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Accurately diagnosing apple leaf diseases can reduce the use of pesticides and improve the quality of fruits, which is of significance to smart agriculture. Convolutional neural network as a deep learning model is widely used in the field of intelligent diagnosis of apple leaf diseases. Deploying a deep neural network for apple disease diagnosis to mobile devices allows for smarter, more efficient, and more accurate disease identification. However, classical convolutional neural networks have some limitations on agricultural disease diagnosis, such as a huge number of parameters, heavy computation, and long inference time. Thus, such a complex deep learning model is not easily deployed to mobile devices. To address the above problems, we propose the ECA-KDNet, an improved lightweight model based on the ECA attention mechanism and knowledge distillation, which shows superiority in accuracy, robustness, and lightweight. The experimental results show that compared with the classical convolutional neural network models, ECA-KDNet improves accuracy (98.28%) while ensuring lightweight (3.38 M).

In this paper, the Gynaecological Disease Diagnosis Expert System (GDDES) is a Graphical User Interface, developed with the Support Vector Classifier (Machine Learning Algorithm) and Natural Language Processing. It is language-independent, allowing women from any state in India to use the system in their own native tongue and have their disorders diagnosed in that language. The diagnosis process is divided into two steps: At first, the user selects their regional language and the system asks some queries in their selected language and submits the reply for each query, then the system uses the Support Vector Classifier (SVC) Model to predict the disease name; and secondly, the user is prompted to record their symptoms in their native tongue and GDDES uses Natural Language Processing to calculate cosine similarities and play the most similar voice recording of disease diagnosis, and displays the sentences of the recording in the user’s native language. The system with the SVC Model provides 93% accuracy and precision and 92% recall and f1 score.

Tuberculosis (TB) is a highly contagious and life-threatening infectious disease that affects millions of people worldwide. Early diagnosis of TB is essential for prompt treatment and control of the spread of the disease. In this paper, a new deep learning model called CBAMWDnet is proposed for the detection of TB in chest X-ray (CXR) images. The model is based on the Convolutional Block Attention Module (CBAM) and the Wide Dense Net (WDnet) architecture, which has been designed to effectively capture spatial and contextual information in the images. The performance of the proposed model is evaluated based on a large dataset of chest X-ray images and it is compared to several state-of-the-art models. The results show that the proposed model outperforms the other models in terms of accuracy (98.80%), sensitivity (94.28%), precision (98.50%), specificity (95.7%) and F1 score (96.35%). Additionally, our model demonstrates excellent generalization ability, with consistent performance on different datasets. In conclusion, the proposed CBAMWDnet model is a promising tool for the early diagnosis of TB, with superior performance compared to other state-of-the-art models, as evidenced by the evaluation metrics of accuracy, sensitivity, and specificity.

As the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeypox disease has raised concerns about another potential pandemic, highlighting the urgency of early detection and intervention to curb its transmission. Deep Learning (DL)-based disease prediction presents a promising solution, offering affordable and accessible diagnostic services. In this study, we harnessed Transfer Learning (TL) techniques to tweak and assess the performance of an array of six different DL models, encompassing VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, VGG19, and Vision Transformer (ViT). Among this diverse collection, it was the modified versions of the VGG19 and MobileNetV2 models that outshone the others, boasting striking accuracy rates ranging from an impressive 93% to an astounding 99%. Our results echo the findings of recent research endeavors that similarly showcase enhanced performance when developing disease diagnostic models armed with the power of TL. To add to this, we used Local Interpretable Model Agnostic Explanations (LIME) to lend a sense of transparency to our model’s predictions and identify the crucial features correlating with the onset of Monkeypox disease. These findings offer significant implications for disease prevention and control efforts, particularly in remote and resource-limited areas.

