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Semi-Supervised Information Retrieval System for Clinical Decision Support

Harsha Gurulingappa, Luca Toldo, Claudia Schepers, Alexander Bauer, Gerard Megaro
2016 Text Retrieval Conference  
This article summarizes the approach developed for TREC 2016 Clinical Decision Support Track.  ...  In order to address the daunting challenge of retrieval of biomedical articles for answering clinical questions, an information retrieval methodology was developed that combines pseudo -relevance feedback  ...  Introduction The goal of the TREC 2016 Clinical Decision Support Track 2 is to retrieve biomedical articles relevant for answering clinical questions based on patient records.  ... 
dblp:conf/trec/GurulingappaTSB16 fatcat:ztczxt2atvcc5laxl7vczjxpcu

What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature

Byron C. Wallace
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
In this brief paper I highlight work on developing tasks, corpora, and models to support semi-automated evidence retrieval and extraction.  ...  Ideally decisions regarding medical treatments would be informed by the totality of the available evidence.  ...  However, I acknowledge in particular Iain Marshall, Thomas Trikalinos, Matt Lease, and Ani Nenkova for their key contributions to these efforts.  ... 
doi:10.24963/ijcai.2019/899 pmid:34025086 pmcid:PMC8136417 dblp:conf/ijcai/Wallace19 fatcat:ttu7ffypqvaljp2njjpp23kkzy

NLP Algorithms Endowed for Automatic Extraction of Information from Unstructured Free-Text Reports of Radiology Monarchy

Natural Language Processing (NLP) Algorithms are the key factors for automatic information extraction form the unstructured free-text radiology reports .To extract clinically important findings and recommendations  ...  Thus through this survey we can say that, NLP methods used to extract information ,brings new insights into already known clinical evidences.  ...  This system has prospective such as clinical decision support [38] , used for patients appreciative records , for public health care like. Bio-surveillance, and in biomedical research .  ... 
doi:10.35940/ijitee.l8009.1091220 fatcat:sjth33dnvjfnhn442figt75llq

Louhi 2014: Special issue on health text mining and information analysis

Sumithra Velupillai, Martin Duneld, Aron Henriksson, Maria Kvist, Maria Skeppstedt, Hercules Dalianis
2015 BMC Medical Informatics and Decision Making  
Acknowledgements For partial funding we would like to thank the SSF (Swedish  ...  EHRs are used throughout the health care sector primarily for clinical purposes, but also for secondary purposes such as decision support and research.  ...  Kreutzhaler & Schulz describe work on detecting sentence boundaries and abbreviations in German clinical text by developing supervised classifiers (support vector machines) for each task.  ... 
doi:10.1186/1472-6947-15-s2-s1 pmid:26099575 pmcid:PMC4474544 fatcat:scnpu3fwjredjdzy4ftf6yxjvq

Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval

Xiaofan Zhang, Wei Liu, Murat Dundar, Sunil Badve, Shaoting Zhang
2015 IEEE Transactions on Medical Imaging  
Both computer-aided diagnosis (CAD) and content-based image-retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area.  ...  Critically, the supervised information is employed to bridge the semantic gap between low-level image features and high-level diagnostic information.  ...  Retrieving them may not be beneficial for decision support.  ... 
doi:10.1109/tmi.2014.2361481 pmid:25314696 fatcat:frt22wzviraffm7zjex5wgzlau

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
2019 Computers in Biology and Medicine  
The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems.  ...  , image registration, and content-based image retrieval systems.  ...  Acknowledgment Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award  ... 
doi:10.1016/j.compbiomed.2019.02.017 pmid:31054502 pmcid:PMC6531364 fatcat:tcyorm6g3ff6dg7ty2ubtqorjq

A Semi-supervised learning approach to enhance health care Community-based Question Answering: A case study in alcoholism [article]

Papis Wongchaisuwat, Diego Klabjan, Siddhartha R. Jonnalagadda
2016 arXiv   pre-print
Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA.  ...  Automatically answering the posted questions can provide a useful source of information for online health communities.  ...  Jina Huh from Michigan State University for providing the Yahoo! Answers dataset and Dr. Kalpana Raja from Northwestern University for helping with UMLS.  ... 
arXiv:1607.00706v1 fatcat:gj35slc6gzdojevbgqgogmsbu4

Automating Risk of Bias Assessment for Clinical Trials

Iain J Marshall, Joel Kuiper, Byron C Wallace
2015 IEEE journal of biomedical and health informatics  
We demonstrate that systematic reviews may be used to distantly supervise text mining models, obviating the need for manually annotated clinical trial reports.  ...  burdensome for clinical researchers.  ...  Justification for risk of bias decisions The risk of bias classification (high, low, or unknown) is structured and retrievable per clinical trial for individual domains.  ... 
doi:10.1109/jbhi.2015.2431314 pmid:25966488 fatcat:7ke6fe47hzgmzdox6jd3hfgnmy

