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Deep EHR: Chronic Disease Prediction Using Medical Notes [article]

Jingshu Liu, Zachariah Zhang, Narges Razavian
<span title="2018-08-15">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information.  ...  We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease  ...  EHR: CHRONIC DISEASE PREDICTION USING MEDICAL NOTESAcknowledgmentsWe would like to express special thanks to Yin Aphinyanaphongs, Marina Marin, Himanshu Grover at NYUMC Predictive Analytics Unit, Michael  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1808.04928v1">arXiv:1808.04928v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hlg5zfmhb5hepllmv5aa6v6eaa">fatcat:hlg5zfmhb5hepllmv5aa6v6eaa</a> </span>
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Obesity Prediction with EHR Data: A deep learning approach with interpretable elements [article]

Mehak Gupta, Thao-Ly T. Phan, Timothy Bunnell, Rahmatollah Beheshti
<span title="2021-10-22">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We train our proposed model on both dynamic and static EHR data. Our model is used to predict obesity for ages between 2-20 years.  ...  In this paper, we present a deep learning model designed for predicting future obesity patterns from generally available items on children medical history.  ...  Childhood obesity can continue into adulthood and is known to be a major risk factor for chronic diseases such as diabetes, cancer, and cardiovascular diseases [2] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.02655v6">arXiv:1912.02655v6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4ums2og5n5as3dlzlotcj5pid4">fatcat:4ums2og5n5as3dlzlotcj5pid4</a> </span>
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Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review [article]

Yuqi Si, Jingcheng Du, Zhao Li, Xiaoqian Jiang, Timothy Miller, Fei Wang, W. Jim Zheng, Kirk Roberts
<span title="2020-11-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This is generally performed using advanced deep learning methods.  ...  Recurrent Neural Networks were widely applied as the deep learning architecture (LSTM: 13 studies, GRU: 11 studies). Disease prediction was the most common application and evaluation (31 studies).  ...  while 6 papers used unstructured notes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.02809v2">arXiv:2010.02809v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rtl2mqq2fzec3cusp4wo635qvm">fatcat:rtl2mqq2fzec3cusp4wo635qvm</a> </span>
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Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data

Yu-Hsiang Wang, Phung-Anh Nguyen, Md Mohaimenul Islam, Yu-Chuan Li, Hsuan-Chia Yang
<span title="2019-08-21">2019</span> <i title="IOS Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4ju2ftoebbcvrjj6lqnnbh42wq" style="color: black;">Studies in Health Technology and Informatics</a> </i> &nbsp;
We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults.  ...  We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model.  ...  -H.Wang et al. / Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/shti190259">doi:10.3233/shti190259</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31437961">pmid:31437961</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/glxnhalp2ffane2fofxp5oxi7e">fatcat:glxnhalp2ffane2fofxp5oxi7e</a> </span>
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Deep neural network models for identifying incident dementia using claims and EHR datasets

Vijay S. Nori, Christopher A. Hane, Yezhou Sun, William H. Crown, Paul A. Bleicher, Kewei Chen
<span title="2020-09-24">2020</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
This study investigates the use of deep learning methods to improve the accuracy of a predictive model for dementia, and compares the performance to a traditional machine learning model.  ...  Deep learning models can result in significant improvement in performance but come at a cost in terms of run times and hardware requirements.  ...  Recent work [12] on predicting dementia not including Mild Cognitive Impairment (MCI), 1 or 3 years prior to the onset of the disease using ML models trained on EMR data (diagnosis, medical notes, prescriptions  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0236400">doi:10.1371/journal.pone.0236400</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32970677">pmid:32970677</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hkbiqi57tzbopfmofgib4oip4u">fatcat:hkbiqi57tzbopfmofgib4oip4u</a> </span>
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TAPER: Time-Aware Patient EHR Representation [article]

