GCN-Based Risk Prediction for Necrosis Slide of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world which ranks fourth in cancer deaths. Primary pathological necrosis is an effective prognostic indicator for hepatocellular carcinoma. We propose a GCN-based approach that mimics the pathologist's perspective for global assessment of necrosis tissue distribution to analyze patient survival. Specifically, we introduced a graph convolutional neural network to construct a spatial map with necrotic tissue and tumor tissue as graph nodes, aiming to mine the contextual information between necrotic tissue in pathological sections. We used 1381 slides from 303 patients from the First Affiliated Hospital of Zhejiang University School to train the model and used TCGA-LIHC for external validation. The C-index of our method outperforms the baseline by about 4.45%, which proves that the information about the spatial distribution of necrosis learned by GCN is meaningful for guiding patient prognosis.

A Noise-Induced Hearing Loss Prediction Model Based on Asymmetric Convolution for Workers Exposed to Complex Industrial Noise.

OBJECTIVES: Current approaches for evaluating noise-induced hearing loss (NIHL), such as the International Standards Organization 1999 (ISO) 1999 prediction model, rely mainly on noise energy and exposure time, thus ignoring the intricate time-frequency characteristics of noise, which also play an important role in NIHL evaluation. In this study, an innovative NIHL prediction model based on temporal and spectral feature extraction using an asymmetric convolution algorithm is proposed. DESIGN: Personal data and individual occupational noise records from 2214 workers across 23 factories in Zhejiang Province, China, were used in this study. In addition to traditional metrics like noise energy and exposure duration, the importance of time-frequency features in NIHL assessment was also emphasized. To capture these features, operations such as random sampling, windowing, short-time Fourier transform, and splicing were performed to create time-frequency spectrograms from noise recordings. Two asymmetric convolution kernels then were used to extract these critical features. These features, combined with personal information (e.g., age, length of service) in various configurations, were used as model inputs. The optimal network structure was selected based on the area under the curve (AUC) from 10-fold cross-validation, alongside the Wilcoxon signed ranks test. The proposed model was compared with the support vector machine (SVM) and ISO 1999 models, and the superiority of the new approach was verified by ablation experiments. RESULTS: The proposed model had an AUC of 0.7768 ± 0.0223 (mean ± SD), outperforming both the SVM model (AUC: 0.7504 ± 0.0273) and the ISO 1999 model (AUC: 0.5094 ± 0.0071). Wilcoxon signed ranks tests confirmed the significant improvement of the proposed model ( p = 0.0025 compared with ISO 1999, and p = 0.00142 compared with SVM). CONCLUSIONS: This study introduced a new NIHL prediction method that provides deeper insights into industrial noise exposure data. The results demonstrated the superior performance of the new model over ISO 1999 and SVM models. By combining time-frequency features and personal information, the proposed approach bridged the gap between conventional noise assessment and machine learning-based methods, effectively improving the ability to protect workers' hearing.

A few-shot disease diagnosis decision making model based on meta-learning for general practice.

BACKGROUND: Diagnostic errors have become the biggest threat to the safety of patients in primary health care. General practitioners, as the "gatekeepers" of primary health care, have a responsibility to accurately diagnose patients. However, many general practitioners have insufficient knowledge and clinical experience in some diseases. Clinical decision making tools need to be developed to effectively improve the diagnostic process in primary health care. The long-tailed class distributions of medical datasets are challenging for many popular decision making models based on deep learning, which have difficulty predicting few-shot diseases. Meta-learning is a new strategy for solving few-shot problems. METHODS AND MATERIALS: In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n-way, k-shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n×k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset. RESULT: Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90.02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29.13 % and 21.63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results. CONCLUSION: The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.

Design of Household Cognitive Level Assessment System Based on Grip Force and Finger Force Distribution.

In this paper we designed a household cognitive level assessment system based on finger force distribution. The system evaluates the user's current cognitive level according to the degree of matching between the characteristics of user's grip force and finger force distribution data and the characteristics in the database. The system based on finger force distribution will greatly reduce the space and economic cost of household cognitive level assessment.

Design and Application of Multi-Center Clinical Research Platform for Phenotyping of Voriconazole Hepatotoxicity.

We introduce a phenotyping pipeline for voriconazole hepatotoxicity based on a multi-center clinical research platform. Using the platform's queue construction, feature generation, and feature screening functions, 52 features were obtained for model training. The prediction model of voriconazole hepatotoxicity was obtained by using the model training and evaluation functions of the platform. Important risk factors and protection factors of the model were listed.

