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Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Jamie Miles, Janette Turner, Richard Jacques, Julia Williams, Suzanne Mason
2020 Diagnostic and Prognostic Research  
Background The primary objective of this review is to assess the accuracy of machine learning methods in their application of triaging the acuity of patients presenting in the Emergency Care System (ECS  ...  The index test is a machine-learning algorithm that aims to stratify the acuity of incoming patients at initial triage.  ...  Most studies used hospital admission as an outcome for prediction. The objective of this review was to assess the accuracy of different machine learning methods.  ... 
doi:10.1186/s41512-020-00084-1 pmid:33024830 pmcid:PMC7531169 fatcat:vs4my7e32vfdzfl754vezjhz3y

The diagnostic accuracy of artificial intelligence in thoracic diseases

Yi Yang, Gang Jin, Yao Pang, Wenhao Wang, Hongyi Zhang, Guangxin Tuo, Peng Wu, Zequan Wang, Zijiang Zhu
2020 Medicine  
We will report study characteristics and assess methodological quality using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool.  ...  The primary objective is to assess the diagnostic accuracy of thoracic cancers, including assessing potential biases and calculating combined estimates of sensitivity, specificity, and area under the receiver  ...  Our main objective is to assess the accuracy of the diagnosis of thoracic diseases, including assessing potential biases and calculating combined estimates of sensitivity, specificity, and area under the  ... 
doi:10.1097/md.0000000000019114 pmid:32049826 pmcid:PMC7035064 fatcat:7nl34znp4bdunaosszg522wzem

Adversarial Removal of Demographic Attributes Revisited

Maria Barrett, Yova Kementchedjhieva, Yanai Elazar, Desmond Elliott, Anders Søgaard
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
In light of this, we discuss better methodologies for detecting bias in our models.  ...  In other words, the biases detected in Elazar and Goldberg (2018) seem restricted to their particular data sample, and would therefore not bias the decisions of the model on new samples, whether in-domain  ...  Using random subsamples this way is common in machine learning, including bias detection studies (Elazar and Goldberg, 2018; Zhao et al., 2019) and probing studies (Ravfogel et al., 2018; Lin et al.  ... 
doi:10.18653/v1/d19-1662 dblp:conf/emnlp/BarrettKEES19 fatcat:iojb7upeh5dltl33pmap7iqtui

Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment [article]

Chelsea Barabas, Karthik Dinakar, Joichi Ito, Madars Virza, Jonathan Zittrain
2018 arXiv   pre-print
We argue that a core ethical debate surrounding the use of regression in risk assessments is not simply one of bias or accuracy. Rather, it's one of purpose.  ...  biases that are reflected in the data.  ...  ACKNOWLEDGMENTS The research leading to these results has received funding from the Ethics and Governance of Artificial Intelligence Fund.  ... 
arXiv:1712.08238v2 fatcat:r6m7pq3pubg2fdmsvao4oesqzy

Neuroimaging-Based Biomarkers in Psychiatry: Clinical Opportunities of a Paradigm Shift

Cynthia H Y Fu, Sergi G Costafreda
2013 Canadian journal of psychiatry  
Recently, neuroimaging-based diagnoses and clinical predictions derived from machine learning analysis have shown significant potential for clinical translation.  ...  We conclude that diagnostic and prognostic biomarkers will be developed through the joint application of expert psychiatric knowledge in addition to advanced methods of analysis.  ...  Acknowledgements The Canadian Psychiatric Association proudly supports the In Review series by providing an honorarium to the authors.  ... 
doi:10.1177/070674371305800904 pmid:24099497 fatcat:aca2kagxuzgv5hcv2tullra2qe

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Chung-Yu Chen, Wei-Chi Lin, Hsiao-Yu Yang
2020 Respiratory Research  
Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique.  ...  The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement. There was good accuracy in detecting VAP by sensor array and machine learning techniques.  ...  Ethics approval and consent to participate The Research Ethics Committee (201612235RIND) of National Taiwan University Hospital approved the study protocol, and all subjects provided informed consent.  ... 
doi:10.1186/s12931-020-1285-6 pmid:32033607 pmcid:PMC7006122 fatcat:r7kf2kyirzgvtil5kfr5o6xg54

A multiple testing framework for diagnostic accuracy studies with co-primary endpoints [article]

Max Westphal, Antonia Zapf, Werner Brannath
2020 arXiv   pre-print
In this work, we propose a multiple testing framework for (comparative) phase III diagnostic accuracy studies with sensitivity and specificity as co-primary endpoints.  ...  We conclude that an assessment of multiple promising diagnostic models in the same evaluation study has several advantages when suitable adjustments for multiple comparisons are implemented.  ...  For diagnostic accuracy studies, several design-related sources of bias have been identified in the literature (Lijmer et al., 1999; Rutjes et al., 2006; Schmidt & Factor, 2013; P.  ... 
arXiv:1911.02982v2 fatcat:h3jwppuefjh6ro3xtijzpifzim

Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods

Shu-Ting Luo, Bor-Wen Cheng
2010 Journal of medical systems  
In addition, decision tree (DT), support vector machine-sequential minimal optimization (SVM-SMO) and their ensembles were applied to solve the breast cancer diagnostic problem in an attempt to predict  ...  In this study, we used two feature selection methods, forward selection (FS) and backward selection (BS), to remove irrelevant features for improving the results of breast cancer prediction.  ...  Gordon Turner-Walker for his help in correcting earlier versions of this paper. We also would like to thank the anonymous reviewers for their valuable comments and insightful suggestions.  ... 
doi:10.1007/s10916-010-9518-8 pmid:20703679 fatcat:ekdhddby2zfgjbbktpzwtd2bh4

Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis [article]

Guido van Wingen, Claudi Bockting, Jasper Zantvoord, Babet Wezenberg, Sem Cohen
2020 medRxiv   pre-print
Here, we evaluated the accuracy of MRI-guided response prediction in MDD.  ...  Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment  ...  The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed.  ... 
doi:10.1101/2020.06.27.20141465 fatcat:wv6xb46tjjebpimruxncnmqkvq

Page 1494 of Linguistics and Language Behavior Abstracts: LLBA Vol. 28, Issue 3 [page]

1994 Linguistics and Language Behavior Abstracts: LLBA  
fair test need; vocabulary tests; monolingual Spanish/ bilingual Spanish/English Head Start preschoolers; 9406745 Diagnostic Analysis of Nonverbal Accuracy scale, construct validity data review; 9406389  ...  SDS scores; entering students, U of Utah; 9404836 young children’s language assessment, nonbiased approach validity; empirical data; preschoolers; 9404576 Tests Carleton Academic English Language (CAEL  ... 

Building better biomarkers: brain models in translational neuroimaging

Choong-Wan Woo, Luke J Chang, Martin A Lindquist, Tor D Wager
2017 Nature Neuroscience  
of their diagnostic value; and (iii) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts and populations.  ...  We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings.  ...  AckNowleDGmeNTs We thank our colleagues for discussion of issues surrounding biomarker development and consortium data, including V. Apkarian, M. Banich, D.  ... 
doi:10.1038/nn.4478 pmid:28230847 pmcid:PMC5988350 fatcat:53smgjswmbea3ingvpjhqy6oha

Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism

Dennis P. Wall, Rebecca Dally, Rhiannon Luyster, Jae-Yoon Jung, Todd F. DeLuca, Simon Rogers
2012 PLoS ONE  
We used machine learning techniques to study the complete sets of answers to the ADI-R available at the Autism Genetic Research Exchange (AGRE) for 891 individuals diagnosed with autism and 75 individuals  ...  The Autism Diagnostic Interview-Revised (ADI-R) is one of the most commonly used instruments for assisting in the behavioral diagnosis of autism.  ...  Acknowledgments We would like to thank Vincent Fusaro and members of the Tonellato-Wall weekly lab meetings for input on study design and results interpretation.  ... 
doi:10.1371/journal.pone.0043855 pmid:22952789 pmcid:PMC3428277 fatcat:cdrqpo6zzjbxtezzlfb5xu7a54

Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning

Florentino Luciano Caetano dos Santos, Irmina Maria Michalek, Kaija Laurila, Katri Kaukinen, Jari Hyttinen, Katri Lindfors
2019 Scientific Reports  
The study aimed to determine whether machine learning can be applied to create a new observer-independent method of automatic assessment and classification of the EmA test for celiac disease.  ...  The mean assessment time per image was 16.11 seconds. This is the first study deploying machine learning for the automatic classification of IgA-class EmA test for celiac disease.  ...  Acknowledgements This study was supported by the Academy of Finland, the Sigrid Juselius Foundation, and the Competitive State Research Financing of the Expert Area of Tampere University Hospital.  ... 
doi:10.1038/s41598-019-45679-x pmid:31239486 pmcid:PMC6592927 fatcat:v2yyzpyyjrbc5lexi4eolxtjiq

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

Allison L. Hicks, Nicole Wheeler, Leonor Sánchez-Busó, Jennifer L. Rakeman, Simon R. Harris, Yonatan H. Grad, Thomas R. Ioerger
2019 PLoS Computational Biology  
to the clinical utility and sustainability of machine learning-based molecular diagnostics.  ...  Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics.  ...  Acknowledgments We thank Jung-Eun Shin, Mark Labrador, and members of the Grad Lab for helpful discussion, and Julie Schillinger and Preeti Pathela for assistance identifying, selecting, and characterizing  ... 
doi:10.1371/journal.pcbi.1007349 pmid:31479500 pmcid:PMC6743791 fatcat:v3vaj3t4kjcqdj7jhi5qvbzfnm

Brain simulation augments machine-learning-based classification of dementia [article]

Paul Triebkorn, Leon Stefanovski, Kiret Dhindsa, Margarita-Arimatea Diaz-Cortes, Patrik Bey, Konstantin Bülau, Roopa Kalsank Pai, Andreas Spiegler, Ana Solodkin, Viktor Jirsa, Randy McIntosh, Petra Ritter (+1 others)
2021 bioRxiv   pre-print
diagnostics in Alzheimer's disease.  ...  RESULTS: The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34%  ...  scores in a recent machine learning study [44] .  ... 
doi:10.1101/2021.02.27.433161 fatcat:uv6ukpaw6vaqnpoc3nxvohf4em
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