Reviewerly/exHarmony huggingface dataset:
Nonlinear Hyperbolic PDE-based Filter for Mixed Poisson-Gaussian Noise Removal from X-ray Images

A nonlinear partial differential equation (PDE) – based mixed noise reduction technique for medical X-Ray images is proposed in this paper. The filtering approach uses a novel well-posed second-order hyperbolic PDE model that is solved numerically applying a fast-converging finite difference method-based discretization algorithm. This iterative numerical approximation scheme restores successfully the medical images affected by mixtures of noise, since it removes properly both the Gaussian and Poisson noise. The effectiveness of the introduced technique is illustrated by the restoration experiments and method comparison described in this work.
Integrated Extended SIR-type Epidemiological Modeling Platform for Covid-19

In these conditions of Covid-19 pandemic, the design and implementation of an epidemiological modeling platform with an API-type integration with beneficiary entities and data providers is a priority for the healthcare system and other organizations. An epidemiological platform (PLIS), replicable and adaptable for managing an epidemic, based on an integrated extended SIR-type model, is presented in this paper. This solution has an open architecture, respecting the scalability and interoperability, requirements, and is based on international communication standards and protocols, and SOA technology.
Coping with Missing Values in Medical Data Sets: A Proposal of an Approach Based on Soft Clustering and Measures of Central Tendency

Medical data presents several hidden challenges compared to conventional datasets, the first challenge we encounter before even considering data processing and analysis is the presence of missing values in datasets. In this paper, we present a theoretical framework for missing values imputation through a fuzzy c-means machine learning clustering algorithm and statistical measures of central tendency, namely mean, median and mode. The algorithm of the proposed approach along with the experimental design is presented, aiming to evaluate the performance of the proposed missing values imputation on different medical datasets from the University of California Irvine Machine Learning Repository.
Multi-task Learning with Consistent Prediction for Efficient Breast Ultrasound Tumor Detection

Segmentation and classification a re h ighly correlated tasks in tumor detection from breast ultrasound images. Recent studies have successfully applied multi-task learning to breast ultrasound image analysis to explore the correlation between tasks. However, there exists potential inconsistency between individual tasks that critically affect the overall performance of breast ultrasound image analysis. Therefore, this study designs a consistency branch for harmonizing the segmentation and classification t ask 0 ptimization. T he c onsistency b ranch characterizes the outputs of individual task-specific models to maintain consistency during training, thereby generating highly consistent results. Specifically, the consistency branch outputs a consistency probability while determining the inconsistency types predicted by both tasks. Subsequently, the segmentation and classification loss weights are reconciled using consistency probabilities based on the inconsistent prediction behavior for each sample, thus constraining the two tasks to produce consistent predictions close to the ground truth. The evaluation using private and public breast ultrasound image datasets indicates that the proposed method can effectively remedy the inconsistent predictions between tasks for improved computerized breast ultrasound image analysis.
Low Data Image Analysis with a Generative Adversarial Network: A Case Study on Women Pelvic MRI Scans

Medical image analysis and geometry reconstruction have vast applications in evaluating pathology and treatment. The emergence of machine learning (ML) has led to considerable progress in the field. However, the performance of ML algorithms strongly depends on the size of the training dataset. Providing quality data, though, is often expensive, laborious, and time-consuming. The approach presented in this paper demonstrates that Generative Adversarial Networks (GANs) are helpful for generating high-quality synthetic images. With the artificially generated data, the size of the required clinical images for 3D reconstruction of patient-specific pelvic can remain small. Our results show that given just five clinical MRI scans, the ML performs image segmentation with %68 accuracy. This number increases to %85 when synthetic data generated by the GAN model is added to the training set. Details on the code for the generative model are publicly available at https://github.com/sabino-ramirez/task driven data augmentation.git
Disease Risk Prediction via Heterogeneous Graph Attention Networks