With the rapid advancements in artificial intelligence, particularly in machine learning and deep learning, automated disease diagnosis is becoming increasingly feasible. Generating larger databases is crucial for training and validating the performance of models for chronic diseases such as glaucoma and diabetic retinopathy, which progress slowly and unnoticed. Automated procedures for retinal vessel segmentation and optic cup/disk localization are preferred for large-scale screening of the public, contributing to the early detection and treatment of eye diseases, preventing blindness, and improving public health. This paper focuses on the challenges involved in segmenting the retinal vessels from fundus images and presents a modified ColonSegNet model for retinal vessel segmentation that includes efficient methods for locating the true vessels and applies data augmentation to overcome the issue of fewer graded images. The paper uses the optimal values for the contrast enhancement of retinal fundus images using intelligent evolution algorithms. The central vessel reflex, bifurcation, crossover, thin vessels, and lesion presence are highlighted as significant challenges in retinal vessel segmentation. The proposed method achieves high sensitivity, specificity, and accuracy, {0.839, 0.979, 0.966}, {0.865, 0.979, 0.971}, and {0.867, 0.981, 0.972}, segmenting retinal vessels on DRIVE, CHASE_DB, and STARE. The work is crucial in developing automated systems for the early detection and treatment of eye diseases, thereby improving public health.

In the field of dermatological diseases, especially for skin cancer, machine learning (ML) methods are used to classify melanoma and nevus using skin images. ML techniques result in high accuracy of diagnostic tasks since they are trained on balanced datasets. However, MLs working with imbalanced datasets produce erroneous results on precision, sensitivity, and specificity measured criteria. To deal with this problem, an augmentation approach combined with a category seesaw is used for the compensation factor. It increases the penalty for misclassified instances, thereby reducing the occurrence of false positives within the less common categories. This paper presents an approach to improve the efficiency of DCNN for classifying multi-class medical images on imbalanced datasets. The solution consists of three major contributions: (1) feature extraction based on some backbone models with customizing fully connected layers for classifier layers, (2) optimizing loss function (LF) and training parameters, (3) solving the problem of imbalanced samples using optimizing domination of weights between asymmetric classes with majority and minority categories. The method was evaluated and analyzed using the ISIC2018 benchmark and Chest X-ray dataset. Some well-known backbones were used for this study, e.g., EfficientNets, MobileNets, and DenseNets. The use of these backbones is to demonstrate that our methods are more efficient and stable in both light and heavy DCNN architectures. We also provide comparisons with existing methods that deal with the imbalance problem, e.g., data augmentation (AU), downsamples, customizing LF, and focal loss method (FL) for focusing on hard samples. Experimental results showed that these methods achieve good performance. However, there are several problems caused by generating new samples, and weighting samples, such as data overloading to train classifier models, a corrupt problem when applied to imbalanced data. Moreover, the FL method produced insufficient results on various DCNN backbones. Differently, our approach solves the imbalanced dataset based on boosting the sample weights of the minority and reducing the impact ratio of samples in majority categories. This strategy results in high precision and stable performance with various DCNN models without augmenting the dataset. Experiment results on ISIC2018 dataset demonstrated that our approach achieves more efficiency than other methods in some specific evaluation criteria as follows: higher than the FL method with 2.73% recall, 2.63% precision, 2.81% specificity, and 3.09% F1 using EfficientNet backbones; higher than AU method with 5.16% recall, 5.97% precision, 8.93% specificity, 6.16% F1 using DenseNet backbones.

Knowledge graph (KG) embedding has been used to benefit the diagnosis of animal diseases by analyzing electronic medical records (EMRs), such as notes and veterinary records. However, learning representations to capture entities and relations with literal information in KGs is challenging as the KGs show heterogeneous properties and various types of literal information. Meanwhile, the existing methods mostly aim to preserve graph structures surrounding target nodes without considering different types of literals, which could also carry significant information. In this paper, we propose a knowledge graph embedding model for the efficient diagnosis of animal diseases, which could learn various types of literal information and graph structure and fuse them into unified representations, namely LiteralKG. Specifically, we construct a knowledge graph that is built from EMRs along with literal information collected from various animal hospitals. We then fuse different types of entities and node feature information into unified vector representations through gate networks. Finally, we propose a self-supervised learning task to learn graph structure in pretext tasks and then towards various downstream tasks. Experimental results on link prediction tasks demonstrate that our model outperforms the baselines that consist of state-of-the-art models.

