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medical

Computational Phenotyping

3260 papers • 126 benchmarks • 313 datasets

Computational Phenotyping is the process of transforming the noisy, massive Electronic Health Record (EHR) data into meaningful medical concepts that can be used to predict the risk of disease for an individual, or the response to drug therapy. Source: Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis

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Benchmarks

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Libraries

Use these libraries to find computational-phenotyping models and implementations

Datasets

Rare Diseases Mentions in MIMIC-III

Subtasks

Patient Phenotyping

Most implemented papers

Multitask learning and benchmarking with clinical time series data

A. Galstyan, Hrant Khachatrian, Hrayr Harutyunyan, David C. Kale•Tue Mar 21 2017

This work proposes four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database, covering a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification.

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PMS-Net: Robust Haze Removal Based on Patch Map for Single Images

Wei-Ting Chen, Jian-Jiun Ding, S. Kuo•Fri May 31 2019

In this paper, we proposed a novel haze removal algorithm based on a new feature called the patch map. Conventional patch-based haze removal algorithms (e.g. the Dark Channel prior) usually performs dehazing with a fixed patch size. However, it may produce several problems in recovered results such as oversaturation and color distortion. Therefore, in this paper, we designed an adaptive and automatic patch size selection model called the Patch Map Selection Network (PMS-Net) to select the patch size corresponding to each pixel. This network is designed based on the convolutional neural network (CNN), which can generate the patch map from the image to image. Experimental results on both synthesized and real-world hazy images show that, with the combination of the proposed PMS-Net, the performance in haze removal is much better than that of other state-of-the-art algorithms and we can address the problems caused by the fixed patch size.

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PMHLD: Patch Map-Based Hybrid Learning DehazeNet for Single Image Haze Removal

Wei-Ting Chen, H. Fang, Jian-Jiun Ding, S. Kuo•Wed May 13 2020

Images captured in a hazy environment usually suffer from bad visibility and missing information. Over many years, learning-based and handcrafted prior-based dehazing algorithms have been rigorously developed. However, both algorithms exhibit some weaknesses in terms of haze removal performance. Therefore, in this work, we have proposed the patch-map-based hybrid learning DehazeNet, which integrates these two strategies by using a hybrid learning technique involving the patch map and a bi-attentive generative adversarial network. In this method, the reasons limiting the performance of the dark channel prior (DCP) have been analyzed. A new feature called the patch map has been defined for selecting the patch size adaptively. Using this map, the limitations of the DCP (e.g., color distortion and failure to recover images involving white scenes) can be addressed efficiently. In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously. A combination of traditional and learning-based methods can efficiently improve the haze removal performance of the network. Experimental results show that the proposed method can achieve better reconstruction results compared to other state-of-the-art haze removal algorithms.

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Unsupervised Learning for Computational Phenotyping

Chris Hodapp•Sun Dec 25 2016

This work implements a method derived strongly from Lasko et al., but implements it in Apache Spark and Python and generalizes it to laboratory time-series data in MIMIC-III, showing great potential for finding patterns in Electronic Health Records that would otherwise be hidden and that can lead to greater understanding of conditions and treatments.

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Learning Inter-Modal Correspondence and Phenotypes From Multi-Modal Electronic Health Records

Kejing Yin, W. K. Cheung, B. Fung, Jonathan Poon•Wed Nov 11 2020

The collective hidden interaction tensor factorization (cHITF) is proposed to infer the correspondence between multiple modalities jointly with the phenotype discovery to discover phenotypes that are more clinically relevant and diverse, and achieves better predictive performance compared with a number of state-of-the-art computational phenotyping models.

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Computational Phenotyping | State-of-the-Art