Does a Digital Health Application Could Be the Supplement to the Influenza Surveillance System?

Epidemics of seasonal influenza is a major public health concern in china. Historical percentage of influenza-like illness (ILI%) from CDC and health enquiry data from a health-related application were collected, when combining the real-time ILI-related search queries with one-week ago's ILI%, it was able to predict the trend of ILI correctly and timely. Digital health application is potentializing a supplement to the traditional influenza surveillance systems in China.

Physician-Centered EHR Data Utilization: A Pilot Study.

The utilization of vast amounts of EHR data is crucial to the studies in medical informatics. Physicians are medical participants who directly record clinical data into EHR with their personal expertise, making their roles essential in follow-up data utilization, which current studies have yet to recognize. This paper proposes a physician-centered perspective for EHR data utilization and emphasizes the feasibility and potentiality of digging into physicians' latent decision patterns in EHR. To support our proposal, we design a physician-centered CDS approach named PhyC and test it on a real-world EHR dataset. Experiments show that PhyC performs significantly better in the auxiliary diagnosis of multiple diseases than globally learned models. Discussions on experimental results suggest that physician-centered data utilization can help to derive more objective CDS models, while more means for utilization need further exploration.

Personalized Prediction of Parkinson's Disease Progression Based on Deep Gaussian Processes.

Parkinson's disease is a chronic progressive neurodegenerative disease with highly heterogeneous symptoms and progression. It is helpful for patient management to establish a personalized model that integrates heterogeneous interpretation methods to predict disease progression. In the study, we propose a novel approach based on a multi-task learning framework to divide Parkinson's disease progression modeling into an unsupervised clustering task and a disease progression prediction task. On the one hand, the method can cluster patients with different progression trajectories and discover new progression patterns of Parkinson's disease. On the other hand, the discovery of new progression patterns helps to predict the future progression of Parkinson's disease markers more accurately through parameter sharing among multiple tasks. We discovered three different Parkinson's disease progression patterns and achieved better prediction performance (MAE=5.015, RMSE=7.284, r2=0.727) than previously proposed methods on Parkinson's Progression Markers Initiative datasets, which is a longitudinal cohort study with newly diagnosed Parkinson's disease.

Hierarchical Label Distribution Learning for Disease Prediction.

The prediction of disease can facilitate early intervention, comprehensive diagnosis and treatment, thereby benefiting healthcare and reducing medical costs. While single class and multi-class learning methods have been applied for disease prediction, they are inadequate in distinguishing between primary and secondary diagnoses, which is crucial for treatments. In this paper, label distribution is suggested to describe the diagnosis, which assigns the description degree to quantify the diagnosis. Additionally, a novel hierarchical label distribution learning (HLDL) model is proposed to make fine-grained predictions based on the hierarchical classification of diseases, taking into account the relationship among diseases. The experimental results on real-world datasets demonstrate that the HLDL model outperforms the baselines with statistical significance.

Extracting Dynamic Information of Temporal Clinical Data to Predict the Outcome in Critically Ill Patients.

Outcome prediction is essential for the administration and treatment of critically ill patients. For those patients, clinical measurements are continuously monitored and the time-varying data contains rich information for assessing the patients' status. However, it is unclear how to capture the dynamic information effectively. In this work, multiple feature extraction methods, i.e. statistical feature classification methods and temporal modeling methods, such as recurrent neural network (RNN), were analyzed on a critical illness dataset with 18415 cases. The experimental results show when the dimension increases from 10 to 50, the RNN algorithm is gradually superior to the statistical feature classification methods with simple logic. The RNN model achieves the largest AUC value of 0.8463. Therefore, the temporal modeling methods are promising to capture temporal features which are predictive of the patients' outcome and can be extended in more clinical applications.

Towards an Environmentally Robust Speech Assistant System for Emergency Medical Services.

Automated speech recognition technology with robust performance in various environments is highly needed by emergency clinicians, but there are few successful cases. One main challenge is the wide variety of environmental interference involved during a typical prehospital care emergency service such as background noises and overlapping speech. To solve this problem, we try to establish an environmentally robust speech assistant system with the help of the proposed personalized speech enhancement (PSE) method, which utilizes the target physician's voiceprint feature to suppress non-target signal components. We demonstrate its potential value using both general public test set and our real EMS test set by evaluating the objective speech quality metrics, DNSMOS, and the recognition accuracy. Hopefully, the proposed method will raise EMS efficiency and security against non-target speech.

