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Machine learning-based prediction of impulse control disorders in Parkinson's disease from clinical and genetic data

Johann Faouzi, Samir Bekadar, Fanny Artaud, Alexis Elbaz, Graziella Mangone, Olivier Colliot, Jean-Christophe Corvol
2022 IEEE Open Journal of Engineering in Medicine and Biology  
We trained three logistic regressions and a recurrent neural network to predict ICDs at the next visit using clinical risk factors and genetic variants previously associated with ICDs.  ...  We showed that ICDs in PD can be predicted with better accuracy with a recurrent neural network model than a trivial model.  ...  the steering committee), Marie Vidailhet, MD (Pitié-Salpêtrière Hospital, Paris, member of the steering committee), Alexis Brice, MD (Pitié-Salpêtrière Hospital, Paris, member of the steering committee and  ... 
doi:10.1109/ojemb.2022.3178295 pmid:35813487 pmcid:PMC9252337 fatcat:na2mdmeysvgujpyktfov7rj2ha

Predicting Rate of Cognitive Decline at Baseline Using a Deep Neural Network with Multidata Analysis [article]

Sema Candemir, Xuan V. Nguyen, Luciano M. Prevedello, Matthew T. Bigelow, Richard D.White, Barbaros S. Erdal
2020 arXiv   pre-print
Approach: We built a predictive model based on a supervised hybrid neural network utilizing a 3-Dimensional Convolutional Neural Network to perform volume analysis of Magnetic Resonance Imaging and integration  ...  Conclusion: To our knowledge, this is the first study that predicts slowly deteriorating/stable or rapidly deteriorating classes by processing routinely collected baseline clinical and demographic data  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD  ... 
arXiv:2002.10034v3 fatcat:76s6lhg3vvh4xe52pedcuyfu7i

Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks [article]

Cristóbal Esteban, Oliver Staeck, Yinchong Yang, Volker Tresp
2016 arXiv   pre-print
We also used the same models for a second task, i.e., next event prediction, and found that here the model based on a Feedforward Neural Network outperformed the other models.  ...  In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events.  ...  RECURRENT NEURAL NETWORKS FOR CLINICAL EVENT PREDICTION RNNs are a type of Neural Network where the hidden state of one time step is computed by combining the current input with the hidden state of the  ... 
arXiv:1602.02685v2 fatcat:mlcg3fs73bda3mbnv5z5fkrma4

Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning [article]

Thai-Hoang Pham, Changchang Yin, Laxmi Mehta, Xueru Zhang, Ping Zhang
2021 arXiv   pre-print
In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of  ...  In addition, it leverages additional information from both related labeled and unlabeled datasets to generate more generalized representations by using a new multi-task learning scheme for making more  ...  A variant of recurrent neural network (RNN) that uses gating mechanism. • Bidirectional GRU (Bi-GRU) [20] .  ... 
arXiv:2109.12276v1 fatcat:2bvamr3suvcgdmti7qaxuakpn4

Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models

Fulvia Ceccarelli, Marco Sciandrone, Carlo Perricone, Giulio Galvan, Francesco Morelli, Luis Nunes Vicente, Ilaria Leccese, Laura Massaro, Enrica Cipriano, Francesca Romana Spinelli, Cristiano Alessandri, Guido Valesini (+2 others)
2017 PLoS ONE  
We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks.  ...  We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage.  ...  In particular, for the aim of the present study, we employ Recurrent Neural Networks (RNNs) as model suited to deal with sequential information.  ... 
doi:10.1371/journal.pone.0174200 pmid:28329014 pmcid:PMC5362169 fatcat:yseuvu7jjfes5aldkk5zolthsq

Predicting Brain Degeneration with a Multimodal Siamese Neural Network [article]

Cecilia Ostertag, Marie Beurton-Aimar, Muriel Visani, Thierry Urruty, Karell Bertet
2020 arXiv   pre-print
In this work we present a neural network architecture for multimodal learning, able to use imaging and clinical data from two time points to predict the evolution of a neurodegenerative disease, and robust  ...  Our multimodal network achieves 92.5\% accuracy and an AUC score of 0.978 over a test set of 57 subjects.  ...  Random forests (RF) have also been used in [8] to handle imaging data, free text, and demographic information, after feature extraction with 2D Convolutional Neural Networks (CNN) and a medical ontology  ... 
arXiv:2011.00840v1 fatcat:i7jkhs52tffvtnd2swoyebfnoy

Dipole

Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao
2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17  
Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships  ...  Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results.  ...  ACKNOWLEDGMENTS e authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. is work is supported in part by the US National Science Foundation under grants  ... 
doi:10.1145/3097983.3098088 dblp:conf/kdd/MaCZYSG17 fatcat:6sth7fjv7bcwlhsfzl4zu6oz6u

