Health Risk Management

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  Patient Subtyping via Time-Aware LSTM Networks

  • Published on: August, 2017
  • Xi Zhang, Anil K. Jain, Fei Wang, Jiayu Zhou, Cao Xiao, Inci M. Baytas
  • Code Available

  Grouping of patients based on similar disease progression pathways, also known as Patient Subtyping is extremely important to account heterogeneity in ...

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  AutoDiscern-Rating the Quality of Online Health Information with Hierarchical Encoder Attention-Based Neural Networks

  • Published on: May, 2020
  • Michael Krauthammer, Ahmed Allam, Laura Kinkead
  • Code Available

  A huge amount of medical information is available online and people refer to it before consulting a health professional. The information may not be r ...

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  CT Based COVID-19 triage Deep multitask learning improves joint identification and severity quantification

  • Published on: March, 2021
  • MikhailGoncharov, MaximPisov
  • Code Available

  Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem ...

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  MRI Brain Tumor Segmentation and Uncertainty Estimation using 3D-UNet Architectures

  • Published on: September, 2020
  • Veronica Vilaplana, Laura Mora Ballestar
  • Code Available

  Convolutional neural networks have shown significant results in brain tumor segmentation. Automation of brain tumor segmentation can be very useful fo ...

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  Predicting the deterioration of COVID-19 patients in the emergency department

  • Published on: November, 2020
  • Yvonne W. Lui, David Kudlowitz, Taro Makino, Ben Zhang, Lea Azour, Carlos Fernandez-Granda, Siddhant Dogra, Yi...
  • Code Available

  During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decis ...

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  Co occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation

  • Published on: March, 2019
  • Max Strobel
  • Code Available

  Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets. The most cruc ...

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  Cardiologist-level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using A Deep Neural Network

  • Published on: July, 2017
  • Masoumeh Haghpanahi, Awni Y. Hannun, Codie Bourn, Pranav Rajpurkar, Andrew Y. Ng
  • Code Available

  Arrhythmia is a condition of improper heart beating, which is diagnosed with help of electrocardiograms(ECG ). In this model an algorithm is applied w ...

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  Using Machine Learning of Clinical Data to Diagnose COVID-19-A Systematic Review and Meta-analysis

  • Published on: September, 2020
  • Wei Tse Li, Jiayan Ma, Grant Castaneda et al., Neil Shende
  • Code Available

  The model is designed to predict whether a patient has Covid-19 or influenza based on clinical data. Testing patient rapidly for Covid-19 is a key ste ...

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  Heart Segmentation From MRI Scans Using Convolutional Neural Network

  • Published on: November, 2019
  • Muhammad Usman, Shakeel Muhammad Ibrahim,  Imran Naseem, Muhammad Sohail Ibrahim, Muhammad Moinuddin
  • Code Available

  Heart is essential organ of human body. For patients having cardiovascular diseases(CVD), it is important to monitor physiology of heart effectively. ...

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  Attend and Predict-Understanding Gene Regulation by Selective Attention on Chromatin

  • Published on: May, 2018
  • Jack Lanchantin, Ritambhara Singh, Arshdeep Sekhon, Yanjun Qi
  • Code Available

  AttentiveChrome is an attention-based deep learning model that uses histone modification signals covering the gene’s neighboring DNA region of Transcr ...