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From a biological standpoint, due to the special combination of complex symptoms, some type of complex diseases is difficult to be accurately diagnosed. Known as phenotypic overlap, these sets of disease-related symptoms reveal a common pathological and physiological mechanism. Researchers attempt to visualize the phenotypic relationships between different human diseases from the perspective of machine...
Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record...
With the emergence of deep-learning algorithms, the accuracy of computer-aided supporting systems advanced., However, their adoption in the field of medicine has been limited, partially due to the challenges of generating reliable and timely results. In this research, we focused on classifying four common cutaneous diseases based on dermoscopic images using deep learning algorithms.
In the field of histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help define precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. One of the severe brain tumors is the Glioma can provide unique insights into identifying and grading disease stages....
Malaria is one of the world's serious diseases causing death of about half a million people in 2015. The protozoan Plasmodium Falciparum inflicts the most damage and is responsible for most malaria related deaths. Biomedical research could enable treating the disease by effectively and specifically targeting essential enzymes of this parasite. However, the parasite has developed resistance to existing...
Diabetes is one of the most prevalent diseases worldwide, and hundreds of millions of patients are suffering from diabetes and its serious complications. Early detection and early treatment are urgent needed for clinical diagnosis of diabetics. In this work, we establish a gene coexpression network framework to identify biomarkers of transcripts with highly different gene coexpression patterns in...
Heart disease is a deadly disease that large population of people around the world suffers from. When considering death rates and large number of people who suffers from heart disease, it is revealed how important early diagnosis of heart disease. Traditional way of diagnosis is not sufficient for such an illness. Developing a medical diagnosis system based on machine learning for prediction of heart...
In this paper, a transfer bi-directional recurrent neural networks (RNN) is proposed for named entity recognition (NER) in Chinese electronic medical records (EMRs) that aims to extract medical knowledge such as phrases recording diseases and treatments automatically. We propose a two-step procedure where the first step is to train a shallow bi-directional RNN in the general domain, and the second...
Gram staining is a traditional bacteriological laboratory technique, which has widely usage on many medical research and application. However, gram staining reading is a time consumption work. In this paper, we employ Convolutional Neural Network method to design a classifier, by which gram staining images can be identified as normal group and disease model group effectively and correctly. And image...
Cough sound analysis has attracted interest as a potential low-cost diagnostic tool for low-resource settings, where the burden of pulmonary disease is quite high. However, published results on cough sound analysis are generally limited to specific pulmonary diseases (e.g. detection of Whooping cough — Pertussis) and the study sizes are small. In this paper, we present a general framework for cough...
In the present project it is focused on patients Amotrophy Lateral Sclerosis (ALS), so these patients do not have control of their motor functions therefore are unable to move on their own, requiring third party assistance to move with his wheelchair. Patients of Amyotrophic Lateral Sclerosis do not lose their cognitive ability, which is why you can use it to control his wheelchair as part of a computer...
Even with an annual expenditure of more than $3 trillion, the U.S. healthcare system is far from optimal. For example, the third leading cause of death in the U.S. is preventable medical error, immediately after heart disease and cancer. Computer-based clinical decision support systems (CDSSs) have been proposed to address such deficiencies and have significantly improved clinical practice over the...
Timely and robust diagnosis of plant diseases and nutrient deficiencies play a major role in management of crop yield. Automation is a low cost alternative to human experts and can help to detect early onset of crop diseases which aids faster decision making and in giving recommendations to farmers to curb yield loss. We have developed a smart-phone based participatory sensing application for agriculture...
Tuberculosis (TB) is a major health threat in the developing countries. Many patients die every year due to lack of treatment and error in diagnosis. Developing a computer-aided diagnosis (CAD) system for TB detection can help in early diagnosis and containing the disease. Most of the current CAD systems use handcrafted features, however, lately there is a shift towards deep-learning-based automatic...
Diabetic Macular Edema (DME) is a common eye disease that causes irreversible vision loss for diabetic patients, if left untreated. Thus, early diagnosis of DME could help in early treatment and prevent blindness. This paper aims to create a framework based on deep learning for DME recognition on Spectral Domain Optical Coherence Tomography (SD-OCT) images through transfer learning. First, images...
Health informatics has emerged as a growing domain of interest among researchers world-wide owing to its major implications on society. Applications of machine learning in healthcare range from disease prediction to patient-level personalized services. The prevalence of big data in healthcare has paved the way for applications based on deep learning techniques in the past few years. This paper reviews...
Attention deficit hyperactivity disorder creates conditions for the child as s/he cannot sit calm and still, control his/her behavior and focus his/her attention on a particular issue. Five out of every hundred children are affected by the disease. Boys are three times more than girls at risk for this complication. The disorder often begins before age seven, and parents may not realize their children...
The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or...
The current work aims to identify patients with atopic dermatitis for inclusion in genome-wide association studies (GWAS). Here we describe a machine learning-based phenotype algorithm. Using the electronic health record (EHR), we combined coded information with information extracted from encounter notes as features in a lasso logistic regression. Our algorithm achieves high positive predictive value...
Chronic heart failure represents a global pandemic, currently affecting over 26 million of patients worldwide. It is a major contributor in the death rate of patients with cardiovascular diseases and results in more than 1 million hospitalizations annually in Europe and North America. Methods for chronic heart failure detection can be utilized to act preventive, improve early diagnosis and avoid hospitalizations...
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