Reviewerly/exHarmony huggingface dataset:
A method of human motion reconstruction with sparse joints based on attention mechanism

Motion capture has significant applications in fields such as rehabilitation, virtual reality, and beyond. The cost-effective utilization of sparse sensors is crucial. The central challenge lies in achieving full-body motion estimation with a reduced sensor count. This paper proposes a neural network model based on attention mechanism for motion reconstruction with sparse joints. Compared to traditional methods, our method places greater emphasis on the spatial characteristics of motion sequences by using spatial attention, thus having lower reconstruction error, while preserving high visual quality. Furthermore, our method employs two encoders, with each being responsible for motion feature extraction and motion reconstruction, respectively, thereby enhancing robustness when dealing with new data. Experiments also show that our method can handle missing markers problem with low error.
Construction of a Traditional Chinese Medicine Dao Yin Science Knowledge Graph Based on Neo4j<sup>*</sup>

Construct a Knowledge Graph based on the characteristics of Dao Yin Science in Traditional Chinese Medicine to facilitate users in querying, learning, and disseminating the culture of health maintenance in Traditional Chinese Medicine, and to enhance their health literacy. Using the Bert-CRF model, with manual proofreading, we conducted named entity recognition on literature related to Dao Yin practice, established relationships between entities based on the characteristics of the subject and built a Knowledge Graph for Dao Yin Science using Neo4j. Incorporating 3152 relevant documents, the constructed knowledge graph contains 2262 entity nodes, 5108 relationship data, 7 types of entity attribute categories, and 7 types of relationships. The knowledge graph can be searched using the Cypher query language of Neo4j. This Knowledge Graph visualizes the knowledge of Dao Yin Science in Traditional Chinese Medicine and can be employed to build applications and web platforms for knowledge retrieval, intelligent question answering, and exercise recommendation. It holds practical value for developing related artificial intelligence devices and fostering innovation in the fields of computer science and automation. Moreover, it can be utilized for in-depth data mining and uncovering new research directions.
MVP: Optimizing Multi-view Prompts for Medical Dialogue Summarization

Medical dialogue summarization (MDS) is commonly known as summarizing patients’ electronic health records (EHRs) from doctor-patient dialogues, and the automating of this task can significantly liberate doctors from trivial recordings. Recently, state-of-the-art abstractive summarization systems in open domains are typically adapted from pre-trained language models (PLMs), providing a promising paradigm for MDS to follow. However, such large models with millions of parameters tend to overfit the limited number of training samples available from the medical community. This makes the generated EHRs always contain hallucinatory facts that never appeared in the input dialogue. To address these problems, we propose MVP, a prompt learning method with multi-view prompts to adapt large PLMs for medical dialogue summarization. It learns from the input dialogue: a single input-specific prompt (InP) captures the global dialogue features to ensure faithful results, and several distinct item-specific prompts (ItPs) extract local dialogue information for prompting the generation of each EHR item. We freeze all PLM parameters and only tune the two types of prompts under a multi-level contrastive learning framework, thus effectively avoiding the overfitting problem. Experimental results on two public datasets demonstrate the superior performances of our method.
Multimodal reasoning for nutrition and human health via knowledge graph embedding

The established links between nutrition and human health are widely acknowledged. Dietary nutrients play a crucial role in regulating gut microbial communities, influencing various human diseases. With a growing number of related studies, there’s a need to systematically organize these associations for coherent knowledge reasoning. However, due to the diverse and extensive nature of the knowledge landscape, significant challenges persist. To address this, we propose an approach using multimodal data and knowledge embeddings for effective knowledge reasoning in nutrition and human health. We create a comprehensive knowledge graph, KG4NH, covering dietary nutrition, gut microbiota, and human diseases. To ensure efficient knowledge representation, we employ knowledge embedding techniques to develop modality-specific encoders for structure, category, and description. Additionally, we introduce a mul-timodal fusion method to capture shared information across modalities. Our experimental results demonstrate the superiority of our approach over other state-of-the-art methods.
Patient-Centered Data Sharing and Revision Framework Based on Redactable Blockchain

