Attention-enhanced multiscale feature fusion network for pancreas and tumor segmentation.

BACKGROUND: Accurate pancreas and pancreatic tumor segmentation from abdominal scans is crucial for diagnosing and treating pancreatic diseases. Automated and reliable segmentation algorithms are highly desirable in both clinical practice and research. PURPOSE: Segmenting the pancreas and tumors is challenging due to their low contrast, irregular morphologies, and variable anatomical locations. Additionally, the substantial difference in size between the pancreas and small tumors makes this task difficult. This paper proposes an attention-enhanced multiscale feature fusion network (AMFF-Net) to address these issues via 3D attention and multiscale context fusion methods. METHODS: First, to prevent missed segmentation of tumors, we design the residual depthwise attention modules (RDAMs) to extract global features by expanding receptive fields of shallow layers in the encoder. Second, hybrid transformer modules (HTMs) are proposed to model deep semantic features and suppress irrelevant regions while highlighting critical anatomical characteristics. Additionally, the multiscale feature fusion module (MFFM) fuses adjacent top and bottom scale semantic features to address the size imbalance issue. RESULTS: The proposed AMFF-Net was evaluated on the public MSD dataset, achieving 82.12% DSC for pancreas and 57.00% for tumors. It also demonstrated effective segmentation performance on the NIH and private datasets, outperforming previous State-Of-The-Art (SOTA) methods. Ablation studies verify the effectiveness of RDAMs, HTMs, and MFFM. CONCLUSIONS: We propose an effective deep learning network for pancreas and tumor segmentation from abdominal CT scans. The proposed modules can better leverage global dependencies and semantic information and achieve significantly higher accuracy than the previous SOTA methods.

Ultra-small CuOx/GDYO nanozyme with boosting peroxidase-like activity via electrochemical strategy: Toward applicable colorimetric detection of organophosphate pesticides.

In this paper, an ultra-small-sized CuOx/GDYO nanozyme in situ grown on ITO glass was rationally synthesized from mixed precursors of graphdiyne oxide (GDYO) and copper based infinite coordination polymer (Cu-ICP, consisting of Cu ions and two organic ligands 3,5-di-tert-butylcatechol and 1,4-bis(imidazole-1-ylmethyl)benzene) via mild and simple electrochemical strategy. On one hand, the preferential electro-reduction of Cu-ICP enabled the formation of ultra-small CuOx with Cu(I) as the main component and avoided the loss of oxygen-containing functional groups and defects on the surface of GDYO; on the other hand, GDYO can also serve as electroless reductive species to facilitate the electrochemical deposition of CuOx and turn itself to a higher oxidation state with more exposed functional groups and defects. This one-stone-two-birds electrochemical strategy empowered CuOx/GDYO nanozyme with superior peroxidase-mimicking activity and robust anchoring stability on ITO glass, thus enabled further exploration of the portable device with availability for point-of-use applications. Based on the organophosphorus pesticides (OPs) blocked acetylcholinesterase (AChE) activity, the competitive redox reaction was regulated to initiate the chromogenic reaction of 3,3',5,5'-tetramethylbenzidine (TMB) catalyzed by CuOx/GDYO peroxidase-like nanozyme, which laid out a foundation for the detection of OPs (with chlorpyrifos as an example). With a detection of limit low to 0.57 nM, the OPs residues during agricultural production can be directly monitored by the portable device we developed.

Highly efficient and sensitive detection of tetracycline in environmental water: Insights into the synergistic mechanism of biomass-derived carbon dots and N-methyl pyrrolidone solvent.

The tetracycline (TC) residue in water environment has caused serious public safety issue. Thus, efficient sensing of TC is highly desirable for environmental protection. Herein, biomass-derived nitrogen-doped carbon dots (N-CDs) synthesized from natural Ophiopogon japonicus f. nanus (O. japonicus) were used for TC detection. The unique solvent synergism efficiently enhanced detection sensitivity, and the detailed sensing mechanism was deeply investigated. The blue fluorescence of N-CDs was quenched by TC via static quenching and inner filter effect. Moreover, the enhancement of green fluorescence from deprotonated TC was firstly proposed and sufficiently verified. The solvent effect of N-methyl pyrrolidone (NMP) and the fluorescence resonance energy transfer (FRET) with N-CDs achieved an instantaneous enhancement of the green emission by 64-fold. Accordingly, a ratiometric fluorescence method was constructed for rapid and sensitive sensing of TC with a low detection limit of 6.3 nM within 60 s. The synergistic effect of N-CDs and solvent assistance significantly improved the sensitivity by 7-fold compared to that in water. Remarkably, the biomass-derived N-CDs displayed low cost, good solubility, and desired stability. The deep insights into the synergism with solvent can provide prospects for the utilization of biomass-based materials and broaden the development of advanced sensors with promising applications.

Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. OBJECTIVE: This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. RESULTS: The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

Adaptation and Validation of Learned Helplessness Scale in Chinese Law School Students.

Purpose: This study aims to translate and validate the Learned Helplessness Scale (LHS) for use in the educational context and specifically among Chinese law school students. Understanding learned helplessness in the context of Chinese law students can provide unique insights into the interaction of legal education, psychological health, and cultural influences, thereby contributing to a more nuanced understanding of learned helplessness. Methods: A total of 711 Chinese college students from two law schools participated in this study. The Learned Helplessness Scale (LHS) was translated into Chinese using forward and backward translation. Exploratory and confirmatory factor analysis, and construct validity were conducted to assess the dimensionality of the Chinese version of the LHS (Chinese LHS). Results: The exploratory factor analysis indicated that the Chinese LHS has a four-factor structure consisting of 14 items, which accounted for 50% of the total variance. The subsequent confirmatory factor analysis further supported this four-factor structure. The internal consistency of the Chinese LHS was found to be medium to high, with Cronbach's α values ranging from 0.63 to 0.87 for the subfactors, and 0.79 for the total scale. In addition, concurrent validity is also confirmed. Conclusion: The 14-item version of the Chinese LHS is a psychometric sound instrument for assessing learned helplessness among Chinese law school students.

pDenoiser: A Personalized Speech Enhancement Neural Network for Pre-hospital Emergency Medical Services.

Pre-hospital emergency medical service (EMS) tasks often come with complex and diverse noise interferences, posing challenges in implementing ASR-based medical technologies and hindering efficient and accurate telephonic communication. Among the different types of noise distortion, interfering speech is especially annoying. To address these issues, our aim is to develop a technology capable of extracting the intended speech content of the target physician from noisy and mixed audio during EMS tasks. In this work, we propose a monoaural personalized speech enhancement (PSE) method called pDenoiser, which is a real-time neural network that operates in the time domain. By leveraging the prior vocalization cues of emergency physicians, pDenoiser selectively enhances target speech components while suppressing noise and nontarget speech components, thereby improving speech quality and speech recognition accuracy under noisy conditions. We demonstrate the potential value of our approach through evaluations on both public general-domain test sets and our self-collected real-world EMS test sets. The experimental results are promising, as our model effectively promotes both speech quality and ASR performance under various conditions and outperforms related methods across multiple evaluation metrics. Our methodology will hopefully elevate EMS efficiency and fortify security against nontarget speech during EMS tasks.

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.

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.

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.

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.