Disease risk prediction is an important component of clinical decision support systems. In order to predict disease risk, most studies attempt to extract features associated with disease risk from electronic health records (EHRs), however, the complex interactions between heterogeneous entities associated with disease risk (e.g., diseases, symptoms, medications, and other treatment items) are ignored, resulting in imprecise disease risk prediction. To tackle this issue, we propose a disease prediction method based on heterogeneous graph attention networks (HAN) in this paper. In our study, the EHRs are used to constructed the heterogeneous medical graph, in which the embedding vector of each node can be calculated via its directly connected neighbors’ features. The heterogeneous medical graph contains different types of nodes and links, and in our proposed model, the attention mechanism is adopted, which is used to learn the importance between a node and its meta-path connected neighboring nodes. The proposed method is good at mining the potential information of the electronic medical records. Experiment results on real datasets show that the performance of the poposed methods outperforms that of the baseline methods.
An Embedded Machine Learning Model for Early Detection and Intervention of High-Risk Intensive Care Unit Readmission Patients

Hospitalized patients who are transferred out from the Intensive Care Unit (ICU) but readmitted within three days are considered ICU readmission. Compared with general hospital readmission, ICU readmission brings higher morbidity and mortality risks along with more financial and labor expenses because ICU requires a higher nurse-to-patient ratio and closer monitoring of high-risk patients. Early prediction and prevention of potential ICU readmission benefits both healthcare providers and patients. In this work, we designed an embedded machine learning (ML) system to detect individuals with high ICU readmission risk and support healthcare providers’ decisions on further interventions based on population health informatics. To discover patterns from the sparse and imbalanced ICU data, an algorithm called explainable ML with noise reduction (NRL) is proposed to predict patient readmission risk and identify controllable variables for early intervention. The proposed algorithm is tested on the MIMIC-II and achieved a better performance with an improvement of over 113% on sensitivity compared with widely utilized models, including logistic regression and decision tree, in predicting ICU readmission. A graphical user interface of the embedded machine learning system is further designed to illustrate a case study on its application in ICU readmission early detection and intervention in practice.
ConTenNet: Quantum Tensor-augmented Convolutional Representations for Breast Cancer Histopathological Image Classification

In recent years, deep convolutional neural networks (CNNs) have been spectacularly successful in the classification and diagnosis of breast cancer and its histopathological images. However, for CNNs, the whole learning process requires high computational complexity, a large number of parameters, and loss of certain global feature information. Meanwhile, the flexibility of tensor networks (TNs) algorithms to machine learning leads to creativity in devising new approaches. In this paper, we propose a novel framework named ConTenNet based on the pre-trained CNNs and quantum TNs (QTNs) to address the weaknesses in CNNs. We propose ConTenNet on the BreakHis dataset, and the experiments show that our model competes with the state-of-the-art methods on both original and normalized images with lower computational complexity, a less number of parameters, and global feature information. Moreover, we adopt the color normalization method to avoid the interference of color in model learning, using the gradient-weighted class activation mapping (Grad-CAM) to prove the necessity of color normalization and the reliability of model learning.
Smart Scan of Blood Test Documents to be Integrated in a mHealth Application

Controlling the physiological evolution of people plays an important role in the prevention and detection of health problems. Often, the medical analyzes performed by people are stored in documents that in the long term tend to be lost, suffer wear and tear and most importantly, do not facilitate the comparison of data to assess the physiological evolution. This work proposes an intelligent scanning system for blood test documents. This type of component can be found in digital (Digitally created PDFs, scanned PDFs and images) or physical format. It is intended to extract from this type of documents only the essential information, consisting of the blood components and their concentration. Optical Character Recognition (OCR) Machine Learning (ML) Kit was used to extract relevant information from documents in the case of scanned PDF's (SPDFs), images, and physical documents, and Apache's PdfBox for digitally created PDF's (DCPDFs). To filter the data and associate the blood constituents detected with the respective concentrations, a condition tree was developed. In the end, the methods used were able to detect an average of 95.38% of the blood compounds present in the different document formats. On average, 87.63% of the concentrations were correctly associated with the detected compounds.
Research on Bidirectional Recurrent Imputation of Multivariate Time Series for Clinical Outcomes Prediction

Clinical multivariate time series can be used to predict clinical outcomes and in turn support clinical decision. Due to various reasons such as irregular sampling, clinical time series typically contain many missing values, and some missing values contain a large amount of information for clinical prediction. It is important to impute missing values as accurately as possible prior to clinical prediction. However, the missing rate of different variables in clinical time series varies greatly. In addition, clinical time data also has strong burst, which increases the difficulty of missing value imputation. In this paper, we propose a new method for missing value imputation in multivariate time series data, using a trainable weighted decay mechanism to improve the traditional Bi-LSTM model to capture temporal correlations, impute missing values based on bidirectional recurrent neural network and perform clinical outcomes predict. We take the missing rate, cumulative missing rate and burstiness of each variable as input to learn the features of different variables separately and improve the imputation effect of missing values. Experiments on two real-world clinical datasets show that our model achieves optimal performance in both imputation effect and prediction accuracy.
Causal Coupled Mechanisms: A Control Method with Cooperation and Competition for Complex System

