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Automatic Identification of Recent High Impact Clinical Articles in PubMed to Support Clinical Decision Making Using Time-agnostic Features

Jiantao Bian, Samir Abdelrahman, Jianlin Shi, Guilherme Del Fiol
2018 Journal of Biomedical Informatics  
With the advantage of relying only on time-agnostic features, the proposed approach can be used as an adjunct to help clinicians identify recent high impact clinical studies to support clinical decision-making  ...  We investigated a machine learning approach to find recent studies with high clinical impact to support clinical decision making and literature surveillance.  ...  Mork in the National Library of Medicine (NLM) for processing our initial dataset using NLM Medical Text Indexer (MTI).  ... 
doi:10.1016/j.jbi.2018.11.010 pmid:30468912 pmcid:PMC6342626 fatcat:256u3rzvojax3m5klgmcp3dkg4

Integrating radiomics into holomics for personalised oncology: from algorithms to bedside

Roberto Gatta, Adrien Depeursinge, Osman Ratib, Olivier Michielin, Antoine Leimgruber
2020 European Radiology Experimental  
In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning.  ...  We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other "omics" into clinical workflows.  ...  OR and OS contributed to the Big Data challenges for precision medicine paragraph. All authors read and approved the final manuscript.  ... 
doi:10.1186/s41747-019-0143-0 pmid:32034573 pmcid:PMC7007467 fatcat:3d7oelc6fnfufeqsll3teq3n5y

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review [article]

Felipe Giuste, Wenqi Shi, Yuanda Zhu, Tarun Naren, Monica Isgut, Ying Sha, Li Tong, Mitali Gupte, May D. Wang
2022 arXiv   pre-print
We find that successful use of XAI can improve model performance, instill trust in the end-user, and provide the value needed to affect user decision-making.  ...  Evaluation of XAI results is also discussed as an important step to maximize the value of AI-based clinical decision support systems.  ...  Siva Bhavani from Emory University for his insights on leveraging artificial intelligence in clinical practice. We would like to thank Mr.  ... 
arXiv:2112.12705v4 fatcat:g44642qdnfchnmhgzsaqwjacua

Applications of machine learning in drug discovery and development

Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, Shanrong Zhao
2019 Nature reviews. Drug discovery  
Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials.  ...  Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data.  ...  Segler for contributing to the small-molecule optimization subsection and A. Janowczyk for providing the pathology images in Figure 4 .  ... 
doi:10.1038/s41573-019-0024-5 pmid:30976107 pmcid:PMC6552674 fatcat:4gubtr5kz5fe7khrbtfrre4lhy

Clinical information extraction for preterm birth risk prediction

Lucas Sterckx, Gilles Vandewiele, Isabelle Dehaene, Olivier Janssens, Femke Ongenae, Femke Debackere, Filip De Turck, Kristien Roelens, Johan Decruyenaere, Sofie Van Hoecke, Thomas Demeester
2020 Journal of Biomedical Informatics  
In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in  ...  This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making.  ...  outcome) clinical trial (EC/2018/0609) of Ghent University Hospital.  ... 
doi:10.1016/j.jbi.2020.103544 pmid:32858168 fatcat:cvdq6eu47zc45k7eez6ygyczc4

Automated Verification of Phenotypes using PubMed

Ryan Bridges, Jette Henderson, Joyce C. Ho, Byron C. Wallace, Joydeep Ghosh
2016 Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB '16  
In this paper, we propose a supervision-free method of verification that uses co-occurrence in PubMed articles to determine clinical significance.  ...  In the realm of data driven clinical research, medical concepts, or phenotypes, are used to serve as indicators for patient clusters of interest.  ...  Phenotypes also enable cohort identification to target patients for screening tests and interventions, support surveillance of infectious diseases, and aid in the conduct of pragmatic clinical trials and  ... 
doi:10.1145/2975167.2985844 dblp:conf/bcb/BridgesHHWG16 fatcat:err3ixhcszbhhfsz5oad4cnrhy

Challenges and opportunities for public health made possible by advances in natural language processing

Oliver Baclic, Matthew Tunis, Kelsey Young, Coraline Doan, Howard Swerdfeger
2020 Canada Communicable Disease Report  
The recent advances in NLP technologies are enabling rapid analysis of vast amounts of text, thereby creating opportunities for health research and evidence-informed decision making.  ...  The purpose of this paper is to provide some notable examples of both the potential applications and challenges of NLP use in public health.  ...  Acknowledgements We thank J Nash and J Robertson who were kind enough to offer feedback and suggestions. Funding This work is supported by the Public Health Agency of Canada.  ... 
doi:10.14745/ccdr.v46i06a02 pmid:32673380 pmcid:PMC7343054 fatcat:dk34gbgf3vgknltffwa6srj3w4

Machine Learning Techniques for Biomedical Natural Language Processing: A comprehensive Review

Essam H. Houssein, Rehab E. Mohamed, Abdelmgeid A. Ali
2021 IEEE Access  
In this way, biomedical NLP applications can be used to direct clinical decisions, identify medical problems, and effectively postpone or avoid the occurrence of a disease.  ...  Therefore, the performance of biomedical NLP techniques is required to unlock the full potential of EHR data to convert a clinical narrative text automatically into structured clinical data.  ...  [17] , supporting clinical decisions [18] , etc.  ... 
doi:10.1109/access.2021.3119621 fatcat:pl7h35nvqngk3gxpbdxvrgzg2u

