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Introduction to the Special Issue on Computational Intelligence for Biomedical Data and Imaging
2020
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Computational intelligence in the field of biomedical data includes working in the field of image processing, computer vision, machine learning, deep learning, big data analytics, and cloud computing. ...
An efficient algorithm that deals with class imbalance is required to get high-performance models with a small database. ...
The article "Random Forest with Self-paced Bootstrap Learning in Lung Cancer Prognosis," authored by Qingyong Wang, Yun Zhou, Weiping Ding, Zhiguo Zhang, Khan Muhammad, and Zehong Cao, presents the improvement ...
doi:10.1145/3381919
fatcat:taaidy72hzaxjcmrozp6qmmbt4
A Multi-Learning Training Approach for distinguishing low and high risk cancer patients
2021
IEEE Access
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Here, we report on the Multi Learning Training (MuLT) algorithm, which employs supervised, unsupervised, and self-supervised learning methods in order to take advantage of the interplay of clinical and ...
All cancers are caused by changes in the DNA within cells that occur over the course of an individual's lifetime. ...
GSE68465 contains data about lung adenocarcinoma, the most common type of lung cancer. These data were originally used on a study of survival prediction in lung adenocarcinoma [54] . ...
doi:10.1109/access.2021.3104820
fatcat:utjccd6sfbgmlmxliqdosujjvq
Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade
[article]
2016
bioRxiv
pre-print
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The Cancer Genome Atlas revealed the genomic landscapes of common human cancers. In parallel, immunotherapy with checkpoint blockers is transforming the treatment of advanced cancers. ...
Using machine learning we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. ...
(a) Major parameters determining immunogenicity in solid cancers revealed using random forest approach. (b) Immunophenogram for the visualization of the parameters determining immunogenicity. ...
doi:10.1101/056101
fatcat:6lsrafpbdvfjbmdxar4k2jhpk4
Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade
2017
Cell Reports
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We have recently developed an analytical strategy to characterize the cellular composition of the immune infiltrates and examined colorectal cancer (CRC) datasets from the TCGA (Angelova et al., 2015) ...
(a) Major parameters determining immunogenicity in solid cancers revealed using random forest approach. (b) Immunophenogram for the visualization of the parameters determining immunogenicity. ...
For each cancer type we used a random forest classification approach, which is based on a multitude of decision trees, including 127 parameters (Supplementary Table S4 ) to separate tumors with high ...
doi:10.1016/j.celrep.2016.12.019
pmid:28052254
fatcat:ixpdydpn5neo7nxmwck7jfpgi4
Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19
2020
Machine Vision and Applications
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, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. ...
It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation. ...
[154] targeted lung cancer detection using deep CNN in addition to a random forest classifier to detail the diagnosis. ...
doi:10.1007/s00138-020-01101-5
pmid:32834523
pmcid:PMC7386599
fatcat:tkkylrptc5hkpoj52hjs3kuttu
Can we use machine learning to discover risk factors? Testing the proof of principle using data on >11,000 predictors and mortality in the UK Biobank
[article]
2021
medRxiv
pre-print
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Machine learning (ML) can harness information from large databases with complex structures. ...
We present a simple and fast hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing ...
This earlier study compared artificial neural network and random forest methods against Cox regression using a set of 60 variables, selected based on their biological plausibility [7] . ...
doi:10.1101/2021.05.07.21256791
fatcat:dv6u46v4sfgcdiviozpiw5jucu
2020 Index IEEE Transactions on Industrial Informatics Vol. 16
2020
IEEE Transactions on Industrial Informatics
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Forests-Based Model for Ultra-Short-Term Prediction of PV Characteristics; TII Jan. 2020 202-214 Imran, A., see Hussain, B., TII Aug. 2020 4986-4996 Imran, M., see Fu, S., TII Sept. 2020 6013-6022 ...
, L., see Cai, H., TII Jan. 2020 587-594 Jiang, L., see Xia, Z., TII Jan. 2020 629-638 Jiang, Q., Yan, S., Yan, X., Yi, H., and Gao, F., Data-Driven Two-Dimensional Deep Correlated Representation Learning ...
., +, TII Aug. 2020 5202-5212 Lung Automated Decision Support System for Lung Cancer Detection and Classification via Enhanced RFCN With Multilayer Fusion RPN. ...
doi:10.1109/tii.2021.3053362
fatcat:blfvdtsc3fdstnk6qoaazskd3i
ACRT-SCTS Scholar Abstracts
2010
Clinical and Translational Science
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High WC was associated with high risk of CV mortality only in women. High BMI and high WC were not associated with all-cancer mortality in either men or women. ...