Secure and efficient healthcare data sharing plays a key role in cross-enterprise document sharing (XDS). However, security management for healthcare data is equally important. The emergence of blockchain technology is a great solution for healthcare data sharing. However, the problem that incorrect messages are permanently preserved in the blockchain must be considered. Fortunately, the redactable blockchain is an effective solution. The redactable blockchain allows for modifying incorrect data stored on the blockchain. In this paper, we propose a safe, effective, and patient-centered medical data sharing and revision framework based on a redactable blockchain. Firstly, we leverage ciphertext-policy attribute-based encryption (CP-ABE) scheme to enable data-sharing on blockchain. Then, we introduce the chameleon hash function to achieve a data revision mechanism that enables the modification of incorrect historical medical records. At the same time, we adopt a verifiable secret sharing (VSS) scheme to decentralize the revision privilege in order to eliminate the single-point failure problem. Further, we show the feasibility and practicality of our framework by conducting comprehensive experiments.
EGDE: A Framework for Bridging the Gap in Medical Zero-shot Relation Triplet Extraction

Medical zero-shot relation triplet extraction, referred to as Med-ZeroRTE, requires the model to extract triplets comprising entities and relations from medical sentences. Importantly, the sentences include relations that were unseen during the model’s training phase. While Med-ZeroRTE had not been formally explored before this work, the limited availability of medical datasets, influenced by privacy concerns and annotation costs, emphasizes the necessity of exploring Med-ZeroRTE. This exploration faces two main challenges: Firstly, there is a gap of work specifically focused on triplet extraction from medical text in a zero-shot setting. Secondly, while a few approaches tackle the general zero-shot problems by employing generative models to produce synthetic data for unseen classes, the quality of some synthetic data remains suboptimal. Therefore, we propose a novel Enhanced Generator - Discriminator - Extractor framework (EGDE), which consists of three core modules, a prompt-tuned generator for generating synthetic samples given unseen relations, a fine-tuned discriminator for filtering qualified synthetic samples, a prompt-tuned extractor for extracting predicted medical triplets, to resolve Med-ZeroRTE and mitigate issues related to poor synthetic samples. The proposed framework is shown to be effective and superior compared to several robust baselines in experiments conducted on two distinct dataset settings.
Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation

The polygenic risk score has proven to be a valuable tool for assessing an individual's genetic predisposition to phenotype (disease) within biomedicine in recent years. However, traditional regression-based methods for polygenic risk scores calculation have limitations that can impede their accuracy and predictive power. This study introduces an innovative approach to enhance polygenic risk scores calculation through the application of genetic programming. By harnessing the power of genetic programming, we aim to overcome the limitations of traditional regression techniques and improve the accuracy of polygenic risk scores predictions. Specifically, we showed that a polygenic risk score generated through Cartesian genetic programming yielded comparable or even more robust statistical distinctions between groups that we evaluated within three independent case studies.
Domain-specific Knowledge Guided Self-supervised Learning for Pathological Image Segmentation

Self-supervised learning provides a possible solution to extract effective visual representations from unlabeled pathological images. However, most of the existing methods either do not effectively utilize domain-specific information or are designed and optimized for image classification, resulting in these pre-trained models that may not be optimal for pathological image segmentation. In this paper, we propose DKSL: Domain-specific Knowledge guided Self-supervised Learning, which uses image reconstruction tasks to aid contrastive learning and exploits single-dye stained pathological images after stain separation as domain-specific knowledge to guide the model. Our method provides a novel way to exploit the domain-specific knowledge of pathological images. In contrastive learning, we add single-dye stained images as an expansion of the original positive samples to the contrastive learning process to preserve more global semantic information. In image reconstruction, the model is forced to focus on local image details relevant to downstream tasks by reconstructing single-dye stained images from the representation extracted by the encoder of contrastive learning. Finally, the encoder and decoder from the pre-training stage are fine-tuned by the downstream segmentation task. Fine-tuning experimental results demonstrate that DKSL outperforms state-of-the-art methods with Dices of 90.50% and 79.68% on two publicly available datasets, GlaS and MoNuSeg, respectively.
Using Knowledge graph and Quantum Computing to Optimize the Comprehensive Mental Health Adaptive Test System

It is important for mental health research to study the scale of the elementary and secondary school students. Methods: This study uses knowledge graph technology to strengthen the relationship between individual psychological questionnaires and various test indicators of mental health, and we develop the lightweight extraction method "Relational database to Knowledge graph" (RETG) to generate the necessary files to build the knowledge graph. We not only employ quantum search algorithm to reduce the computational complexity, but also replace multi bit quantum gate in the quantum circuit by combining single- and two-bit quantum gate. Results: We built a scalable adaptive mental health testing platform for the elementary and secondary school students; The RETG method increases the efficiency of Knowledge graph construction; The quantum computing can reduce the time complexity of construction process and we increase the accuracy of quantum acceleration algorithms on physical machines.
A Morphology Focused Cell Detection Model for Histopathology Images

