A variety of deep learning architectures have been developed for the goal of predictive modelling and knowledge extraction from medical records. Several models have placed strong emphasis on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability

Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, three-stream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis

Recently, several schemes are proposed for enhancing the dark regions of the skeletal scintigraphy image. Nevertheless, most of them are flawed by some performance problems. This paper presents an adaptive scheme based on Salp Swarm algorithm (SSA) and a neutrosophic set (NS) under multi-criteria to enhance the dark regions of the skeletal scintigraphy image efficiently. Enhancing the dark regions

Two algorithms for explaining decisions of a lung cancer computer-aided diagnosis system are proposed. Their main peculiarity is that they produce explanations of diseases in the form of special sentences via natural language. The algorithms consist of two parts. The first part is a standard local post-hoc explanation model, for example, the well-known LIME, which is used for selecting important features

Traditional Chinese Medicine (TCM) considers that the personal constitution determines the occurrence trend and therapeutic effects of certain diseases, which can be recognized by machine learning through tongue images. However, current machine learning methods are confronted with two challenges. First, there are not some larger tongue image databases available. Second, they do not use the domain knowledge

Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not a straightforward task mainly due to the heterogeneity of tumors, the variability

An electronic medical record (EMR) is a rich source of clinical information for medical studies. Each physician usually has his or her own way to describe a patient's diagnosis. This results in many different ways to describe the same disease, which produces a large number of informal nonstandard diagnoses in EMRs. The Tenth Revision of International Classification of Diseases (ICD-10) is a medical

Microarray gene expression profiling has emerged as an efficient technique for cancer diagnosis, prognosis, and treatment. One of the major drawbacks of gene expression microarrays is the “curse of dimensionality”, which hinders the usefulness of information in datasets and leads to computational instability. In recent years, feature selection techniques have emerged as effective tools to identify

Evidence-Based Medicine (EBM) has been an important practice for medical practitioners. However, as the number of medical publications increases dramatically, it is becoming extremely difficult for medical experts to review all the contents available and make an informative treatment plan for their patients. A variety of frameworks, including the PICO framework which is named after its elements (Population

Alzheimer's disease (AD) is now difficult to be identified for clinicians, especially, at its prodromal stage, mild cognitive impairment (MCI), because of no obvious clinical symptom and few impacts on daily life at this phase. In addition, energy distribution differences of brain atrophies reflected in structural magnetic resonance imaging (sMRI) images between MCI patients and older healthy controls

Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of

Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the diagnoses often challenge pathologists. Here we propose a rule-based system to differentiate thyroid nodules from intraoperative frozen sections using deep learning techniques. The proposed system consists of three components: (1) automatically locating tissue regions in the whole slide images (WSIs)

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality

Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet)

In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting many virtual care services—such as tailoring educational content towards an individual’s current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive

Progress in proteomics has enabled biologists to accurately measure the amount of protein in a tumor. This work is based on a breast cancer data set, result of the proteomics analysis of a cohort of tumors carried out at Karolinska Institutet. While evidence suggests that an anomaly in the protein content is related to the cancerous nature of tumors, the proteins that could be markers of cancer types

Temporal phenotyping enables clinicians to better understand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories. These models will enable

Objective In this paper, we proposed new methods for feature extraction in machine learning-based classification of atrial fibrillation from ECG signal. Methods Our proposed methods improved conventional 1-dimensional local binary pattern method in two ways. First, we proposed a dynamic threshold LBP code generation method for use with 1-dimensional signals, enabling the generated LBP codes to have

Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and health care practitioners. Skin lesion restoration is an essential pre-processing step for lesion enhancements for accurate automated analysis and diagnosis by both dermatologists and computer-aided diagnosis tools. Hair occlusion is one of the most popular artifacts in dermatoscopic

Automated detection of dynamical change in EEG signals has been a long-standing problem in a wide range of clinic applications. It is essential to extract an effective and accurate EEG rhythm indicator that can reflect the dynamical behavior of a given EEG signal. Time-frequency analysis is a promising method to achieve this end, but existing methods still have limitations in real implementation making

Nowadays, the demand for segmenting different types of cells imaged by microscopes is increased tremendously. The requirements for the segmentation accuracy are becoming stricter. Because of the great diversity of cells, no traditional methods could segment various types of cells with adequate accuracy. In this paper, we aim to propose a generic approach that is capable of segmenting various types

