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 Generating Multi-label Discrete Patient Records using Generative Adversarial Networks

  

Access to electronic health record (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing synthetic EHR data could mitigate risk. In this paper, we propose a new approa…


 A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines

Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as the input dimensionality increases, hence the inter…


 Deep Architectures for Automated Seizure Detection in Scalp EEGs

   

Automated seizure detection using clinical electroencephalograms is a challenging machine learning problem because the multichannel signal often has an extremely low signal to noise ratio. Events of interest such as seizures are easily confused with signal artifacts (e.g, eye movements) or benign v…


 Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures

 

Objective: A clinical decision support tool that automatically interprets EEGs can reduce time to diagnosis and enhance real-time applications such as ICU monitoring. Clinicians have indicated that a sensitivity of 95% with a specificity below 5% was the minimum requirement for clinical acceptance.…


 Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning

 

Physical activity and sleep play a major role in the prevention and management of many chronic conditions. It is not a trivial task to understand their impact on chronic conditions. Currently, data from electronic health records (EHRs), sleep lab studies, and activity/sleep logs are used. The rapid…


 Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding

 

Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approa…


 Virtual Sensor Modelling using Neural Networks with Coefficient-based Adaptive Weights and Biases Search Algorithm for Diesel Engines

With the explosion in the field of Big Data and introduction of more stringent emission norms every three to five years, automotive companies must not only continue to enhance the fuel economy ratings of their products, but also provide valued services to their customers such as delivering engine p…


 Detection and classification of masses in mammographic images in a multi-kernel approach

  

According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality …


 Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images

 

Breast cancer is already one of the most common form of cancer worldwide. Mammography image analysis is still the most effective diagnostic method to promote the early detection of breast cancer. Accurately segmenting tumors in digital mammography images is important to improve diagnosis capabiliti…


 Collaborating with patients for better outcomes

Collaborating with patients for better outcomes by DeepMind


 Applying machine learning to mammography screening for breast cancer

Applying machine learning to mammography screening for breast cancer by DeepMind


 Understanding Medical Conversations

   

Posted by Katherine Chou, Product Manager and Chung-Cheng Chiu, Software Engineer, Google Brain Team Good documentation helps create good clinical care by communicating a doctor’s thinking, their concerns, and their plans to the rest of the team. Unfortunately, physicians routinely spend more…


 Learning compressed representations of blood samples time series with missing data

 

Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data. An autoencoder can learn low dimensional vectorial representations of MTS that preserve important data characteristics, but cannot deal explicitly with missing da…


 Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery for Pathologically-Proven Lung Cancer Detection

While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features which m…


 Deceased Organ Matching in Australia

Despite efforts to increase the supply of organs from living donors, most kidney transplants performed in Australia still come from deceased donors. The age of these donated organs has increased substantially in recent decades as the rate of fatal accidents on roads has fallen. The Organ and Tissue…


 Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

  

An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide …


 Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks

 

With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for the continuous monitoring of patients at risk. We utilise an annotated dataset of 12,186 single-lead ECG recordings to bu…


 Using artificial intelligence to improve early breast cancer detection

Model developed at MIT’s Computer Science and Artificial Intelligence Laboratory could reduce false positives and unnecessary surgeries. Using artificial intelligence to improve early breast cancer detection by Adam Conner-Simons | CSAIL


 Safe Medicine Recommendation via Medical Knowledge Graph Embedding

Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big…


 Checkpoint Ensembles: Ensemble Methods from a Single Training Process

   

We present the checkpoint ensembles method that can learn ensemble models on a single training process. Although checkpoint ensembles can be applied to any parametric iterative learning technique, here we focus on neural networks. Neural networks’ composable and simple neurons make it possibl…


 Protein identification with deep learning: from abc to xyz

    

Proteins are the main workhorses of biological functions in a cell, a tissue, or an organism. Identification and quantification of proteins in a given sample, e.g. a cell type under normal/disease conditions, are fundamental tasks for the understanding of human health and disease. In this paper, we…


 Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis`

  

This work handles the inverse reinforcement learning (IRL) problem where only a small number of demonstrations are available from a demonstrator for each high-dimensional task, insufficient toestimate an accurate reward function. Observing that each demonstrator has an inherent reward for each stat…


 End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design

 

We develop an end-to-end training algorithm for whole-image breast cancer diagnosis based on mammograms. It requires lesion annotations only at the first stage of training. After that, a whole image classifier can be trained using only image level labels. This greatly reduced the reliance on lesion…


 The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy

We used neural networks in ~3,000 sleep recordings from over 10 locations to automate sleep stage scoring, producing a probability distribution called an hypnodensity graph. Accuracy was validated in 70 subjects scored by six technicians (gold standard). Our best model performed better than any ind…


 Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. Fi…