The accurate automatic recognition of cell locations is of great significance for downstream tasks in pathology. Due to the various size and distribution of different cell types, previous cell detection methods applied fixed circles as labels to localize cell position by default, which makes less precise and more difficult to adapt various cell morphology. In this paper, we integrate adaptive areas of interests based on morphology to address the limitation in detecting cells with irregular shapes in pathological images. Moreover, a EGSI module is proposed to extract global distribution of cells in slices, which enables model to acquire more rich semantic information. We exhibit favorable F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of our method on four public datasets. Experimental results demonstrate that the proposed method could detect cells more accurately in those images with different cell morphologies and dense cell distribution.
HealthBridge: A Decentralized and Interoperable Healthcare App for Rare Disease Communities

Traditional centralized database systems share many weaknesses, including the overhead of sharing medical documentation, data fragmentation, and susceptibility to centralized data attacks. Those problems would especially influence rare disease patient communities given their higher need for medical data interoperability and security. In this paper, we present an alternative approach to managing healthcare data that is tailored to the rare disease community. Specifically, we design and implement HealthBridge, a decentralized and interoperable mobile application with distributed ledger technology. The underlying permissioned network utilizes Hyperledger Fabric and has a modular design, empowering efficient data transactions directly between trusted entities with predefined smart contracts that regulate access permissions. For the application design, we focus on the needs of the rare disease community, prioritizing ease of use without compromising data security. In particular, our application provides a delegate feature, aimed at supporting rare disease patients with additional needs. Through this feature, if needed, patients can grant pre-defined access permissions to delegates through smart contracts. While the overall implementation of HealthBridge is still in the developmental stages, it presents a proof of concept and a promising system to support the underfunded rare disease community in terms of healthcare interoperability through a permissioned blockchain network.
Diff-SFCT: A Diffusion Model with Spatial-Frequency Cross Transformer for Medical Image Segmentation

Most existing semantic segmentation methods primarily employ supervised learning with discriminative models. Although these methods are straightforward, they overlook the modeling of underlying data distributions. In this paper, we propose a novel medical image segmentation framework called Diff-SFCT based on Diffusion Model. We formulate semantic segmentation as a generative problem for segmentation masks, replacing the conventional pixel-wise discriminative learning with a latent prior learning process to produce more accurate segmentation results. Diff-SFCT employs a backbone network combining Convolutional Neural Network (CNN) and Transformer, and utilizes the local perception of CNN and the global information modeling capability of Transformer. In Diff-SFCT, we design a Semantic Encoder that effectively extracts fine-grained semantic features from real images. Meanwhile, we propose a novel Spatial-Frequency Cross Transformer (SFCT) framework, which can effectively model and interact the global features of the diffuse noise mask and the real semantic features, reducing the domain gap between the two and enhancing the model’s representational capacity. Additionally, to preserve spatial and frequency information in the diffusion model, we design a Spatial-Frequency Attention Module (SFAM) as part of the Convolutional Block. This module improves the model’s spatial and frequency perception abilities while incurring negligible computational overhead. Experimental results evince that our DiffSFCT substantially outperforms other segmentation methods, exhibiting remarkable performance across various medical image segmentation datasets.
Joint Biomedical Entity and Relation Extraction with Unified Interaction Maps

Automatic extraction of entities and their relations from unstructured literature to form structured triples is essential for biomedical knowledge construction. Although most existing joint methods have effectively addressed some challenging problems in the biomedical corpora, i.e., the prevalent overlapping issue, they still suffer from a lack of consideration for the intrinsic correlations between entities and relations, as well as low computational efficiency. In this paper, we present a joint entity and relation extraction model with unified interaction maps. Specifically, we concatenate all relations in the natural language form with the input text to integrate the semantic information of relations through a deep Transformer-based encoder. In addition, we apply unified interaction maps to capture the correlations, which can naturally handle the overlapping issue. Extensive experiments on the CHEMPROT and DDIExtraction2013 datasets demonstrate the effectiveness of our model, achieving the state-of-the-art performance with higher efficiency.
ChatICD: Prompt Learning for Few-shot ICD Coding through ChatGPT