Machine learning (ML) approaches have been widely applied to medical data in order to find reliable classifiers to improve diagnosis and detect candidate biomarkers of a disease. However, as a powerful, multivariate, data-driven approach, ML can be misled by biases and outliers in the training set, finding sample-dependent classification patterns. This phenomenon often occurs in biomedical applications

Background Cancer remains the second major cause of death in the United States over the last decade. Chemotherapy is a core component of nearly every cancer treatment plan. Chemotherapy-Induced Nausea and Vomiting (CINV) are the two most dreadful and unpleasant side-effects of chemotherapy for cancer patients. Several patient-specific factors affect the risk of CINV. However, none of the guidelines

Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made by MKN-based clinical decision support system depends on the accuracy of medical

The early detection of Alzheimer’s disease can potentially make eventual treatments more effective. This work presents a deep learning model to detect early symptoms of Alzheimer’s disease using synchronization measures obtained with magnetoencephalography. The proposed model is a novel deep learning architecture based on an ensemble of randomized blocks formed by a sequence of 2D-convolutional, batch-normalization

The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical

Objectives Lung cancer is the leading cause of cancer death worldwide. Prognosis of lung cancer plays a crucial role in the clinical decision-making process to optimize the treatment for patients. Most of the existing data-driven prognostic prediction models explore the relations between patient’s characteristics and outcomes at a specific time interval. Although valuable, they neglect the relations

Blood glycemic control is crucial for minimizing severe side effects in diabetes mellitus. Currently, two opposing treatment approaches exist: in formulaic methods, insulin care is calculated by parameter-based computation (i.e., correction factor, insulin-to-carb ratio, and absorption duration), which are fixed by the medical team based on the history of a tested patient blood glucose levels (BGLs)

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement methods based on pulse transit time or multiple parameters require simultaneous electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Compared with PPG signals, ECG signals are easy to collect using wearable devices. This study examined a novel continuous

Recognition of activities of daily living (ADL) is an essential component of assisted living systems based on actigraphy. This task can nowadays be performed by machine learning models which are able to automatically extract and learn relevant features but, most of time, need to be trained with large amounts of data collected on several users. In this paper, we propose an approach to learn personalized

Patients with advanced cancer are burdened physically and psychologically, so there is an urgent need to pay more attention to their health-related quality of life (HRQOL). With an expected clinical endpoint prediction, over-treatment can be effectively eliminated by the means of palliative care at the right time. This paper develops a deep learning based approach for cancer clinical endpoint prediction

Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can

Patients suffering from Barrett's Esophagus (BE) are at an increased risk of developing esophageal adenocarcinoma and early detection is crucial for a good prognosis. To aid the endoscopists with the early detection for this preliminary stage of esophageal cancer, this work concentrates on the development and extensive evaluation of a state-of-the-art computer-aided classification and localization

Healthcare industry is the leading domain that has been revolutionized by the incorporation of Internet of Things (IoT) technology resulting in smart medical applications. Conspicuously, this study presents an effective system of home-centric Urine-based Diabetes (UbD) monitoring system. Specifically, the proposed system comprises of 4-layers for predicting and monitoring diabetes-oriented urine infection

Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Automated brain hematoma segmentation and outcome prediction for patients with TBI can effectively facilitate patient management. In this study, we propose a novel Multi-view convolutional neural network with a mixed loss to segment total acute hematoma on head CT scans collected within 24 h after the injury. Based on

Objective The aim of this study is to compute similarities between patient records in an electronic health record (EHR). This is an important problem because the availability of effective methods for the computation of patient similarity would allow for assistance with and automation of tasks such as patients stratification, medical prognosis and cohort selection, and for unlocking the potential of

Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare. Hitherto, research works have not investigated the types of medical conditions being modelled with BNs, nor whether there are any differences in how and why they are applied to different conditions. This research seeks to identify

The problem of the explainability of AI decision-making has attracted considerable attention in recent years. In considering AI diagnostics we suggest that explainability should be explicated as ‘effective contestability’. Taking a patient-centric approach we argue that patients should be able to contest the diagnoses of AI diagnostic systems, and that effective contestation of patient-relevant aspect