Diabetic Retinopathy (DR) is a result of prolonged diabetes with poor blood sugar management. It causes vision problems and blindness due to the deformation of the human retina. Recently, DR has become a crucial medical problem that affects the health and life of people. Diagnosis of DR can be done manually by ophthalmologists, but this is cumbersome and time consuming especially in the current overloaded physician’s environment. The early detection and prevention of DR, a severe complication of diabetes that can lead to blindness, require an automatic, accurate, and personalized machine learning-based method. Various deep learning algorithms, particularly convolutional neural networks (CNNs), have been investigated for detecting different stages of DR. Recently, transformers have proved their capabilities in natural language processing. Vision transformers (ViTs) are extensions of these models to capture long-range dependencies in images, which achieved better results than CNN models. However, ViT always needs huge datasets to learn properly, and this condition reduced its applicability in DR domain. Recently, a new real-world and large fundus image dataset called fine-grained annotated diabetic retinopathy (FGADR) has been released which supported the application of ViT in DR diagnosis domain. The literature has not explored FGADR to optimize ViT models. In this paper, we propose a novel ViT based deep learning pipeline for detecting the severity stages of DR based on fundus photography-based retina images. The model has been built using FGADR dataset. The model has been optimized using a new optimizer called AdamW to detect the global context of images. Because FGADR is an imbalanced dataset, we combine several techniques for handling this issue including the usage of F1-score as the optimization metric, data augmentation, class weights, label smoothing, and focal loss. Extensive experiments have been conducted to explore the role of ViT with different data balancing techniques to detect DR. In addition, the proposed model has been compared with the state-of-the-art CNN algorithms such as ResNet50, Incep-tionV3, and VGG19. The adopted model was able to capture the crucial features of retinal images to understand DR severity better. It achieved superior results compared to other CNN and baseline ViT models (i.e., 0.825, 0.825, 0.826, 0.964, 0.825, 0.825, and 0.956 for F1-score, accuracy, balanced accuracy, AUC, precision, recall, specificity, respectively). The results of the proposed ViT model were quite encouraging to be applied in real medical environment for assisting physicians to make accurate, personalized, and timely decisions.

In this paper, we propose an effective and intelligent prediction model that can well distinguish complex acute appendicitis from uncomplicated acute appendicitis. In this study, 358 patients admitted to the First Hospital of Jilin University in Changchun for acute appendicitis in the past 5 years were included, and the data panel was constructed based on 32 factors collected. The framework comprised mainly of a Principal Component Analysis (PCA) algorithm and a Support Vector Machine(SVM) with new kernel function named the Mercer Support Vector Machine(MSVM) model and optimized by a Hunger Game Search (HGS) algorithm. First, the PCA was used to reduce the dimension of the data. Second, the HGS integrated into the MSVM was employed to train the parameter of the classification model MSVM to obtain the optimal model—Hunger Game Search (HGS-MSVM). Finally, based on the acute appendicitis data, comparative experiments were conducted between HGS-MSVM and five well-known machine-learning models, namely the Random Forest (RF), K-Nearest Neighbor (KNN), MSVM, Logistic Regression (LR) and Discriminant Analysis (DA). The experimental data shows that the proposed model can achieve the best prediction performance, with a sensitivity, specificity, Matthews correlation coefficient(MCC), and accuracy(ACC) of 87.5%, 71.95%, 60.43%, and 81.65%, respectively. The experimental data proved that the HGS-MSVM model can be used for clinical auxiliary diagnosis of complex acute appendicitis.

Machine learning is now widely used in various fields, and it has made a big splash in the field of disease diagnosis. But traditional machine learning models are general-purpose, that is, one model is used to evaluate the health status of different patients. A general-purpose machine learning algorithm depends on a large amount of data and requires abundant computing power support, relies on the average level to describe the model performance, and cannot achieve optimal results on a specific problem. In this paper, we propose to train a unique model for each patient to improve the accuracy and ease of use of the model. The proposed approach to solving a problem in the paper is from three perspectives (1) targeted data processing, (2) model structure design: Passing in patient-related information into the model, and (3) hyperparameter tailored optimization. The preliminary experimental results show that using the custom model has advantages of high accuracy, high confidence, and low resource required to diagnose a patient. In the Hepatitis C dataset, over 99% accuracy and 94% recall were achieved using a smaller dataset (only 615 individuals’ data) without knowledge of the relevant field. Traditional algorithms such as XGBoost or multi-algorithm ensemble could achieve less than 95% accuracy and only less than 70% recall. Out of a total of 56 patients, the custom model was able to identify 53 patients 20 more than traditional methods, bringing a new and efficient tool for future hepatitis C prevention and treatment efforts.

