Jiajia Li

Exercise is a foundational intervention for preventing and managing chronic diseases, yet substantial inter-individual variation in physiological and molecular responses limits translation into personalized clinical practice. Meanwhile, wearables, multi-omics profiling, imaging phenotypes, and large-scale cohorts are generating multi-scale data that characterize exercise responses from molecular programs to real-world behavior. Artificial intelligence (AI) offers computational tools to integrate these modalities, learn predictive representations, and derive clinically relevant phenotypes. In this review, we summarize recent advances in AI-enabled translational exercise biomedicine. We first describe the evolving data ecosystem, including wearable-derived digital phenotypes, imaging biomarkers, and exercise-responsive multi-omics signatures. We then discuss key modeling paradigms – time-series learning, multimodal fusion, causal inference, and reinforcement learning – with emphasis on their roles in response prediction and personalized intervention design. We highlight emerging applications across cardiometabolic disease, metabolic liver disease, neurodegeneration, cancer survivorship, and chronic kidney disease. We also delineate persistent barriers to translation, including data heterogeneity, reliability under distribution shift, interpretability and uncertainty communication, and regulatory and ethical constraints. We argue that progress will depend on frameworks that link mechanistic insight with scalable digital monitoring and safety-aware adaptive prescriptions, enabling precision exercise biomedicine to move beyond empirical guidelines toward evidence-based, individualized decision-making.

Background: Medical abstract sentence classification is crucial for enhancing medical database searches, literature reviews, and generating new abstracts. However, Chinese medical abstract classification research is hindered by a lack of suitable datasets. Given the vastness of Chinese medical literature and the unique value of traditional Chinese medicine, precise classification of these abstracts is vital for advancing global medical research. Objective: This study aims to address the data scarcity issue by generating a large volume of labeled Chinese abstract sentences without manual annotation, thereby creating new training datasets. Additionally, we seek to develop more accurate text classification algorithms to improve the precision of Chinese medical abstract classification. Methods: We developed 3 training datasets (dataset #1, dataset #2, and dataset #3) and a test dataset to evaluate our model. Dataset #1 contains 15,000 abstract sentences translated from the PubMed dataset into Chinese. Datasets #2 and #3, each with 15,000 sentences, were generated using GPT-3.5 from 40,000 Chinese medical abstracts in the CSL database. Dataset #2 used titles and keywords for pseudolabeling, while dataset #3 aligned abstracts with category labels. The test dataset includes 87,000 sentences from 20,000 abstracts. We used SBERT embeddings for deeper semantic analysis and evaluated our model using clustering (SBERT-DocSCAN) and supervised methods (SBERT-MEC). Extensive ablation studies and feature analyses were conducted to validate the model’s effectiveness and robustness. Results: Our experiments involved training both clustering and supervised models on the 3 datasets, followed by comprehensive evaluation using the test dataset. The outcomes demonstrated that our models outperformed the baseline metrics. Specifically, when trained on dataset #1, the SBERT-DocSCAN model registered an impressive accuracy and F₁-score of 89.85% on the test dataset. Concurrently, the SBERT-MEC algorithm exhibited comparable performance with an accuracy of 89.38% and an identical F₁-score. Training on dataset #2 yielded similarly positive results for the SBERT-DocSCAN model, achieving an accuracy and F₁-score of 89.83%, while the SBERT-MEC algorithm recorded an accuracy of 86.73% and an F₁-score of 86.51%. Notably, training with dataset #3 allowed the SBERT-DocSCAN model to attain the best with an accuracy and F₁-score of 91.30%, whereas the SBERT-MEC algorithm also showed robust performance, obtaining an accuracy of 90.39% and an F₁-score of 90.35%. Ablation analysis highlighted the critical role of integrated features and methodologies in improving classification efficiency. Conclusions: Our approach addresses the challenge of limited datasets for Chinese medical abstract classification by generating novel datasets. The deployment of SBERT-DocSCAN and SBERT-MEC models significantly enhances the precision of classifying Chinese medical abstracts, even when using synthetic datasets with pseudolabels.

Academic data processing is crucial in scientometrics and bibliometrics, such as research trending analysis and citation recommendation. Existing datasets in this domain have predominantly concentrated on textual data, overlooking the importance of visual elements. To bridge this gap, we introduce a multidisciplinary multimodal aligned dataset (MMAD) specifically designed for academic data processing. This dataset encompasses over 1.1 million peer-reviewed scholarly articles, enhanced with metadata and visuals that are aligned with the text. We assess the representativeness of MMAD by comparing its country/region distribution against benchmarks from SCImago. Furthermore, we propose an innovative quality validation method for MMAD, leveraging Language Model-based techniques. Utilizing carefully crafted prompts, this approach enhances multimodal processing capabilities to evaluate the accuracy of text-to-visual alignments. We also outline prospective applications for MMAD, providing the way for novel research endeavors, including automated caption generation and analysis of trends in figures. Thus, this work signals new research prospects and provides a fertile ground for advances in academic data processing.

Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global temporal information. Second, the correlation for special waves in different signals is rarely used in sleep staging modeling. Third, the logic of scoring rules based on adjacent epochs is not considered in developing sleep staging models. This paper introduces a multimodal hybrid-embedding fusion network (MHFNet), which aims to tackle these challenges in automating sleep stage scoring. MHFNet comprises multi-stream Xception blocks to extract wave characteristics, a hybrid time-embedding module to combine local and global temporal information, a dual-path gate transformer to fuse and enhance attention features, and a refined output header to reconstruct sleep scoring. We perform experiments using three publicly available datasets (SleepEDF-ST, SleepEDF-SC, and SHHS). Experimental results indicate the superiority of MHFNet over baseline approaches in cross-validation. Moreover, at the individual level, MHFNet yielded an average R 2 score improvement of 9% in the testing dataset compared to state-of-the-art models, paving the way for its applications in real-world sleep medicine.

Understanding and addressing the dynamics of infectious diseases, such as coronavirus disease 2019, are essential for effectively managing the current situation and developing intervention strategies. Epidemiologists commonly use mathematical models, known as epidemiological equations (EE), to simulate disease spread. However, accurately estimating the parameters of these models can be challenging due to factors like variations in social distancing policies and intervention strategies. In this study, we propose a novel method called deep dynamic epidemiological modeling (DDE) to address these challenges. The DDE method combines the strengths of EE with the capabilities of deep neural networks to improve the accuracy of fitting real-world data. In DDE, we apply neural ordinary differential equations to solve variant-specific equations, ensuring a more precise fit for disease progression in different geographic regions. In the experiment, we tested the performance of the DDE method and other state-of-the-art methods using real-world data from five diverse geographic entities: the USA, Colombia, South Africa, Wuhan in China, and Piedmont in Italy. Compared to the state-of-the-art method, DDE significantly improved accuracy, with an average fitting Pearson coefficient exceeding 0.97 across the five geographic entities. In summary, the DDE method enhances the accuracy of parameter fitting in epidemiological models and provides a foundation for constructing simpler models adaptable to different geographic areas.

Multivariate time series (MTS) forecasting is a crucial aspect in many classification and regression tasks. In recent years, deep learning models have become the mainstream framework for MTS forecasting. Among these deep learning methods, the transformer model has been proved particularly effective due to its ability to capture long- and short-term dependencies. However, the computational complexity of transformer-based models sets the obstacles for resource-constrained scenarios. To address this challenge, we propose a novel and efficient Skip-RCNN network that incorporates Skip-RNN and Skip-CNN modules to split the MTS into multiple frames with various time intervals. Thanks to the skipping process of Skip-RNN and Skip-CNN, the resulting network could process information with different reception field together and achieves better performance than the state-of-the-art network. We conducted comparative experiments using our proposed method and six baseline models on seven publicly available datasets. The results demonstrate that our model outperforms other baseline methods in accuracy under most conditions and surpasses the transformer-based model with 0.098 for a short interval and 0.068 for a long interval. Our Skip-RCNN network presents a promising approach to MTS forecasting that can meet the demands of resource-constrained prediction scenarios.

In the field of artificial intelligence for information technology operations, operational data are often modeled as aperiodic multivariate time series, which contain rich multidimensional and nonlinear patterns. However, the existing approaches are unable to effectively acquire knowledge and recognize patterns due to their reliance on processing and modeling periodic patterns. To address this issue, this article proposes a novel deep-saliency-aware dual embedded attention network for aperiodic multivariate time-series forecasting. Our network consists of three main components: 1) a convolutional-neural-network- and transformer-based component for saliency representation of the aperiodic patterns; 2) a lightweight recurrent neural network component for capturing long-term dependence features; and 3) an attention mechanism for fusing the latent representations from the former components. Extensive empirical studies are conducted on a real-world dataset and five other public datasets to evaluate the proposed network against four state-of-the-art models. The results show that our method achieves impressive high performance on most evaluation metrics. Furthermore, the data and code used in this study are publicly available, which can facilitate progress in the community.

Understanding and effectively addressing the dynamics of infectious diseases, including global diseases like COVID-19, is crucial for managing the current situation and developing effective intervention strategies. Epidemiologists commonly use epidemiological equations (EE) to model disease progression. Nevertheless, the traditional approach to parameter estimation in EE frequently faces challenges in accurately fitting real-world data, primarily due to factors like the different implementation of social distancing policies and intervention strategies. However, developing high-quality but complex EE models can be time-intensive for epidemiologists. Hence, we introduce a novel method known as the deep dynamic epidemiological (DDE) approach, which integrates the strengths of EE with the capabilities of deep neural networks (DNN) to enhance accuracy. The DDE method incorporates DNNs to model dynamic effects and adapt to evolving situations. It employs the neural ordinary differential equation (ODE) method to solve variant-specific equations, ensuring a precise fit to disease progression in diverse geographic regions. In this study, we introduce four variants of EE customized to address specific scenarios in different countries and regions. We assess the performance of our DDE method using real-world data from five diverse geographic entities (countries: the USA, Colombia, and South Africa; regions: Wuhan in China and Piedmont in Italy). We show that the DDE method outperforms alternative approaches, achieving the highest predictive accuracy in modeling disease progression across all five geographic entities. This paves the way for constructing a simplified EE model for various geographic levels.