Diagnosis Prediction via Recurrent Neural Networks

Yangzi Mu, Mengxing Huang, Chunyang Ye, Qingzhou Wu
2018 International Journal of Machine Learning and Computing  
Using historical data from the EHR, we can predict medical conditions and medication uses. Existing works model EHR data by using recurrent neural networks (RNNs).  ...  We propose an application of using bidirectional RNNs to remember all the information of both the past and future visits and add some patient's characteristics as side information into this model.  ...  In order to model the sequential EHR data, recurrent neural networks (RNNs) are used in the literature to obtain accurate and robust representations of patient visits in diagnostic predictive tasks [3  ... 
doi:10.18178/ijmlc.2018.8.2.673 fatcat:cxfbdro2vjgrvneqqnswzfjboi

NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification [article]

David Wood, James Cole, Thomas Booth
2019 arXiv   pre-print
When applied to Alzheimer's disease prediction, NEURODRAM achieves state-of-the-art classification accuracy on an out-of-sample dataset, significantly outperforming a baseline convolutional neural network  ...  We address this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging classification.  ...  Acknowledgments We would like to thank Pritesh Mehta for many helpful comments and discussions, and Jeremy Lynch for providing the computer used for training the model.  ... 
arXiv:1910.04721v3 fatcat:dw34mocxcvabflmuaim4gt2wiu

Real-Time Medical Electronic Data Mining Based Hierarchical Attention Mechanism

Yi Mao, Yun Li, Yixin Chen
2020 ICIC Express Letters  
Deep neural networks are supported by a Recurrent Neural Network (RNN) architecture with Long Short-Term Memory (LSTM) units, and have achieved state-of-the-art results in a number of clinical prediction  ...  demographics, hospitalization history, vital sign and laboratory tests.  ...  The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.  ... 
doi:10.24507/icicel.14.12.1155 fatcat:hqbycbrvknbltmihden2hgl6v4

Deep Learning Architectures for Vector Representations of Patients and Exploring Predictors of 30-Day Hospital Readmissions in Patients with Multiple Chronic Conditions [chapter]

Muhammad Rafiq, George Keel, Pamela Mazzocato, Jonas Spaak, Carl Savage, Christian Guttmann
2019 Lecture Notes in Computer Science  
We implemented Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on sequential Electronic Health Records data at the Danderyd Hospital in Stockholm, Sweden.  ...  The approach also has potential implications for supporting care delivery, care design and clinical decisionmaking.  ...  Ethical considerations The study has been approved by the Regional Ethics Committee (Diary Numbers: 2014/384-31/1 and 2017/999-31/2).  ... 
doi:10.1007/978-3-030-12738-1_17 fatcat:w22f5r44mrenbg2rllwvv4wyae

An Attention-based Recurrent Neural Networks Framework for Health Data Analysis

Qiuling Suo, Fenglong Ma, Giovanni Canino, Jing Gao, Aidong Zhang, Agostino Gnasso, Giuseppe Tradigo, Pierangelo Veltri
2018 Sistemi Evoluti per Basi di Dati  
Patients' historical records are fed into a Recurrent Neural Network (RNN) which memorizes all the past visit information, and then a task-specific layer is trained to predict multiple diagnoses.  ...  Experimental results show that prediction accuracy is reliable if compared to widely used approaches 1 1 An extended version of such a paper has been included in the  ...  Acknowledgement This work was supported in part by NSF IIS-1218393 and IIS-1514204, and by SISTABENE POR project as PIHGIS POR project.  ... 
dblp:conf/sebd/SuoMCGZGTV18 fatcat:ekmas57zwnaq7mjcrx2hiu62ry

Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare

Xianlong Zeng, Simon Lin, Chang Liu
2021 IEEE Open Journal of the Computer Society  
Our multi-view approach can effectively model the heterogeneous information, including patient demographic features, medical codes, drug usages, and facility utilization.  ...  Existing approaches mainly rely on manually designed features and linear regression-based models, which require massive medical domain knowledge and show limited predictive performance.  ...  Our approach leverages a feedforward neural network, an attention-based bidirectional recurrent neural network, and a hierarchical attention network to exploit heterogeneous information in claims data  ... 
doi:10.1109/ojcs.2021.3052518 fatcat:v4znvrhiw5colmfjqzq4dym3sq

Predicting Alzheimer's disease progression using multi-modal deep learning approach

Garam Lee, Kwangsik Nho, Byungkon Kang, Kyung-Ah Sohn, Dokyoon Kim
2019 Scientific Reports  
To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network.  ...  Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction  ...  support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (AIG), were used in this study.  ... 
doi:10.1038/s41598-018-37769-z pmid:30760848 pmcid:PMC6374429 fatcat:o4nlg7st3vh25dadt7yp3opguu

Deep Learning for Electronic Health Record Analytics

Gaspard Harerimana, Jong Wook Kim, Beakchol Jang
2019 IEEE Access  
INDEX TERMS Electronic health records, convolutional neural networks, recurrent neural networks, adverse drug events, EHR raw features.  ...  These data recorded in hospital's Electronic Health Records (EHR) consists of patient information, clinical notes, charted events, medications, procedures, laboratory test results, diagnosis codes, and  ...  Esteban et al. used Recurrent Neural Networks (RNNs) and static information like patient gender, blood type, etc. and dynamic information like clinical charted events to predict future adverse events  ... 
doi:10.1109/access.2019.2928363 fatcat:kqt7hapxkjf7tnevks5ncs6c74
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