For polymorphisms without a published meta-analysis we performed one de novo, using allele-based random effects models. ...
lung cancer. ...
doi:10.1111/j.1752-8062.2010.00181_2.x
fatcat:lwvd6nteozgezazejeqpjupawu
CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020
2020
International Journal of Computer Assisted Radiology and Surgery
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In the times of COVID-19 overshadowing CARS 2020 and what the future may hold, a CARS meeting with these numbers of participants is not feasible anymore and new ways have to be explored to still fulfill ...
A hybrid (analogue and digital) CARS 2020 has therefore been envisaged to take place at the University Hospital in Munich, with a balanced combination of analogue/personal and digital presentations and ...
Conclusion This work attempts to provide and validate a self-sustained tool for the automatic performance assessment of virtual temporal bone dissection performed within a mastoidectomy surgical simulator ...
doi:10.1007/s11548-020-02171-6
pmid:32514840
fatcat:lyhdb2zfpjcqbf4mmbunddwroq
The effectiveness and cost-effectiveness of hospital-based specialist palliative care for adults with advanced illness and their caregivers
2020
The Cochrane library
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There is a need for clarity on the effectiveness and optimal models of HSPC, given that most people still die in hospital and also to allocate scarce resources judiciously. ...
Hospital-based specialist palliative care (HSPC) has developed to assist in better meeting the needs of patients and their families and potentially reducing hospital care expenditure. ...
and lung cancers. ...
doi:10.1002/14651858.cd012780.pub2
pmid:32996586
fatcat:l2o5f4rllffrnpqi477iwcy4ey
Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy
[article]
2020
arXiv
pre-print
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Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. ...
., machine learning), and communication (e.g., interaction between the health data centers). ...
Experiments are performed on 416 subjects of the OASIS database implementing Extreme Learning Machine, Bootstrapped Dendritic Computing (BDC), Hybrid Extreme Random Forest, and Random Forest with BDC scoring ...
arXiv:2008.10733v1
fatcat:oj2neoftf5hcbpatnfn7ntyhzy
Cancer diagnostic tools to aid decision-making in primary care: mixed-methods systematic reviews and cost-effectiveness analysis
2020
Health Technology Assessment
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We surveyed 4600 general practitioners in randomly selected UK practices to determine the proportions of general practices and general practitioners with access to, and using, cancer decision support tools ...
Bibliographic searches were conducted on MEDLINE, MEDLINE In-Process, EMBASE, Cochrane Library and Web of Science) in May 2017, with updated searches conducted in November 2018. ...
Effect of delays on prognosis in patients with non-small cell lung cancer. Thorax 2004;59:45-9. ...
doi:10.3310/hta24660
pmid:33252328
fatcat:sot5kiknojg3re5ceanszima4a
32nd Annual Meeting and Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2017): Part One
2017
Journal for ImmunoTherapy of Cancer
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Reference IHC learning (random forest analysis [3]) was conducted to rank the most
photo atlas was made for each type of cancer. ...
the positive correlation with the prognosis in HGS-OC. ...
doi:10.1186/s40425-017-0289-3
fatcat:vtk3uddmajfqfjzebor3hqn64e
The Influence of Past Unemployment Duration on Symptoms of Depression Among Young Women and Men in the United States
2009
American Journal of Public Health
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In Random Forest analyses, the SI scale, followed by comorbidity, best predicted self-reported depression, and no other variable or combination of variables improved prediction compared with the SI scale ...
In Random Forest analyses, the SI scale, followed by comorbidity, best predicted self-reported depression, and no other variable or combination of variables improved prediction compared with the SI scale ...
They divided the unemployed in groups with from five to seven times difference in re-employment rate. ...
doi:10.2105/ajph.2008.152561
pmid:19696382
pmcid:PMC2741513
fatcat:az32zsizqzdahltg7gjrbywiku
Abstracts of the NIH-FDA Conference "Biomarkers and Surrogate Endpoints: Advancing Clinical Research and Applications"
1998
Disease Markers
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The challenge to conference participants is to couple the growing understanding of pathogenesis with tools from the laboratory to develop more precise and accurate measures of disease in patients. ...
Adaptation of these biomarkers as substitutes for clinical endpoints, or surrogate endpoints, in clinical trials has taken on new importance in light of the opportunities now before us. ...
with lung cancer (Sidransky 1997). ...
doi:10.1155/1998/698239
fatcat:kfeuw7vayffm5da47qgzdv2fyi
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