Automating risk of bias assessment for clinical trials

Iain J. Marshall, Joël Kuiper, Byron C. Wallace
2014 Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB '14  
We demonstrate that systematic reviews may be used to distantly supervise text mining models, obviating the need for manually annotated clinical trial reports.  ...  burdensome for clinical researchers.  ...  Justification for risk of bias decisions The risk of bias classification (high, low, or unknown) is structured and retrievable per clinical trial for individual domains.  ... 
doi:10.1145/2649387.2649406 dblp:conf/bcb/MarshallKW14 fatcat:mydf3uuxibay5oniumetoeawuy

Using Electronic Health Record Systems in Medical Education – A Needs Assessment

Muhammad Jawad HASHIM, Moien Abdul Basith KHAN
2018 Applied Medical Informatics  
The aim of this study was to determine the features needed in electronic health record systems by supervising physicians for teaching medical students and residents.  ...  The study findings provide a reference for developing teaching resources within electronic health records systems.  ...  Acknowledgments We would like to thank the participants in the study for sharing their valuable insights.  ... 
doaj:b42125d4a26e4f6eba127ef57dc998fc fatcat:mdvjomaf3bbzjphflvqww3un5i

Behind the screens: Clinical decision support methodologies – A review

Paolo Fraccaro, Dympna O׳Sullivan, Panagiotis Plastiras, Hugh O׳Sullivan, Chiara Dentone, Antonio Di Biagio, Peter Weller
2015 Health Policy and Technology  
Permanent repository link: Link to published version: http://dx.Abstract Clinical decision support systems (CDSSs) are interactive software systems designed to assist  ...  The purpose of this review is to introduce clinicians and policy makers to the most commonly computer-based methodologies employed to construct decision models to compute clinical decisions in a non-technical  ...  Systems to retrieve and analyse textual information are needed to effectively exploit the increasing amounts of digital medical textual data which may be used to support clinicians during the decision  ... 
doi:10.1016/j.hlpt.2014.10.001 fatcat:cju35i3kpvffnd67xtqmrngtum

Competency in Nursing Students: A Systematic Review

Batool Nehrir, Zohreh Vanaki, Jamileh Mokhtari Nouri, Seyyed Mohammad Khademolhosseini, Abbas Ebadi
2016 International Journal of Travel Medicine and Global Health  
Results: From a total of 13,115 articles, 20 were retrieved in the final step.  ...  , according to the University of York Center for Reviewers and Dissemination Guidance approach, 2008.  ...  Amir Vahedian-Azimi for his thoughtful help in methodology and critical revision of the manuscript.  ... 
doi:10.20286/ijtmgh-04013 fatcat:zok5jhlk5nfwjm5zprft6t6gmm

A System for Mining Temporal Physiological Data Streams for Advanced Prognostic Decision Support

Jimeng Sun, Daby Sow, Jianying Hu, Shahram Ebadollahi
2010 2010 IEEE International Conference on Data Mining  
The proposed approach and system were tested using the MIMIC II database, which consists of physiological waveforms, and accompanying clinical data obtained for ICU patients.  ...  In the experiments we report the efficiency and throughput of the stream processing unit for feature extraction, the effectiveness of the supervised similarity measure both in the context of classification  ...  ONLINE DATA ANALYSIS The online data analysis components of the system provide support for retrieval of similar patients using the similarity metric learned during offline data analysis, and assessment  ... 
doi:10.1109/icdm.2010.102 dblp:conf/icdm/SunSHE10 fatcat:tp24gpg6hfg4dnmtik7nrr3ixq

IMIA LaMB WG event: 'Biomedical Semantics in the Big Data Era', Workshop at MEDINFO 2015 – São Paulo,Brazil

Roland Cornet, Stephane Meystre, Stefan Schulz, Patrick Ruch, Tomasz Adamusiak, Laszlo Balkanyi, Jianying Hu
2019 Zenodo  
knowledge and information, by Stefan Schulz - Deep question-answering for biomedical decision support, by Patrick Ruch - Feature extraction for predictive modeling, by Jianying Hu - Connecting structured  ...  This document set document has (1) all the presentation materials: - Introduction, by Ronald Cornet - From free text to ontology, by Stephane Meystre - Bridging natural and formal languages for representing  ...  applications like clinical decision support systems.  ... 
doi:10.5281/zenodo.3398964 fatcat:ksn3rajzs5earbjd3kczdvrwdi

A Novel Similarity Learning Method via Relative Comparison for Content-Based Medical Image Retrieval

Wei Huang, Peng Zhang, Min Wan
2013 Journal of digital imaging  
Extensive experimental results and comprehensive statistical analysis demonstrate the superiority of adopting the newly introduced learning paradigm, compared with several conventional supervised and semi-supervised  ...  Nowadays, the huge volume of medical images represents an enormous challenge towards health-care organizations, as it is often hard for clinicians and researchers to manage, access, and share the image  ...  Five popular supervised and semi-supervised similarity learning methods are implemented for retrieval performance comparison.  ... 
doi:10.1007/s10278-013-9591-x pmid:23563792 pmcid:PMC3782604 fatcat:mbr3y7t26ba77e5fjj7drtgaku
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