Development of Clinical Concept Extraction Applications: A Methodology Review [article]

Sunyang Fu, David Chen, Huan He, Sijia Liu, Sungrim Moon, Kevin J Peterson, Feichen Shen, Liwei Wang, Yanshan Wang, Andrew Wen, Yiqing Zhao, Sunghwan Sohn, Hongfang Liu
2020 arXiv   pre-print
range of applications ranging from clinical decision support to care quality improvement.  ...  The methods used for developing clinical concept extraction applications were discussed in this review.  ...  Acknowledgements We gratefully acknowledge Katelyn Cordie and Luke Carlson for editorial support.  ... 
arXiv:1910.11377v3 fatcat:rt3flc4kvvhqzawa5nqp6pucy4

An Evidence-Based Tool for Safe Configuration of Electronic Health Records: The eSafety Checklist

Ruth McCorkle, Elizabeth Borycki, Pritma Dhillon-Chattha
2018 Applied Clinical Informatics  
Objective This article outlines the development of a detailed and comprehensive evidence-based checklist of safe configuration practices for use by clinical informatics professionals when configuring hospital-based  ...  However, in recent years, evidence suggests that specific features and functions of EHRs can introduce new, unanticipated patient safety concerns that can be mitigated by safe configuration practices.  ...  We further acknowledge the significant time commitment of our four expert panelists in reviewing the checklist and providing their thoughtful and candid feedback.  ... 
doi:10.1055/s-0038-1675210 fatcat:mesaskyjovavvfzpwjorjonexm

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Anke Meyer-Bäse, Lia Morra, Uwe Meyer-Bäse, Katja Pinker
2020 Contrast Media & Molecular Imaging  
The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes.  ...  Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging.  ...  Acknowledgments e authors would like to thank Angelo Laudani for assistance in the retrieval of scientific literature.  ... 
doi:10.1155/2020/6805710 pmid:32934610 pmcid:PMC7474774 fatcat:f2mmqwpyybeg7p7qzq33ol763u

Biomedical Informatics on the Cloud

Peipei Ping, Henning Hermjakob, Jennifer S. Polson, Panagiotis V. Benos, Wei Wang
2018 Circulation Research  
In 2016, a consortium of researchers, publishers, and research funders published the FAIR guiding principles to make data Findable, Accessible, Interoperable, and Re-usable 2 .  ...  practical scenarios of machine learning-supported molecular signature extraction.  ...  We would also like to thank Dr.  ... 
doi:10.1161/circresaha.117.310967 pmid:29700073 pmcid:PMC6192708 fatcat:kekqc3gegvcc3hm56xzw3cosmu

Description and pilot results from a novel method for evaluating return of incidental findings from next-generation sequencing technologies

Katrina A.B. Goddard, Evelyn P. Whitlock, Jonathan S. Berg, Marc S. Williams, Elizabeth M. Webber, Jennifer A. Webster, Jennifer S. Lin, Kasmintan A. Schrader, Doug Campos-Outcalt, Kenneth Offit, Heather Spencer Feigelson, Celine Hollombe
2013 Genetics in Medicine  
stage iii The purpose of stage III is for a decision-making panel of experts to review the evidence in the summary document and make decisions about clinically actionable IFs.  ...  This method is completely agnostic to the specific technology used to detect any given variant.  ...  DISCLOSURE The authors declare no conflict of interest.  ... 
doi:10.1038/gim.2013.37 pmid:23558254 pmcid:PMC3927794 fatcat:wypc3clfdjhr5mvvxga4v3z57i

Radiomics in hepatic metastasis by colorectal cancer

Vincenza Granata, Roberta Fusco, Maria Luisa Barretta, Carmine Picone, Antonio Avallone, Andrea Belli, Renato Patrone, Marilina Ferrante, Diletta Cozzi, Roberta Grassi, Roberto Grassi, Francesco Izzo (+1 others)
2021 Infectious Agents and Cancer  
Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems.  ...  It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly  ...  Acknowledgements The authors are grateful to Alessandra Trocino, librarian at the National Cancer Institute of Naples, Italy.  ... 
doi:10.1186/s13027-021-00379-y pmid:34078424 fatcat:amgc7zl7y5bevebazfpmtdu4oa

Artificial intelligence in computational pathology – challenges and future directions

Sandra Morales, Kjersti Engan, Valery Naranjo
2021 Digital signal processing (Print)  
The work of Sandra Morales has been co-funded by the Universitat Politècnica de València through the program PAID-10-20.  ...  Especially in medical applications, the black box models can be seen as negative, since a high degree of trust is needed in decision-making.  ...  Due to its high clinical and scientific relevance, several Grand Challenges or competitions on computational pathology have been launched in the recent years with the aim of evaluating the performance  ... 
doi:10.1016/j.dsp.2021.103196 fatcat:gbxv35zmxbembehlhi6i2r7zkq
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