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image segmentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances

Significant progress has been achieved in recent years in the application of artificial intelligence (AI) for medical decision support. However, many AI-based systems often only provide a final prediction to the doctor without an explanation of its underlying decision-making process. In scenarios concerning deadly diseases, such as breast cancer, a doctor adopting an auxiliary prediction is taking

Infection of bone, osteomyelitis (OM), is a serious bacterial infection in children requiring urgent antibiotic therapy. While biological specimens are often obtained and cultured to guide antibiotic selection, culture results may take several days, are often falsely negative, and may be falsely positive because of contamination by non-causative bacteria. This poses a dilemma for clinicians when choosing

Atrial Fibrillation (AF) at an early stage has a short duration and is sometimes asymptomatic, making it difficult to detect. Although the use of mobile sensing devices has provided the possibility of real-time cardiac detection, it is highly susceptible to the noise signals generated by body movement. Therefore, it is of great importance to study early AF detection for mobile terminals with noise

Pap smear is often employed as a screening test for diagnosing cervical pre-cancerous and cancerous lesions. Accurate identification of dysplastic changes amongst the cervical cells in a Pap smear image is thus essential for rapid diagnosis and prognosis. Manual pathological observations used in clinical practice require exhaustive analysis of thousands of cell nuclei in a whole slide image to visualize

Learning a Bayesian network is a difficult and well known task that has been largely investigated. To reduce the number of candidate graphs to test, some authors proposed to incorporate a priori expert knowledge. Most of the time, this a priori information between variables influences the learning but never contradicts the data. In addition, the development of Bayesian networks integrating time such

Regular medical records are useful for medical practitioners to analyze and monitor patient's health status especially for those with chronic disease. However, such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based models have been developed for missing data imputation in recent papers. This approach makes

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of clinical imaging patterns into healthy and diseased states. We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of

In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised breast ultrasound image analysis. This task is time consuming and there is inconsistency among human experts. Researchers attempting to automate the process of obtaining the ROIs have been relying on image processing and conventional machine learning methods

Accurate diagnoses of specific diseases require, in general, the review of the whole medical history of a patient. Currently, even though many advances have been made for disease monitoring, domain experts are still requested to perform direct analyses in order to get a precise classification, thus implying significant efforts and costs. In this work we present a framework for automated diagnosis based

Background Health utilities express the perceptions patients have on the impact potential adverse events of medical treatments may have on their quality of life. Being able to accurately assess health utilities is crucial when deciding what is the best treatment when multiple and diverse treatment options exist, or when performing a cost / utility analysis. Due to the emotional and other complexities

Background Two common issues may arise in certain population-based breast cancer (BC) survival studies: I) missing values in a survivals’ predictive variable, such as “Stage” at diagnosis, and II) small sample size due to “imbalance class problem” in certain subsets of patients, demanding data modeling/simulation methods. Methods We present a procedure, ModGraProDep, based on graphical modeling (GM)

Computer-aided detection (CADe) systems play a crucial role in pulmonary nodule detection via chest radiographs (CXRs). A two-stage CADe scheme usually includes nodule candidate detection and false positive reduction. A pure deep learning model, such as faster region convolutional neural network (faster R-CNN), has been successfully applied for nodule candidate detection via computed tomography (CT)

Graph signal processing (GSP) is a framework that enables the generalization of signal processing to multivariate signals described on graphs. In this paper, we present an approach based on Graph Fourier Transform (GFT) and machine learning for the analysis of resting-state functional magnetic resonance imaging (rs-fMRI). For each subject, we use rs-fMRI time series to compute several descriptive statistics

Window settings to rescale and contrast stretch raw data from radiographic images such as Computed Tomography (CT), X-ray and Magnetic Resonance images is a crucial step as data pre-processing to examine abnormalities and diagnose diseases. We propose a distant-supervised method for determining automatically the best window settings by attaching a window estimator module (WEM) to a deep convolutional

The clinical assessment after stroke depends on the rating scale, usually lack of quantitative feedback such as biomedical signal captured from stroke patients. This study attempts to develop a unified assessment framework for persons after stroke via surface electromyography (sEMG) bias from bilateral limbs, based on four types of selected movements, namely forward lift arm, lateral lift arm, forearm