With the explosive growth of data traffic and the ubiquitous connectivity of wireless devices, the energy demands of wireless networks have inevitably escalated. Reconfigurable intelligent surfaces (RIS) have emerged as a promising solution for 6G networks due to their energy efficiency (EE) and low cost, while cell-free massive multiple-input multiple-output (CF mMIMO) has been proposed as an innovative network architecture without fixed cell boundaries to enhance these measures even further. However, existing studies often assume consistently high traffic loads, neglecting the dynamic nature of user demand. This can result in underutilized access points (APs) and unnecessary energy expenditure during low-demand periods. To tackle the challenge of EE in CF mMIMO systems under low-load conditions, this paper proposes a novel energy-efficient transmission scheme that jointly coordinates active APs and multiple passive RISs. Specifically, a dynamic AP sleep-mode strategy is designed, where certain APs are selectively deactivated while nearby RISs assist in maintaining coverage. To maximize EE, we formulate the EE maximization as a fractional programming problem and adopt the Dinkelbach method in conjunction with alternating optimization (AO) to iteratively solve the coupled subproblems: (i) AP selection via a hybrid branch-and-bound (BnB) and greedy algorithm, and (ii) RIS phase-shift optimization using gradient projection. Additionally, transmit power is allocated to users through a heuristic zero-forcing strategy. Simulation results show that the proposed scheme achieves significantly higher EE than existing methods in both low and moderate user scenarios.

Precise estimation of treatment effects is crucial for accurately evaluating the intervention. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE), a major limitation in most of these models is that they often overlook the diversity of treatment effects across potential subgroups that have varying treatment effects and characteristics, treating the entire population as a homogeneous group. This limitation restricts the ability to precisely estimate treatment effects and provide targeted treatment recommendations. In this paper, we propose a novel treatment effect estimation model, named SubgroupTE, which incorporates subgroup identification in TEE. SubgroupTE identifies heterogeneous subgroups with different responses and more precisely estimates treatment effects by considering subgroup-specific treatment effects in the estimation process. In addition, we introduce an expectation-maximization (EM)-based training process that iteratively optimizes estimation and subgrouping networks to improve both estimation and subgroup identification. Comprehensive experiments on the synthetic and semi-synthetic datasets demonstrate the outstanding performance of SubgroupTE compared to the existing works for treatment effect estimation and subgrouping models. Additionally, a real-world study demonstrates the capabilities of SubgroupTE in enhancing targeted treatment recommendations for patients with opioid use disorder (OUD) by incorporating subgroup identification with treatment effect estimation.

Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.

Abstract Attention mechanism has recently aroused increasing concerns in the field of computer vision like Action Unit (AU) detection. Because facial AU exists in a fixed local area of a human face, it is advantageous to apply the attention mechanism to AU detection. A Dual‐Attention Guided Network (DAGNet) is proposed for automatically AU detection, which introduces dual attention to selectively extract deep features. Dual attention refers to predefined explicitly models feature dependencies from spatial and channel attention, respectively, based on the semantics of AU label. In addition, since the global and local features show different facial attributes and supplement mutually, the proposed DAGNet learns feature representations from global and local perspectives, respectively. Learning global and local features simultaneously during training can lead to better generalization performance; a fusion module is designed for aggregating all the learned information to construct a unified architecture for end‐to‐end AU detection. Extensive experiments on two challenging datasets, BP4D and DISFA, result in F1‐scores of 64.0% and 62.6%, respectively, which shows that the proposed DAGNet achieves the performance of the state‐of‐the‐art in the field of image‐based AU detection.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) is a resource-intensive global pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive patients who need timely health care. This study was conducted to predict the hospitalization of older adults who have tested positive for COVID-19. METHODS: We screened all patients with COVID test records from 11 Mass General Brigham hospitals to identify the study population. A total of 1495 patients with age 65 and above from the outpatient setting were included in the final cohort, among which 459 patients were hospitalized. We conducted a clinician-guided, 3-stage feature selection, and phenotyping process using iterative combinations of literature review, clinician expert opinion, and electronic healthcare record data exploration. A list of 44 features, including temporal features, was generated from this process and used for model training. Four machine learning prediction models were developed, including regularized logistic regression, support vector machine, random forest, and neural network. RESULTS: All 4 models achieved area under the receiver operating characteristic curve (AUC) greater than 0.80. Random forest achieved the best predictive performance (AUC = 0.83). Albumin, an index for nutritional status, was found to have the strongest association with hospitalization among COVID positive older adults. CONCLUSIONS: In this study, we developed 4 machine learning models for predicting general hospitalization among COVID positive older adults. We identified important clinical factors associated with hospitalization and observed temporal patterns in our study cohort. Our modeling pipeline and algorithm could potentially be used to facilitate more accurate and efficient decision support for triaging COVID positive patients.

Youri Xu, Haihong E, Meina Song, Wenyu Song, Xiaodong Lv, Wang Haotian, Yang Jinrui. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

OBJECTIVE: Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data. METHODS: We utilized rich electronic health record data, including full assessment records entered by nurses, from 5 different hospitals affiliated with a large integrated healthcare organization to develop machine learning-based prediction models for pressure injury. Five-fold cross-validation was conducted to evaluate model performance. RESULTS: Two pressure injury phenotypes were defined for model development: nonhospital acquired pressure injury (N = 4398) and hospital acquired pressure injury (N = 1767), representing 2 distinct clinical scenarios. A total of 28 clinical features were extracted and multiple machine learning predictive models were developed for both pressure injury phenotypes. The random forest model performed best and achieved an AUC of 0.92 and 0.94 in 2 test sets, respectively. The Glasgow coma scale, a nurse-entered level of consciousness measurement, was the most important feature for both groups. CONCLUSIONS: This model accurately predicts pressure injury development and, if validated externally, may be helpful in widespread pressure injury prevention.