Topic Tag: health

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 Discriminant chronicles mining: Application to care pathways analytics

Pharmaco-epidemiology (PE) is the study of uses and effects of drugs in well defined populations. As medico-administrative databases cover a large part of the population, they have become very interesting to carry PE studies. Such databases provide longitudinal care pathways in real condition conta…


 Balancing Lexicographic Fairness and a Utilitarian Objective with Application to Kidney Exchange

Balancing fairness and efficiency in resource allocation is a classical economic and computational problem. The price of fairness measures the worst-case loss of economic efficiency when using an inefficient but fair allocation rule; for indivisible goods in many settings, this price is unacceptabl…


 Feature selection in high-dimensional dataset using MapReduce

This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open …


 Phylogenetic Convolutional Neural Networks in Metagenomics

    

Background: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data ba…


 Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients

Sepsis is a condition caused by the body’s overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Common signs and symptoms include fever, increased heart rate, increased breathing rate, and confusion. Sepsis is difficult to…


 Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records

   

The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing…


 Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals

Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we prese…


 Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction

 

This work presents a novel semi-supervised learning approach for data-driven modeling of asset failures when health status is only partially known in historical data. We combine a generative model parameterized by deep neural networks with non-linear embedding technique. It allows us to build progn…


 Deep Reinforcement Learning: An Overview

      

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, includi…


 Learning what to read: Focused machine reading

 

Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today’s sc…


 Clustering Patients with Tensor Decomposition

 

In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem using tensor decomposition. We present the reasons why this approach is preferab…


 Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

  

We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…


 Intel & MobileODT Cervical Cancer Screening Competition, 1st Place Winner’s Interview: Team ‘Towards Empirically Stable Training’

 

In June of 2017, Intel partnered with MobileODT to challenge Kagglers to develop an algorithm with tangible, real-world impact–accurately identify a woman’s cervix type in images. This is really important because assigning effective cervical cancer treatment depends on the doctor’s ab…