Major depressive disorder (MDD) is a common and severe psychiatric illness marked by loss of interest and low energy, which result in the highest burden of disability among all mental disorders. Clinical MDD diagnosis still utilizes the phenomenological approach of syndrome-based interview, which leads to a high rate of misdiagnosis. Therefore, it is highly imperative to explore effective biomarkers to enable precise personalized diagnosis. There still exist two main challenges due to complexity of MDD and individual differences. On the one hand, discriminative features need to be investigated to better reflect the characteristics of MDD. On the other hand, the performance from shallow and static learning models is still not satisfactory. To overcome these issues, we propose a novel Multi-Stage Graph Fusion Networks (MSGFN) for major depressive disorder diagnosis. At first, functional connectivity is calculated to better characterize interactions between white matter and gray matter. Second, multi-stage features are obtained by a deep subspace learning model, and a number of graphs are constructed under the self-expression constraints at each stage. Finally, a novel graph convolutional fusion module is proposed with graph convolutional operations to integrate features and graph at each stage. Extensive experiments demonstrate the superior performance of the proposed framework. Our source code is available on: https://github.com/LIST-KONG/MSGFN-master. [ABSTRACT FROM AUTHOR]

The development of Big Data and cloud computing has brought great progress to medical diagnosis and clinical services. However, as disease diagnosis technologies usually use lots of clinical medical data, patients’ privacy becomes increasingly important since the clinical results of medical data are much sensitive. In this article, to deal with the privacy preservation issue of patient’s medical data, we propose a privacy-preserving disease diagnosis scheme that is based on the Mahalanobis distance test, in which the role of the system consisting of query user (QU), aided cloud server (ACS), and classification cloud server (CCS) is to jointly calculate and protect the diagnosis data over the sensitive and outsourced medical data. In the diagnosis model, we outsource the clinical diagnosis process to be dealt with by the CCS, and then, the ACS can reduce the computational cost of the user side. We utilize the homomorphic re-encryption scheme to realize a secure computation over the outsourced medical data, and then, employ a secure multiplication (SM) protocol to implement the privacy-preserving Mahalanobis distance to output the disease diagnosis. Concretely, we provide an extended SM algorithm to solve the problem of multiplication of two encrypted data, and a minimum value comparison algorithm over ciphertext for comparing the encrypted Mahalanobis distance. Finally, we give the experimental performance in real data sheets, and the experimental results indicate that our scheme provides a lower computational cost in practical diagnosis services. [ABSTRACT FROM AUTHOR]

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.

The recent advancements in Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. In this view, the current research article presents a new AI and IoT convergence-based disease diagnosis model for smart healthcare system. The major goal of this article is to design a disease diagnosis model for heart disease and diabetes using AI and IoT convergence techniques. The presented model encompasses different stages namely, data acquisition, preprocessing, classification, and parameter tuning. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. The proposed method uses Crow Search Optimization algorithm-based Cascaded Long Short Term Memory (CSO-CLSTM) model for disease diagnosis. In order to achieve better classification of the medical data, CSO is applied to tune both ‘weights’ and ‘bias’ parameters of CLSTM model. Besides, isolation Forest (iForest) technique is employed in this research work to remove the outliers. The application of CSO helps in considerable improvement in the diagnostic outcomes of CLSTM model. The performance of CSO-LSTM model was validated using healthcare data. During the experimentation, the presented CSO-LSTM model accomplished the maximum accuracies of 96.16% and 97.26% in diagnosing heart disease and diabetes respectively. Therefore, the proposed CSO-LSTM model can be employed as an appropriate disease diagnosis tool for smart healthcare systems.

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular ‘omics’ data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification. [ABSTRACT FROM AUTHOR]

