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1. Forecasting Health and Wellbeing for Shift Workers Using Job-Role Based Deep Neural Network

   Yu, Han; Itoh, Asami; Sakamoto, Ryota; Shimaoka, Motomu; Sano, Akane. (February 2021, International Conference on Wireless Mobile Communication and Healthcare MobiHealth 2020: Wireless Mobile Communication and Healthcare)

   null (Ed.)

   Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and behavioral wearable sensor data from shift working nurses and doctors, as well as their behavioral questionnaire data and their self-reported daily health and wellbeing labels, including alertness, happiness, energy, health, and stress. We found the similarities and differences between the responses of nurses and doctors. According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants’ next day’s multidimensional self-reported health and wellbeing status. Our model showed significantly better performances than baseline models and previous state-of-the-art models in the evaluations of binary/3-class classification and regression prediction tasks. We also found features related to heart rate, sleep, and work shift contributed to shift workers’ health and wellbeing. 

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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. Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics

   https://doi.org/10.2196/55530  

   Washington, Peter (January 2025, JMIR AI)

   Digital health interventions often use machine learning (ML) models to make predictions of repeated adverse health events. For example, models may be used to analyze patient data to identify patterns that can anticipate the likelihood of disease exacerbations, enabling timely interventions and personalized treatment plans. However, many digital health applications require the prediction of highly heterogeneous and nuanced health events. The cross-subject variability of these events makes traditional ML approaches, where a single generalized model is trained to classify a particular condition, unlikely to generalize to patients outside of the training set. A natural solution is to train a separate model for each individual or subgroup, essentially overfitting the model to the unique characteristics of the individual without negatively overfitting in terms of the desired prediction task. Such an approach has traditionally required extensive data labels from each individual, a reality that has rendered personalized ML infeasible for precision health care. The recent popularization of self-supervised learning, however, provides a solution to this issue: by pretraining deep learning models on the vast array of unlabeled data streams arising from patient-generated health data, personalized models can be fine-tuned to predict the health outcome of interest with fewer labels than purely supervised approaches, making personalization of deep learning models much more achievable from a practical perspective. This perspective describes the current state-of-the-art in both self-supervised learning and ML personalization for health care as well as growing efforts to combine these two ideas by conducting self-supervised pretraining on an individual’s data. However, there are practical challenges that must be addressed in order to fully realize this potential, such as human-computer interaction innovations to ensure consistent labeling practices within a single participant. 

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4. Personalized modeling of stress and blood pressure reactivity using mobile health data

   https://doi.org/10.1038/s44184-026-00210-9  

   Kargarandehkordi, Ali; Jaiswal, Aditi; Banerjee, Agnik; Qian, Yang; Slade, Christopher R; Sun, Yinan; Islam, Tanvir; Tadesse, Hiwot Belay; Kostrinsky-Thomas, Alexander; Park, Chanhyun; et al (May 2026, npj Mental Health Research)

   Abstract Psychological stress is a key driver of short-term blood pressure (BP) elevations and cardiovascular risk, yet its moment-to-moment impact in daily life remains difficult to predict. In this longitudinal observational study, we collected multimodal data from 20 adults with self-reported hypertension, including continuous wearable-derived heart rate and activity, ecological momentary assessment (EMA) stress ratings, and ambulatory BP measurements in free-living conditions. The dataset comprised 3694 EMA responses and 3812 BP measurements collected over approximately four weeks per participant (mean 24.1 ± 8.5 days). We evaluated whether participant-specific (“personalized”) models outperform a single pooled population model. Two prediction tasks were examined: (i) prediction of near-term BP elevations from wearable signals and stress EMA responses and (ii) prediction of self-reported stress from wearable signals and BP. Across both tasks, personalized models consistently improved predictive performance. For BP prediction, personalized models achieved a mean AUROC of 0.803, exceeding the population model by 0.235, while for stress prediction they achieved a mean AUROC of 0.849, exceeding the population model by 0.208. These findings suggest that personalized wearable-based models can capture individual patterns of stress and BP dynamics, with direct implications for precision mental health assessment and just-in-time adaptive intervention design in future work. 

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5. Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks

   https://doi.org/10.1038/s41598-023-27418-5  

   Shickel, Benjamin; Loftus, Tyler J.; Ruppert, Matthew; Upchurch, Gilbert R.; Ozrazgat-Baslanti, Tezcan; Rashidi, Parisa; Bihorac, Azra (December 2023, Scientific Reports)

   Abstract Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machine learning models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests and XGBoost for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data. Our study indicated several significant results across experimental settings that suggest the utility of deep learning for capturing more precise representations of patient health for augmented surgical decision support. Multi-task learning improved efficiency by reducing computational resources without compromising predictive performance. Integrated gradients interpretability mechanisms identified potentially modifiable risk factors for each complication. Monte Carlo dropout methods provided a quantitative measure of prediction uncertainty that has the potential to enhance clinical trust. Multi-task learning, interpretability mechanisms, and uncertainty metrics demonstrated potential to facilitate effective clinical implementation. 

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