Automated International Classification of Diseases (ICD) coding involves the automated assignment of diverse disease codes to clinical medical texts. It is considered as a multi-label classification task. Because most ICD codes are rare, the imbalanced distribution and small sample size issue make this task challenging. Inspired by the recent success of ChatGPT and prompt-based fine-tuning, this study proposes a model called ChatICD to address the issue of few-shot ICD coding. First, ChatGPT for data augumentation rephrases the descriptions of ICD codes into multiple samples. Then, ChatICD fine-tunes the pretrained model by generating prompt templates and label mapping words. We conduct an evaluation of ChatICD on benchmark datasets, namely MIMIC-III-50 and MIMIC-III-rare50. On the few-shot ICD coding task of MIMIC-III-rare50, ChatICD achieves macro-F1 and micro-F1 of 35.8% and 38.2% respectively, which is a 5.4% and 5.6% improvement over the current best model.
Incremental Semi-Supervised Tri-Training: A framework for model personalization

The need for real-time, tailored, and autonomous decision-making applications driven by machine learning and deep learning models has propelled the development and use of edge computing devices. Edge computing devices are characterized by the generation of large volumes of data and minimal computational resources (memory, processing capacity, etc.). In this work, we present a novel and integrated framework, called Incremental Semi-Supervised Tri-Training, for incremental update as well as personalization of models residing on edge computing devices in the absence of domain specific labelled data. The proposed framework encompasses three heavy weight neural networks working collaboratively to generate high quality pseudo-labels in a self-supervised manner to train a teacher model. The knowledge from the teacher model is then distilled into the student model residing on the edge device. The developed models are incrementally updated resulting in personalization of the edge model over time without requiring labels/annotations. We demonstrate the efficacy of the proposed framework in the context of a sleep staging application using the Physionet Sleep EDF-Expanded data repository.
LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images

Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classification accuracy of histopathology images, we propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. In order to be able to provide local field of view diagnostic results, we propose the LDCSF model, which consists of a Swin transformer module, a local depth convolution (LDC) module, a feature reconstruction (FR) module, and a ResNet module. The Swin transformer module reduces the amount of computation generated by the attention mechanism by limiting the attention to each window. The LDC then reconstructs the attention map and performs convolution operations in multiple channels, passing the resulting feature map to the next layer. The FR module uses the corresponding weight coefficient vectors obtained from the channels to dot product with the original feature map vector matrix to generate representative feature maps. Finally, the residual network undertakes the final classification task. As a result, the classification accuracy of LDCSF for interstitial area, necrosis, non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively.
An End-to-End Multi-stage Network for Ultrasound Video Object Segmentation

Real-time tracking and segmentation of ultrasound video sequence are prerequisite for identifying and analyzing lesions. While significant progress has been made in natural video object segmentation, developing a model for ultrasound video is still challenging due to problems such as low distinguishability and low visual saliency of the target objects, large variation between adjacent frames. These challenges are inherently complex and cannot be effectively tackled through a single process. This paper develops an end-to-end multi-stage network (EMNet) for ultrasound video object segmentation. EMNet consists of two stages. The inital mask generation stage comprises a contrast-enhanced layer to enhance visual contrast between targets and backgrounds. In this stage, a module that adopts the encoder-attention-decoder structure is designed for mask induction. After obtaining the initial segmentation mask, the mask refinement stage is followed to further improve initial segmentation. To prevent the propagation of errors, a gating mechanism is designed to control the fusion of segmentation probability maps in the initial and refinement stages. By transforming certain fixed parameters in different stage into trainable parameters and establishing an end-to-end learning process, we optimized the performance of our approach. We evaluate EMNet on real-world lymphoma ultrasound video dataset. Compared with the best results among seven competing baselines, EMNet achieves the best performance in terms of ℐ&ℱ and ℱ measures, the second-best performance with Param and FPS measures, which demonstrates the competitive performance in terms of both speed and accuracy.
An Attention-based Label Mapping and Multi-factor Domain Adaptation Approach for ACS Prediction

Acute Coronary Syndrome (ACS), an emergent medical condition, is intricately linked to environmental factors like air pollution and meteorological conditions. Harnessing regional environmental data, such as weather metrics, can promptly forecast ACS incidence rates, enabling optimised medical resource allocation and increased patient recovery rates. However, the prediction task is rendered complex due to disparities in data collection capabilities across institutions, yielding datasets with analogous features but significant label variations, impeding the application of universal models. Challenges abound due to the heterogeneity of multi-factor data, temporal alignment disparities, and the intricacies of sparse data. To address these challenges, this paper introduces the Domain Adaptation with Multi-factor Associative Structures (DAMAS), a time-series domain adaptation approach based on multi-factor sparse associative frameworks. Augmented by an isomorphic attention-driven variable label mapping scheme and combined with multi-layer perceptrons, our approach skilfully negotiates label imbalances. This results in refined prediction precision connecting environmental factors to regional ACS incidences.