Sajad Darabi, Mohammad Kachuee, Shayan Fazeli, Majid Sarrafzadeh
<span title="2020-05-03">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes.  ...  Code avaialble at https://github.com/sajaddarabi/TAPER-EHR  ...  Similarly, in [19] , the authors evaluate different models for embedding clinical notes such as CNNs, LSTMs and evaluate them on chronic disease prediction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.03971v4">arXiv:1908.03971v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/utlxuuh4svhvdfndpb5y34gelu">fatcat:utlxuuh4svhvdfndpb5y34gelu</a> </span>
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Multimodal Learning for Cardiovascular Risk Prediction using EHR Data [article]

Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis, Pim A. de Jong, Folkert W. Asselbergs, Daniel L. Oberski
<span title="2020-08-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To exploit the potential information captured in EHRs, in this study we propose a multimodal recurrent neural network model for cardiovascular risk prediction that integrates both medical texts and structured  ...  Various machine learning approaches have been developed to employ information in EHRs for risk prediction.  ...  In their study, CNN-and RNN-based deep learning models have been developed for chronic disease prediction using medical notes, and then a fully connected neural network has been applied with one hidden  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.11979v1">arXiv:2008.11979v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4qgn4jtuxncihboeuca3wtxj7q">fatcat:4qgn4jtuxncihboeuca3wtxj7q</a> </span>
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SAVEHR: Self Attention Vector Representations for EHR based Personalized Chronic Disease Onset Prediction and Interpretability [article]

Sunil Mallya, Marc Overhage, Sravan Bodapati, Navneet Srivastava, Sahika Genc
<span title="2019-11-13">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Chronic disease progression is emerging as an important area of investment for healthcare providers.  ...  We present SAVEHR, a self-attention based architecture on heterogeneous structured EHR data that achieves > 0.51 AUC-PR and > 0.87 AUC-ROC gains on predicting the onset of four clinical conditions (CHF  ...  To predict outcomes on ICU events, attention is used by [14] and attention is used on clinical notes to detect adverse medical events by [15] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.05370v1">arXiv:1911.05370v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/it3mr4r4rnezfc4cesyn5yldoq">fatcat:it3mr4r4rnezfc4cesyn5yldoq</a> </span>
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Modelling EHR timeseries by restricting feature interaction [article]

Kun Zhang, Yuan Xue, Gerardo Flores, Alvin Rajkomar, Claire Cui, Andrew M. Dai
<span title="2019-11-14">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We analyze its performance on mortality, ICD-9 and AKI prediction from observational values on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset.  ...  The patterns of these values may be significant indicators of patients' clinical states and there might be patterns that are unknown to clinicians but are highly predictive of some outcomes.  ...  or notes data in the EHR are better predictors for this task.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.06410v1">arXiv:1911.06410v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3x7lla4gj5b5joo2b74kzdnn6q">fatcat:3x7lla4gj5b5joo2b74kzdnn6q</a> </span>
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EHR based Genetic Testing Knowledge Base (iGTKB) Development

Qian Zhu, Hongfang Liu, Christopher G Chute, Matthew Ferber
<span title="2015-11-25">2015</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bnylrk2y7bfnrn7u2f2vjkx7ta" style="color: black;">BMC Medical Informatics and Decision Making</a> </i> &nbsp;
Methods: We extracted genetic testing information and patient medical records from EHR systems at Mayo Clinic.  ...  There are 60 clinical features with at least one mention in clinical notes of patients taking the test.  ...  Introduction Individualized medicine, as a rapidly advancing field of healthcare, intends to enable accurate predictions about a person's susceptibility of developing disease, the course of disease, and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1472-6947-15-s4-s3">doi:10.1186/1472-6947-15-s4-s3</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26606281">pmid:26606281</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4660117/">pmcid:PMC4660117</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ebkb7swm5zftrawx757z43hovq">fatcat:ebkb7swm5zftrawx757z43hovq</a> </span>
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EHR-based phenotyping: Bulk learning and evaluation