Complex systems are ubiquitous in the real world and tend to have complicated and poorly understood dynamics. For their control issues, the challenge is to guarantee accuracy, robustness, and generalization in such bloated and troubled environments. Fortunately, a complex system can be divided into multiple modular structures that human cognition appears to exploit. Inspired by this cognition, a novel control method, Causal Coupled Mechanisms (CCMs), is proposed that explores the cooperation in division and competition in combination. Our method employs the theory of hierarchical reinforcement learning (HRL), in which 1) the high-level policy with competitive awareness divides the whole complex system into multiple functional mechanisms, and 2) the low-level policy finishes the control task of each mechanism. Specifically for cooperation, a cascade control module helps the series operation of CCMs, and a forward coupled reasoning module is used to recover the coupling information lost in the division process. On both synthetic systems and a real-world biological regulatory system, the CCM method achieves robust and state-of-the-art control results even with unpredictable random noise. Moreover, generalization results show that reusing prepared specialized CCMs helps to perform well in environments with different confounders and dynamics.
A high-concurrency blockchain model for large-scale medical cohort data storage and sharing

In the medical scenarios, the demand for secure sharing of medical data and trusted federated computing continues to increase, and blockchain technology can provide secure, credible, and tamper-resistant capabilities, which makes it necessary to combine the two. However, the full application of blockchain to medical scenarios faces two issues. Medical assets such as medical cohort data with strong data correlation and large scale are difficult to be accurately described and safely operated by blockchain. Transactions such as disease diagnosis and hospitalization prediction with many parameters are difficult to execute concurrently in blockchain. Therefore, we propose MAA model, which closely associates patients with assets, separates the logic of asset operations from its storage. At the same time, we propose OPE model with double-layer pipeline concurrency. By constructing the Dependency Graph, and generating multiple blocks with low conflict rates, which are executed simultaneously among multiple Execute Groups, OPE supports the high concurrent execution of medical transactions with high computing power requirements such as trusted federated learning. Experiments show that our model supports multiple types of medical data on-chain compared to existing models, and the concurrency is increased by at least 40% in a high-conflict medical environment.
CFDA-M: Coarse-to-Fine Domain Adaptation for Mitochondria Segmentation via Patch-wise Image Alignment and Online Self-training

Accurate and robust segmentation of mitochondria from electron microscopy images plays a critical role in understanding cellular functions. While effective, learning-based approaches require vast quantities of expert annotations. Manual efforts can be alleviated by transferring the knowledge learned from the source domain to unseen target domains, as known as unsupervised domain adaptation. In this work, we propose a two-stage pipeline for cross-dataset mitochondria segmentation, aiming to mitigate domain shift in a coarse-to-fine manner. In the first stage, we integrate the style transfer block and segmentation network into an end-to-end image alignment framework. Specifically, patch-wise contrastive learning is employed to guarantee the semantic fidelity of mitochondria, providing more reliable translated images for the segmentation network. In the second stage, a novel online self-training network updated with target images and co-evolving pseudo labels is proposed to fine-tune the segmentation network trained beforehand. Furthermore, to effectively utilize unlabeled data, consistency regularization is introduced in both stages to enforce stable predictions under various perturbations. Experimental results on public datasets demonstrate that the proposed approach outperforms the existing methods by a large margin in bidirectional adaptation for mitochondria segmentation and achieves comparable results with supervised methods.
A new fraudulent website detection technology based on transfer learning

