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A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records

Francesco Bagattini, Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou
2019 BMC Medical Informatics and Decision Making  
Adverse drug events (ADEs) as well as other preventable adverse events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in the United States alone.  ...  While existing methods have been focusing on the exploitation of static features, limited attention has been given to temporal features.  ...  A software implementation of the framework is available at fbagattini/sparse_symbolic_representation. The system requires Python and Numpy to function.  ... 
doi:10.1186/s12911-018-0717-4 pmid:30630486 pmcid:PMC6327495 fatcat:hu3g2o7y4zaflag4avi7ya4a7i

Deep learning in pharmacogenomics: from gene regulation to patient stratification

Alexandr A Kalinin, Gerald A Higgins, Narathip Reamaroon, Sayedmohammadreza Soroushmehr, Ari Allyn-Feuer, Ivo D Dinov, Kayvan Najarian, Brian D Athey
2018 Pharmacogenomics (London)  
function as applied to pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of drugs, targets, and their interactions.  ...  We anticipate that in the future deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular  ...  [17] , it is likely that variations in complex traits such as drug response and susceptibility to adverse drug events are also controlled by the noncoding genome [53] .  ... 
doi:10.2217/pgs-2018-0008 pmid:29697304 fatcat:tkhmrqkevjfqxdty6ttbw33jam

Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources

Heba Ibrahim, A. Abdo, Ahmed M. El Kerdawy, A. Sharaf Eldin
2021 Artificial Intelligence in the Life Sciences  
This review is substantiated based on the fact that DDIs are contributing to a public health problem represented in 6-30% adverse drug event occurrences.  ...  Signal can be a new safety information or new aspect to already known adverse drug reaction which is possibly causally related to a medication/medications that warrants further investigation to accept  ...  Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this review paper  ... 
doi:10.1016/j.ailsci.2021.100005 fatcat:m324w243gfflxpiyq3g2qt7znq

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
, Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention; JBHI Aug. 2020 2230-2237 Hoogi, A., Mishra, A., Gimenez, F., Dong, J., and Rubin  ...  ., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan  ...  ., +, JBHI Nov. 2020 3154-3161 Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm

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

Gloria Hyunjung Kwak, Pan Hui
2020 arXiv   pre-print
The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare.  ...  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  ...  In [234] , nine entity types such as medications, indications, and adverse drug events (ADEs) and seven types of relations between these entities are extracted from electronic health record (EHR) notes  ... 
arXiv:1909.00384v2 fatcat:sy7pm2c2uvdd3pal2russn4xri

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies [article]

Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu
2021 arXiv   pre-print
Objective: Temporal electronic health records (EHRs) can be a wealth of information for secondary uses, such as clinical events prediction or chronic disease management.  ...  and Web of Science) complemented with hand-searching in several prestigious computer science conference proceedings.  ...  kidney disease prediction [3] and adverse drug event detection [4] .  ... 
arXiv:2107.09951v1 fatcat:ifn7s5amdjgs5b2fbp7w4icafi

Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams

Ryan Eshleman, Rahul Singh
2016 BMC Bioinformatics  
Results: We examine both graph-theoretic and semantic features for the classification task.  ...  Drugs, along with co-occurring symptoms are connected with edges weighted by temporal distance and frequency.  ...  Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Published: 6 October 2016  ... 
doi:10.1186/s12859-016-1220-5 pmid:27766937 pmcid:PMC5073861 fatcat:dg4ym374kjeznkuo63ycftagty

An overview of event extraction and its applications [article]

Jiangwei Liu, Liangyu Min, Xiaohong Huang
2021 arXiv   pre-print
As a particular form of Information Extraction (IE), Event Extraction (EE) has gained increasing popularity due to its ability to automatically extract events from human language.  ...  This study provides a comprehensive overview of the state-of-the-art event extraction methods and their applications from text, including closed-domain and open-domain event extraction.  ...  [12] propose a joint method for medication and adverse drug event extraction. Attention & Transformer based.  ... 
arXiv:2111.03212v1 fatcat:o3oagnjrybh3vapvvp7twgjtuu