People are admitted to intensive care units (ICUs) because they need complete support for failing organ systems, constant monitoring, routine nursing care, and treatment. A critical or intensive illness is different from conventional or chronic diseases that most people are likely to have previously encountered. Such an illness is often unexpected and without warnings and can suddenly strike the previously fit. High levels of treatment and support are generally required to prevent life-threatening complications for the patents. Two of the most noticeable actions during an ICU stay are disease diagnosis and severity assessment of the patients. Unlike the majority of previous approaches where diagnosis and severity assessment are studied separately, we treat these actions as two tasks in an integrated procedure that clinicians must be able to quickly and accurately conduct such that patients are given the best possible chance for therapeutic success. In this paper, we propose an integrated disease diagnosis and severity assessment model (IDDSAM) to diagnose and assess diseases. Moreover, accompanying the prediction, we also provide an evidence-based explanation. IDDSAM is a multisource multitask model that is based on an attention mechanism and utilizes shareable information from laboratory tests, bedside monitoring, and complications to support patients' severity assessment and in-hospital disease diagnoses. We use 50,430 ICU cases consisting of 46,520 patients from 50 kinds of diseases over nine classifications to evaluate our proposed model. The experimental results demonstrated that our model outperforms the existing state-of-the-art mortality and diagnosis prediction framework by 3.79% on average in terms of accuracy for the mortality prediction tasks and by 14.51% on average for the diagnosis tasks.

Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using `HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

To improve the recognition model accuracy of crop disease leaves and locating diseased leaves, this paper proposes an improved Faster RCNN to detect healthy tomato leaves and four diseases: powdery mildew, blight, leaf mold fungus and ToMV. First, we use a depth residual network to replace VGG16 for image feature extraction so we can obtain deeper disease features. Second, the k-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, we carry out a k-means experiment with three kinds of different feature extraction networks. The experimental results show that the improved method for crop leaf disease detection had 2.71% higher recognition accuracy and a faster detection speed than the original Faster RCNN.

The scale of medical data is growing rapidly, and these data come from different data sources. The amount of data is huge, the production speed is fast, and the format is different. Case data is very important because it contains a lot of medical knowledge about diseases, drugs, treatments, etc. It can provide important support for the development of smart medicine. Knowledge graph is a graph-based data structure, which can well represent the relationship between these medical data in reality and form a semantic network. This research uses knowledge graph technology to connect trivial and scattered knowledge in various medical information systems to assist in disease diagnosis. This research takes thyroid disease as an example, constructs a medical knowledge graph and applies it to intelligent medical diagnosis. First, extract the relationships between biomedical entities to construct a biomedical knowledge graph. Then, the entities and relationships in the knowledge graph are transformed into low-dimensional continuous vectors through the knowledge graph embedding method. Finally, the known pathological disease relationship data is used to train the disease diagnosis model of the bidirectional long short-term memory network (BSTLM). Experiments show that the thyroid disease diagnosis method that combines knowledge graphs and deep learning has a better diagnostic effect. This shows that smart medical care based on the knowledge graph will provide a solution path for alleviating the shortage of domestic high-quality medical resources.

Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

Acupoint specificity for diseases has consistently been the focus of acupuncture research owing to its excellent prospects for clinical diagnosis and treatment. However, the specificity of cardiovascular and sleep functions in terms of electrical signals at acupoints remains unclear. In this study, five volunteers were recruited and their electrophysiological signals of GV20 (baihui), RN17 (danzhong), PC6 (neiguan), and SP6 (sanyinjiao) and the corresponding sham points, Pittsburgh sleep quality index, blood pressure, and echocardiography were monitored over four periods of 90-day head-down bed rest (HDBR). The results demonstrated that the power and characteristic amplitude of the acupoints were more significant than those of the sham points under normal conditions. And along with the altered physiological condition of the body after HDBR, the differential signal characteristic amplitude (DSCA) and the power of the acupoints were decreased to a larger extent than those of the sham points. In addition, the difference between the power of acupuncture and sham points was also reduced. During the recovery period, except for GV20, the power and DSCA of other acupoints did not return to normal. In terms of DSCA, GV20 is related to human sleep function and other acupoints are related to cardiovascular function. The above results show that the electrophysiological signals of acupoints are disease-specific and more accurately reflect the changes of physiological homeostasis. The research conduces to the development of acupuncture-based disease diagnosis and treatment integrated methods, and the realization of the portable and accurate diagnosis and regulation of diseases in space medicine. [ABSTRACT FROM AUTHOR]

Big medical data pose great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists, and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of the state-of-the-practice and state-of-the-art systems handling big medical data currently or in the future? Finally, but not least, are we, life scientists, clinicians, computer scientists, and engineers, ready for working together? We believe that answering the above-mentioned issues will help define and shape the landscape of big medical data.

