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A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes

Li Rumeng, Jagannatha Abhyuday N, Yu Hong
2018 AMIA Annual Symposium Proceedings  
We formulate this hybrid neural network model (HNN), composed of recurrent neural network and deep residual network, to jointly predict the presence and period assertion values associated with medical  ...  In this paper, we propose a novel neural network architecture for clinical text mining.  ...  We also thank the anonymous reviewers for their comments and suggestions. This work was supported in part by the grant HL125089 from the National Institutes of Health.  ... 
pmid:29854183 pmcid:PMC5977733 fatcat:zaelo5kn4zh7zbtcuuxj5wva7y

DeepHealth: Review and challenges of artificial intelligence in health informatics [article]

Gloria Hyunjung Kwak, Pan Hui
2020 arXiv   pre-print
This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records  ...  The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare.  ...  In addition, a combination of GRU and the residual network was used to develop a hybrid NN for joint prediction of present and period assertions of medical events in clinical notes [200] .  ... 
arXiv:1909.00384v2 fatcat:sy7pm2c2uvdd3pal2russn4xri

Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data [article]

Sunayan Bandyopadhyay, Julian Wolfson, David M. Vock, Gabriela Vazquez-Benitez, Gediminas Adomavicius, Mohamed Elidrisi, Paul E. Johnson,, Patrick J. O'Connor
2014 arXiv   pre-print
In this paper, we present a machine learning approach based on Bayesian networks trained on EHD to predict the probability of having a cardiovascular event within five years.  ...  Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care.  ...  Acknowledgements This work was partially supported by NHLBI grant R01HL102144-01 and AHRQ grant R21HS017622-01.  ... 
arXiv:1404.2189v1 fatcat:ib347ifkinhmdcqrz732ny5txa

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Jahanzaib Latif, Chuangbai Xiao, Shanshan Tu, Sadaqat Ur Rehman, Azhar Imran, Anas Bilal
2020 IEEE Access  
DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods.  ...  Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases.  ...  The dataset used in this research was comprised of 55,000 clinical notes over a period of seven years (average of 133 notes per patient).  ... 
doi:10.1109/access.2020.3016782 fatcat:j76bwlyrj5dv5mhhsvs4apynje

A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts

Priyankar Bose, Sriram Srinivasan, William C. Sleeman IV, Jatinder Palta, Rishabh Kapoor, Preetam Ghosh
2021 Applied Sciences  
In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current  ...  Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text.  ...  Neural Network Recurrent Neural Network Long Short Term Memory Graph Convolutional Network Concept Dependency Tree Graph Neural Network entities from clinical notes Machine ;earning-based such as drug  ... 
doi:10.3390/app11188319 fatcat:notb6zimcvfxhhuaik73t75sje

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Bilal Saeed Raja, Sohail Asghar
2020 International Journal of Advanced Computer Science and Applications  
The significance of information technology in medical sciences by utilizing the Clinical Decision Support System (CDSS) has opened the spillways of exponentially improved predictive models.  ...  clinical decision by justifying their assertions made, it will be a win-win situation.  ...  The free context of medical data, medical notes and reports have given a new dimension to the text mining paradigm.  ... 
doi:10.14569/ijacsa.2020.0110287 fatcat:otlc5hq56jdifnzefch2kh2ca4

Applications of neuro fuzzy systems: A brief review and future outline

Samarjit Kar, Sujit Das, Pijush Kanti Ghosh
2014 Applied Soft Computing  
AI methods are mainly comprised of fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro fuzzy systems, genetic fuzzy systems and genetic programming neural networks etc  ...  Neuro-fuzzy systems refer to combinations of artificial neural network and fuzzy logic in the field of artificial intelligence, which was proposed by Jang [1] in 1993.  ...  Acknowledgment The authors wish to express sincere gratitude to the anonymous reviewers for their constructive comments and helpful suggestions, which lead to substantial improvements of this paper.  ... 
doi:10.1016/j.asoc.2013.10.014 fatcat:iochb6rlgbhb5dmpmx7jeh54mu

Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions

Marco Leo, Giuseppe Massimo Bernava, Pierluigi Carcagnì, Cosimo Distante
2022 Sensors  
In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the  ...  As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview  ...  Although the authors evaluated the accuracy in the estimation of joint positions and movements encoding without a specific clinical application, they asserted that it was specifically designed to evaluate  ... 
doi:10.3390/s22030866 pmid:35161612 pmcid:PMC8839211 fatcat:6rv7kiyj35hbrdurqmyfcufnwm

Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions

Scott Rich, Axel Hutt, Frances K Skinner, Taufik A Valiante, Jérémie Lefebvre
2020 Scientific Reports  
Our joint computational and mathematical analyses revealed that such stimuli, be they noisy or periodic in nature, exert a stabilizing influence on network responses, disrupting the development of such  ...  of sudden changes in network dynamics.  ...  ) for support of this research.  ... 
doi:10.1038/s41598-020-72335-6 pmid:32958802 pmcid:PMC7506027 fatcat:5nxygt2xzvespc7g6bta3amvye

Performance-Aware Refactoring of Cloud-Based Big Data Applications

Chen Li, Giuliano Casale
2017 2017 International Conference on Computational Science and Computational Intelligence (CSCI)  
These include search engines, monitoring of academic performance, biology and wireless networks. We first discuss a number of clustering methods.  ...  objects into a smaller number of clusters is of importance in many applications.  ...  Multilayer Artificial Neural Network Design and Architecture Optimization for the Pattern Recognition and Prediction of EEG Signals based on Henon Map Chaotic System Lei Zhang Faculty of Engineering  ... 
doi:10.1109/csci.2017.264 fatcat:lq7gvjk2pzarllwdvrfw2g6xdm

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

Michela Assale, Linda Greta Dui, Andrea Cina, Andrea Seveso, Federico Cabitza
2019 Frontiers in Medicine  
Problem: Clinical practice requires the production of a time- and resource-consuming great amount of notes.  ...  In such a way, we bridged literature and real world needs, performing a step further toward the revival of notes fields.  ...  Pedro Berjano, and Dr. Michele Ulivi for their advice in regard to the medical aspects of our research.  ... 
doi:10.3389/fmed.2019.00066 pmid:31058150 pmcid:PMC6478793 fatcat:6koblecrsnbabld6y6qwukwxgy

Extracting Drug Names and Associated Attributes from Discharge Summaries: DrugEx, an End-to-End Text Mining System (Preprint)

Ghada Alfattni, Maksim Belousov, Niels Peek, Goran Nenadic
2020 JMIR Medical Informatics  
This study evaluates the feasibility of using NLP and deep learning approaches for extracting and linking drug names and associated attributes identified in clinical free-text notes and presents an extensive  ...  This study initiated with the participation in the 2018 National NLP Clinical Challenges (n2c2) shared task on adverse drug events and medication extraction.  ...  Acknowledgments This work was partially supported by the Saudi Arabian Ministry of Education, the Saudi Arabian Cultural Bureau in London, and the Healthcare Text Analytics Network (Heal-tex, grant EP/  ... 
doi:10.2196/24678 pmid:33949962 fatcat:ob3etwthpjg3rdvqawluo6yboy

Models of neural dynamics in brain information processing — the developments of 'the decade'

G N Borisyuk, R M Borisyuk, Yakov B Kazanovich, Genrikh R Ivanitskii
2002 Physics Uspekhi  
Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures.  ...  The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration.  ...  The model described in this paragraph allows for a number of interesting predictions that await experimental verification.  ... 
doi:10.1070/pu2002v045n10abeh001143 fatcat:kyk5zwrlurak3kfny3ivmjz6wy

Machine Learning and Decision Support in Critical Care

Alistair E. W. Johnson, Mohammad M. Ghassemi, Shamim Nemati, Katherine E. Niehaus, David Clifton, Gari D. Clifford
2016 Proceedings of the IEEE  
This paper discusses the issues of compartmentalization, corruption, and complexity involved in collection and preprocessing of critical care data.  ...  [139] developed an artificial neural network (ANN) model optimized using a genetic algorithm for the purposes of mortality prediction.  ...  in neural networks [173] .  ... 
doi:10.1109/jproc.2015.2501978 pmid:27765959 pmcid:PMC5066876 fatcat:7i6wi65qgjbapjjznk2nioz32y

Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston

Margaret Reid, Julia Gunn, Snehal Shah, Michael Donovan, Rosalind Eggo, Steven Babin, Ivanka Stajner, Eric Rogers, Katherine B. Ensor, Loren Raun, Jonathan I. Levy, Ian Painter (+6 others)
2016 Online Journal of Public Health Informatics  
The consultancy featured here focused on improving predictions of asthma exacerbation risk in demographic and geographic subdivisions of the city of Boston, Massachusetts, USA based on the combination  ...  A prior paper described the formation of consultancies for requirements analysis and dialogue regarding costs and benefits of sustainable analytic tools.  ...  Acknowledgements We thank the Boston Public Health Commission and particularly Executive Director Monica Valdes Lupi JD, MPH for hosting and leading the consultancy.  ... 
doi:10.5210/ojphi.v8i3.6902 pmid:28210420 pmcid:PMC5302473 fatcat:3lr6cldjifddllkz676sskloyi
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