A Blockchain-Based Approach for Patient Data Alignment Across Institutions.

Clinical studies need multi-center, long-term patient data, which are difficult to align. We present a blockchain-based approach that uses cryptographic matching and attribute-based encryption for secure data alignment, aggregation, and access. It improves efficiency, lowers data synchronization, and facilitates cross-institutional patient data association and visualization.

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice.

General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients' electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0.7873, recall@10 of 0.9020 and hits@10 of 0.9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.

Real-Time Epileptic Seizure Detection Based on Deep Learning.

Epilepsy is one of the most common neurological diseases, and video EEG is the most commonly used examination method for epilepsy diagnosis. However, since the video EEG examination lasts for hours, the escort has a heavy burden, and the large amount of video EEG data needs to be visually checked by the doctor. The real-time detection of epileptic seizures can reduce the stress of the escort and provide a mark for the doctor to check the EEG efficiently. In this paper, we propose a deep neural network with specified signal representation for real-time seizure detection and add a smoothing filter on the model output to enhance performance. First, we compare the performance of real-time epileptic seizure detection model under different signal representations. Then we use the best signal representation for further analysis in real-time scenario. In the experiment, the EEG data of 9 patients in the CHB-MIT public data set was used, and a patient-specific neural network was trained for each individual. The recall was 97%, the false alarm was 0.219 times per hour, and the latency time was 3.4s for real-time seizure event detection. The results show that this method can realize the real-time detection of epileptic seizures, which is of great significance to the subsequent system design combined with actual scenes.

Current Perspectives and Trend of Acupuncture in Breast Cancer-Related Symptoms: A Bibliometric Study.

Purpose: This bibliometric research aims to delineate global publication trends and emerging research interests in the use of acupuncture for breast cancer (BC)-related symptoms treatment over the past three decades. Furthermore, it identifies influential institutions, potential collaborative partners, and future research trends, thereby providing guidance for relevant, novel research directions. Methods: Scientific publications related to acupuncture for BC-related symptoms were gathered from the Web of Science Core Collection (WoSCC) from 1993 to 2023. Four software applications were principally used to analyze the resulting data: the "bibliometrix" package in the R environment (version 4.2.3), VOSviewer, CiteSpace6.1.R6, and the bibliometrics website. These applications were employed to evaluate different parameters. Results: A total of 621 papers on acupuncture in BC-related symptoms treatment were analyzed. The United States, China, and South Korea contributed the most, with Memorial Sloan Kettering Cancer Center, and Columbia University leading institutions. It is interesting to mention that Mao, Jun J. and Molassiotis, A. feature among the top 10 authors and co-cited authors. JAMA is the leading journal, with an ongoing focus on acupuncture's effectiveness. Keywords show that the initial research focus was mainly on "vasomotor symptoms", but in recent years there has been a gradual shift towards "pain", "chemotherapy-induced peripheral neuropathy (CIPN)", "electroacupuncture", and "non-specific effects". Conclusion: Acupuncture has demonstrated a unique value in the process of adjuvant treatment of BC-related symptoms, and has been shown to be effective in reducing pain, eliminating fatigue, and improving quality of life. The study of the mechanisms of acupuncture and the application of electroacupuncture are possible future research priorities in this field. This study offers a deep perspective on acupuncture for BC research, highlighting key points and future trends.

A Knowledge-based and Data-driven Approach for Predicting Acute Kidney Injury in Patients with Heart Failure.

It has great potential to integrate medical knowledge and electronic health record data for diagnosis prediction. However, present studies only utilized information from knowledge graphs, omitting potentially significant global graph structural features. In this study, we proposed a knowledge and data integrating modeling approach to reconstruct patient electronic health record data with graph structure and use medical knowledge as internal information of patient data to build a risk prediction model for acute kidney injury in patients with heart failure based on graph neural networks. Experimental results based on the MIMIC III data showed that the method proposed was superior to other baseline models in predicting the risk of acute kidney injury in heart failure patients, with an accuracy of 0.725 and an F1 score of 0.755. This study provides a novel approach to the disease risk prediction models that integrates medical knowledge and data.

Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation.

Behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in Tourette syndrome. Detecting tic symptoms plays an important role in patient treatment and evaluation; accurate tic identification is the key to clinical diagnosis and evaluation. In this study, we proposed a tic action detection method using face video feature recognition for tic and control groups. Through facial ROI extraction, a 3D convolutional neural network was used to learn video feature representations, and multi-instance learning anomaly detection strategy was integrated to construct the tic action analysis and discrimination framework. We applied this tic recognition framework in our video dataset. The model evaluation results achieved average tic detection accuracy of 91.02%, precision of 77.07% and recall of 78.78%. And the tic score curve with postprocessing provided information of how the patient's twitches change over time. The detection results at the individual level indicated that our method can effectively detect tic actions in videos of Tourette patients without the need for fine labeling, which is significant for the long-term evaluation of patients with Tourette syndrome.

Integrating Medical Domain Knowledge for Early Diagnosis of Fever of Unknown Origin: An Interpretable Hierarchical Multimodal Neural Network Approach.

Accurate and interpretable differential diagnostic technologies are crucial for supporting clinicians in decision-making and treatment-planning for patients with fever of unknown origin (FUO). Existing solutions commonly address the diagnosis of FUO by transforming it into a multi-classification task. However, after the emergence of COVID-19 pandemic, clinicians have recognized the heightened significance of early diagnosis in patients with FUO, particularly for practical needs such as early triage. This has resulted in increased demands for identifying a wider range of etiologies, shorter observation windows, and better model interpretability. In this article, we propose an interpretable hierarchical multimodal neural network framework (iHMNNF) to facilitate early diagnosis of FUO by incorporating medical domain knowledge and leveraging multimodal clinical data. The iHMNNF comprises a top-down hierarchical reasoning framework (Td-HRF) built on the class hierarchy of FUO etiologies, five local attention-based multimodal neural networks (La-MNNs) trained for each parent node of the class hierarchy, and an interpretable module based on layer-wise relevance propagation (LRP) and attention mechanism. Experimental datasets were collected from electronic health records (EHRs) at a large-scale tertiary grade-A hospital in China, comprising 34,051 hospital admissions of 30,794 FUO patients from January 2011 to October 2020. Our proposed La-MNNs achieved area under the receiver operating characteristic curve (AUROC) values ranging from 0.7809 to 0.9035 across all five decomposed tasks, surpassing competing machine learning (ML) and single-modality deep learning (DL) methods while also providing enhanced interpretability. Furthermore, we explored the feasibility of identifying FUO etiologies using only the first N-hour time series data obtained after admission.

A survey of artificial intelligence in rheumatoid arthritis.

The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.

Au/Lum/RhB@Ag-DMcT ICP-Based Double-Ratio Colorimetric and Fluorometric Dual Mode Assay and Multi-Responsive Coffee Ring Chips for Point-of-Use Analysis of Phosphate Ions.

In this work, by fully exploring the stimulus response of the guest-functionalized infinite coordination polymers (ICPs), a double-ratio colorimetric and fluorometric dual mode assay and multi-responsive coffee ring chips for point-of-use analysis of phosphate ions (Pi) were proposed. First, the complex host-guest interactions were rationally designed to obtain Au/Lum/RhB@Ag-DMcT ICPs. The composite ICPs exhibited a purple-blue color resulted from the modulated localized surface plasmon resonance (LSPR) of the Au core, and a blue fluorescence color stemmed from the unique aggregation-induced-emission (AIE) of Luminol (Lum) and the aggregation-caused-quenching (ACQ) of rhodamine B (RhB). With the presence of Pi, the host-guest interactions of the shell within Au/Lum/RhB@Ag-DMcT ICPs were interrupted to release Au core, Lum, and RhB in a dispersed state. Consequently, the color of the solution changed to purple-red (the mixed color of the Au core and RhB guest), and the fluorescence color turned to orange-red (AIE of Lum decreased, while the ACQ of RhB recovered). This constituted the sensing mechanism for dual-mode Pi assay with the double ratiometric response. Second, during the stimulus response, the surface wettability/size/amount of Au/Lum/RhB@Ag-DMcT ICPs simultaneously altered. These changes were reflected in the form of the coffee ring deposition pattern variances on the glass substrate and served as signal readouts for the exploration of multi-responsive coffee ring chips for the first time. Quantitative Pi detection with high accuracy and reliability in real samples was thereby realized, which offered an opportunity for the point-of-use analysis of Pi in resources-limited areas in a high-throughput fashion.