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A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes
2018
AMIA Annual Symposium Proceedings
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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]
2020
arXiv
pre-print
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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]
2014
arXiv
pre-print
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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
2020
IEEE Access
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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
2021
Applied Sciences
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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
2020
International Journal of Advanced Computer Science and Applications
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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
2014
Applied Soft Computing
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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
2022
Sensors
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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
2020
Scientific Reports
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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
2017
2017 International Conference on Computational Science and Computational Intelligence (CSCI)
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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
2019
Frontiers in Medicine
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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)
2020
JMIR Medical Informatics
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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'
2002
Physics Uspekhi
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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
2016
Proceedings of the IEEE
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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
2016
Online Journal of Public Health Informatics
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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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