Po-Hsiang Chiu, George Hripcsak
<span title="">2017</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p4kk6lusgrhyxecgig72iasi5q" style="color: black;">Journal of Biomedical Informatics</a> </i> &nbsp;
In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts.  ...  In this batch-phenotyping framework, disease cohort definitions can be learned from within the abstract feature space established by using multiple diseases as a substrate and diagnostic codes as surrogates  ...  [49] , etc., and iv) deep learning [6] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jbi.2017.04.009">doi:10.1016/j.jbi.2017.04.009</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28410982">pmid:28410982</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5934756/">pmcid:PMC5934756</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iujgakd5ejc2fmiyazw4jjngpq">fatcat:iujgakd5ejc2fmiyazw4jjngpq</a> </span>
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Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction [article]

Bhagya Hettige, Weiqing Wang, Yuan-Fang Li, Suong Le, Wray Buntine
<span title="2021-02-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide granular readmission predictions  ...  However, in the real-world, a patient may have multiple co-existing chronic medical conditions, i.e., multimorbidity, which results in a cascade of visits where a non-immediate historical visit can be  ...  Experimental Settings 1) Datasets: Two real-world proprietary cohort-specific EHR datasets: heart failure (HF) and chronic liver disease (CL), and one public ICU EHR dataset: MIMIC 1 are used.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.02586v1">arXiv:2102.02586v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/miwtjppqijfcdldapmhgpi5n2u">fatcat:miwtjppqijfcdldapmhgpi5n2u</a> </span>
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Phenotyping using Structured Collective Matrix Factorization of Multi--source EHR Data [article]

Suriya Gunasekar, Joyce C. Ho, Joydeep Ghosh, Stephanie Kreml, Abel N Kho, Joshua C Denny, Bradley A Malin, Jimeng Sun
<span title="2016-09-14">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The increased availability of electronic health records (EHRs) have spearheaded the initiative for precision medicine using data driven approaches.  ...  Essential to this effort is the ability to identify patients with certain medical conditions of interest from simple queries on EHRs, or EHR-based phenotypes.  ...  EHR data is often subject to noise and missing data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1609.04466v1">arXiv:1609.04466v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rbjfhryg3jdk7lljpnvdrzbi3m">fatcat:rbjfhryg3jdk7lljpnvdrzbi3m</a> </span>
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Joint learning of representations of medical concepts and words from EHR data

Tian Bai, Ashis Kumar Chanda, Brian L. Egleston, Slobodan Vucetic
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p543puajlnhttk4gqgacas2q7y" style="color: black;">2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)</a> </i> &nbsp;
We also test how well our representations can be used to predict disease patterns of the next visit. The results show that our approach outperforms several common methods.  ...  In particular, we focus on capturing the relationship between medical codes and words by using a novel learning scheme for word2vec model.  ...  First, we selected 6 diverse ICD-9 codes in the EHR data that cover both acute and chronic diseases as well as common and less common diseases.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bibm.2017.8217752">doi:10.1109/bibm.2017.8217752</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29375929">pmid:29375929</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5783648/">pmcid:PMC5783648</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bibm/BaiCEV17.html">dblp:conf/bibm/BaiCEV17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mjjw3b5dwzgtvf7stnlplgcqzq">fatcat:mjjw3b5dwzgtvf7stnlplgcqzq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200211074854/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5783648&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/81/8b/818b3d756e9449680a57e22327eb545b438b7fc9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bibm.2017.8217752"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5783648" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Time Series Prediction using Deep Learning Methods in Healthcare [article]

Mohammad Amin Morid, Olivia R. Liu Sheng, Joseph Dunbar
<span title="2021-12-20">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper we systematically reviewed studies focused on advancing and using deep neural networks to leverage patients structured time series data for healthcare prediction tasks.  ...  Recent deep learning methods have shown promising performance for various healthcare prediction tasks by addressing the high-dimensional and temporal challenges of medical data.  ...  (e.g., disease name, drug name) within EHR data for the next visit diagnosis prediction task.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.13461v2">arXiv:2108.13461v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3e7t5r5qivaszhigvuls6wknd4">fatcat:3e7t5r5qivaszhigvuls6wknd4</a> </span>
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