2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25

2021 IEEE journal of biomedical and health informatics  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  ., +, JBHI Feb. 2021 504-513 DMC-Fusion: Deep Multi-Cascade Fusion With Classifier-Based Feature Synthesis for Medical Multi-Modal Images.  ... 
doi:10.1109/jbhi.2022.3140980 fatcat:ufig7b54gfftnj3mocspoqbzq4

Deep learning in systems medicine

Haiying Wang, Estelle Pujos-Guillot, Blandine Comte, Joao Luis de Miranda, Vojtech Spiwok, Ivan Chorbev, Filippo Castiglione, Paolo Tieri, Steven Watterson, Roisin McAllister, Tiago de Melo Malaquias, Massimiliano Zanin (+2 others)
2020 Briefings in Bioinformatics  
Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour.  ...  It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine.  ...  To illustrate, data were recorded with inertial measurement units [154] , and it has been shown that the precision in detecting events of bradykinesia, i.e. of the slowness of movement, with DL algorithms  ... 
doi:10.1093/bib/bbaa237 pmid:33197934 pmcid:PMC8382976 fatcat:bjhlu5jaubci3lm4j3vxiofehu

Decision support methods for the detection of adverse events in post-marketing data

M. Hauben, A. Bate
2009 Drug Discovery Today  
considerations involved in the implementation of computer algorithms within a comprehensive and holistic drug safety signal detection program.  ...  Computer algorithms that calculate statistical measures of reporting frequency for huge numbers of drug-event combinations are increasingly used to support pharamcovigilance analysts screening large spontaneous  ...  In addition, there are applications for screening of hospital data [9] and also other adverse event monitoring systems for signal detection in primary care He is board certified in preventive medicine  ... 
doi:10.1016/j.drudis.2008.12.012 pmid:19187799 fatcat:lhpsvzebyvfhjpdtu3b3vrolve


Vinit Mehta, Noopur Shrivastava
2021 International Journal of Technical Research & Science  
Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reform the future of artificial intelligence  ...  The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.  ...  Furthermore, mining food and drug records to identify adverse events could provide vital large scale alert mechanisms.  ... 
doi:10.30780/specialissue-icrdet-2021/002 fatcat:gsv76fv4qbe5fh5g3va5da2izu

Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection

Taxiarchis Botsis, Michael D Nguyen, Emily Jane Woo, Marianthi Markatou, Robert Ball
2011 JAMIA Journal of the American Medical Informatics Association  
Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.  ...  Objective The US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination.  ...  Often, MOs try to match them with the reported symptoms in each case report (or the medical record at step 2).  ... 
doi:10.1136/amiajnl-2010-000022 pmid:21709163 pmcid:PMC3168300 fatcat:dvomwl5ahza73eok2ogsmou4u4

Modern Views of Machine Learning for Precision Psychiatry [article]

Zhe Sage Chen, Prathamesh Kulkarni, Isaac R. Galatzer-Levy, Benedetta Bigio, Carla Nasca, Yu Zhang
2022 arXiv   pre-print
In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice.  ...  Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health.  ...  Acknowledgments The research was partially supported from the US National Science Foundation (CBET-1835000 to Z.S.C.), the National Institutes of Health (R01-NS121776 and R01-MH118928 to Z.S.C.).  ... 
arXiv:2204.01607v2 fatcat:coo557v2jzh6debycy3mhccfze

Providing data science support for systems pharmacology and its implications to drug discovery

Thomas Hart, Lei Xie
2016 Expert Opinion on Drug Discovery  
The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target  ...  Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery.  ...  Acknowledgements We sincerely thank the editor and the reviewers for their constructive suggestions This work was supported by the National Library of Medicine of the National Institute of Health under  ... 
doi:10.1517/17460441.2016.1135126 pmid:26689499 pmcid:PMC4988863 fatcat:ol5ra4b2efewtiietrqhcocqle
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