With the emergence of new technologies, new applications, and new business formats, new fraudulent methods emerge and continue to evolve and upgrade. Among them, the main platforms of online fraud are various fraudulent websites, and feature identification of fraudulent websites is a necessary way to effectively prevent such crimes. At present, common fraudulent website detection techniques are mainly based on the classification of known fraud types. This method can only find and known fraudulent types of fraudulent sites. There is no effective way to quickly spot new scam sites. To address this problem, we employ DAN transfer learning technology to timely discover fraudulent website. By studying the correlation between new fraudulent websites and known black sample websites, high-value Information is transferred from known models and data to new fraudulent sites that need to be learned, and new fraudulent sites can be quickly discovered. To verify the effectiveness of the method, we conducted a comparison experiment of multiple algorithms using 71,500 training data and 28,500 testing data. The results of comparison experiment show that the DAN method could identify new fraudulent websites with high accuracy.
Semantic Reasoning with NLI for Assertion Detection in Medical Text

Assertion information is of crucial importance for constructing an intelligent diagnosis system as it contains clinical findings and decision basis of clinicians in the electronic medical records (EMRs), e.g., whether a symptom is present or not. Current work mainly treats assertion detection as a sequence labeling task, or constructs rule-based methods. However, there is a challenge that to detect assertions embedded in the context with long-range dependencies, needs considering the whole text to capture the complex linguistic and underlying semantic information. To tackle the above issues, we consider assertion detection as a semantic reasoning task based on natural language inference (NLI). First, we generate candidate spans with boundary detection on the basis of which we can enrich the training corpus with external knowledge such as assertion definitions. Then we detect the assertions through the NLI-based classification. To the best of our knowledge, we build the first Chinese assertion dataset, which contains 4237 sentences on privacy de-identified ophthalmology remote reading reports. Extensive experiments demonstrate that our proposed method achieves the best results on both of the English dataset i2b2 and the Chinese privacy dataset.
A Pupil Segmentation Framework with Masked Image Modeling Enhanced Swin-Transformer

Detecting pupil from the image is critical in human-machine interaction and biomedical computing applications, which is supposed to be an actual image segmentation problem. Recently developed deep learning models provide a variety of novel approaches to the pupil segmentation task. However, dataset preparation and annotation acquirement to build pupil image datasets are labor-intensive and time-consuming. The shortage of labeled samples restricted the improvement of deep learning models. In this work, we use a mask image modeling mechanism to learn the latent representation from limited data samples, which significantly helps train deep models. Further, we propose a novel pupil segmentation model based on the recently proposed Swin-Transformer to validate the improvement validity of the mask mechanism. The proposed computational framework achieves better performance on the pupil segmentation tasks based on the LPW dataset through comparison experiments with other related deep learning models. The proposed framework is a promising solution for pupil segmentation and detection in small-sample learning applications.
MERAS: A method for entity recognition, alignment, and structuration from Chinese Electronic Medical Records

The description of entity information in Chinese Electronic Medical Records (EMRs) is not always isolated, and it often has rich attribute constraints, especially the symptom entities. Moreover, these entity descriptions are unstructured and may be colloquial, non-terminological, and misspelled. This paper proposed a new method, which is called MERAS for entity recognition, alignment, and structuration from Chinese EMRs. Different from the traditional entity extraction tasks, a series of complex entities with attribute constraints are annotated and recognized by MERAS, and then the entities are further converted to the standardized and structured entity sequences in JSON format based on the end-to-end translation idea. BiLSTM-CRF is applied to recognize the complex entities, and Transformer is applied to realize the entity alignment and structuration in this paper. With the manually annotated data set as the reference, 9 types of entities are extracted from the EMRs, and the corresponding corpus-feature-based enhanced strategies are proposed to support the model training. The experimental results show that MERAS can effectively extract standardized and structured entities from Chinese EMRs, and it can have great potential in practical applications. (Abstract)
Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values

Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. The predicted risks can be used to support decision-making by healthcare professionals. EHRs are structured patient journey data. Each patient journey contains a chronological set of clinical events, and within each clinical event, there is a set of clinical/medical activities. Due to variations of patient conditions and treatment needs, EHR patient journey data has an inherently high degree of missingness that contains important information affecting relationships among variables, including time. Existing deep learning-based models generate imputed values for missing values when learning the relationships. However, imputed data in EHR patient journey data may distort the clinical meaning of the original EHR patient journey data, resulting in classification bias. This paper proposes a novel end-to-end approach to modeling EHR patient journey data with Integrated Convolutional and Recurrent Neural Networks. Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation. Extensive experimental results using the proposed model on two real-world datasets demonstrate robust performance as well as superior prediction accuracy compared to existing state-of-the-art imputation-based prediction methods.