Diabetic retinopathy (DR) is an important disease leading to blindness in humans, attracting a lot of research interests. Previous breakthrough research findings rely on deep learning techniques to diagnose diabetic retinopathy in patients with medical imaging. Although the medical imaging achieves reasonable recognition accuracy, the application of mass, easy-to-obtain and free electronic health records (EHR) data in life can make an early diagnosis of the DR more convenient and quick. In this paper, we used a set of five machine learning models to diagnose the DR in patients with the EHR data and formed a set of treatment methods. Our experimental data set is formed by processing the data provided by 301 hospitals. The experimental results show that random forest (RF) in the machine learning model can get 92% accuracy with good performance. Subsequently, the input features were analyzed and their importance graded to find that the predisposing factors triggering the human DR disease were associated with renal and liver function. In addition, disease diagnosis methods based on readily available the EHR data will become an integral part of smart healthcare and mobile healthcare.

The functional magnetic resonance imaging (fMRI) is a noninvasive technique for studying brain activity, such as brain network analysis, neural disease automated diagnosis and so on. However, many existing methods have some drawbacks, such as limitations of graph theory, lack of global topology characteristic, local sensitivity of functional connectivity, and absence of temporal or context information. In addition to many numerical features, fMRI time series data also cover specific contextual knowledge and global fluctuation information. Here, we propose multi-scale time-series kernel-based learning model for brain disease diagnosis, based on Jensen-Shannon divergence. First, we calculate correlation value within and between brain regions over time. In addition, we extract multi-scale synergy expression probability distribution (interactional relation) between brain regions. Also, we produce state transition probability distribution (sequential relation) on single brain regions. Then, we build time-series kernel-based learning model based on Jensen-Shannon divergence to measure similarity of brain functional connectivity. Finally, we provide an efficient system to deal with brain network analysis and neural disease automated diagnosis. On Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our proposed method achieves accuracy of 0.8994 and AUC of 0.8623. On Major Depressive Disorder (MDD) dataset, our proposed method achieves accuracy of 0.9166 and AUC of 0.9263. Experiments show that our proposed method outperforms other existing excellent neural disease automated diagnosis approaches. It shows that our novel prediction method performs great accurate for identification of brain diseases as well as existing outstanding prediction tools. [ABSTRACT FROM AUTHOR]

This paper proposes a method that extracts a feature set for accurate disease diagnosis from a feature (aptamer) array. Our method uses an artificial intelligence of the neural network and 10-fold crossvalidations and is verified by the p-value of the aptamer array response to specimens of 80 liver cancer patients and 310 healthy people. The proposed method is compared with the one-way ANOVA method in terms of accuracy, the number of features, and computing time to determine the feature set required to achieve the same accuracy. An increase in the number of features dramatically improves the diagnosis accuracy of the two methods for 2-10 features. The accuracies with 10 features are 93.5% and 87.5%, and the increases in accuracy per additional feature are 3.39% and 2.65% for our method and the one-way ANOVA, respectively. For the same accuracy, our method needs only 1/2-1/3 number of features of the ANOVA. An interesting statistical characteristic of cross-validation is that diagnostic accuracy saturates after 10000 cross-validations.

Diagnosis of deadly diseases, such as liver fibrosis, is very important. Clinical decision support systems (CDSSs) based on patient's historical medical data and accurate AI techniques can aid physicians in their decision-making process. The task of arriving at an accurate and timely diagnosis decision is always complex because of the dynamic, vagueness, and uncertainty associated with this disease. Fuzzy logic can perfectly handle these issues. In recent years, two of the most interesting techniques are a fuzzy analytical hierarchy process (FAHP) and an adaptive neuro-fuzzy inference system (ANFIS). The FAHP is popular for dealing with uncertainty in multi-criteria decision-making, and the ANFIS is popular in learning fuzzy inference system from data based on artificial neural networks. To the best of our knowledge, these two methods have not been used to model CDSSs in fibrosis stage detection domain. In this paper, we develop a CDSS based on a case comparison of the effectiveness of the FAHP and the ANFIS in the medical diagnosis of the fibrosis disease. We carefully design and implement two frameworks based on these two techniques. Diagnostic real data of 119 cases infected by chronic viral hepatitis C from the Liver Institute at Mansoura University in Egypt are used to train and test both the FAHP and ANFIS. Criteria and subcriteria weights are based on opinions of two domain experts. The ANFIS model is designed using trial and error based on the analysis of various experiments. Results are later compared with the diagnostic conclusions of medical expert and other three medical and fuzzy techniques. The comparison results show that these two techniques can successfully be employed in designing a diagnostic CDSS system for fibrosis diagnosis. The two techniques achieve a classification accuracy of 93.3%. The results confirm the efficiency and effectiveness of both methods. Therefore, both the FAHP and ANFIS are viable approaches in modeling CDSS for diagnosis of a liver fibrosis stage.

Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson’s and Alzheimer’s diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive neurodegenerative motor system disorder. Early diagnosis of PD is important to control the symptoms appropriately. Recent voice and speech recognition techniques provide alternative solutions for PD screening. In this paper, an optimal support vector machine (SVM) based on bacterial foraging optimization (BFO) was established to predict PD effectively. The effectiveness of the proposed method, BFO-SVM, was validated on a PD data set based on vocal measurements. The proposed method was compared with two of the most frequently used parameter optimization methods, including an SVM based on the grid search method and an SVM based on particle swarm optimization. Additionally, to further boost the prediction accuracy, the relief feature selection was employed prior to the BFO-SVM method, consequently the RF-BFO-SVM was proposed. The experimental results have demonstrated that the proposed framework exhibited excellent classification performance with a superior classification accuracy of 97.42%.

Traditional approaches based on short-term heart rate variability for cardiovascular disease diagnosis fail to capture the long-term dynamic information and individual effect from electrocardiography signals among subjects when examining the physiological condition. These shortages may lead to incorrect disease detection and weaken diagnosis performance. To address these problems, this paper proposes a new disease detection approach by considering the long-term dynamics and meanwhile the individual effect existing among subjects. Specifically, a multistate general Gamma cumulative sum (GGCUSUM) scheme is developed for signal state detection. Further, a backward elimination algorithm based on the exponential likelihood ratio test (ELRT) is proposed to reduce the risk of incorrect detection of change points. A general disease severity index is then designed based on our approach to satisfy the clinical requirement for disease diagnosis. A real clinical case from one of cardiovascular diseases, is given to validate the proposed approach, of which the result demonstrates the effectiveness with a satisfactory detection performance. [ABSTRACT FROM AUTHOR]

Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods. [ABSTRACT FROM AUTHOR]

Expert systems have been applied to solve agricultural problems for some time. The complexities involved in plant disease diagnosis make this problem a prime candidate for the application of expert systems. Those same complexities, however, make the development of a viable system a very challenging task. Many attempts at creating such tools have been reported in the literature, with different scopes and degrees of success. This article had the objective of organizing all the information that has been published on the subject in the last four decades by presenting a brief individual description of each proposed system, and then condensing all the information into a critical analysis of how the problem was handled in the past, how it evolved into the current scenario, and which are the possible directions to be explored in the future. [ABSTRACT FROM PUBLISHER]

High-throughput cell imaging and nanotechnology are emerging technologies that exhibit great potential for disease diagnosis, prognosis, and treatment. This paper briefly introduces these emerging technologies and discusses challenging open research issues to fully realize the potentials of these new tools. [ABSTRACT FROM PUBLISHER]

Isolating rare cells from biological fluids including whole blood or bone marrow is an interesting biological problem. Characterization of a few metastatic cells from cancer patients for further study is desirable for prognosis/diagnosis. Traditional methods have not proven adequate, due to the compositional complexity of blood, with its large numbers of cell types. To separate individual cells based on their mechanical characteristics, we have developed a series of massively parallel microfabricated sieving device. These devices were constructed with four successively narrower regions of channels numbering ∼1800 per region. As cells traversed the device, they encountered each region and stopped at a gap width that prohibited passage due to their size. Cultured neuroblastoma cells, when mixed with whole blood and applied to the device, were retained in the 10-μm-wide by 20-μm-deep channels. All other cells migrated to the output. A derivative of the same device was utilized to characterize migration of whole blood. Adult white blood cells were retained at the 2.5-μm-wide by 5-μm-deep channels, while red blood cells passed through these channels. Devices designed to capture rare cells in peripheral circulation for downstream analysis will provide an important tool for diagnosis and treatment. [ABSTRACT FROM PUBLISHER]