than 10%) for all performance metrics. Findings further support sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases. 

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