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Machine learning-based infection prediction model for newly diagnosed multiple myeloma patients
2023
Frontiers in Neuroinformatics
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ObjectiveTo understand the infection characteristics and risk factors for infection by analyzing multicenter clinical data of newly diagnosed multiple myeloma (NDMM) patients.MethodsThis study reviewed ...
Multiple machine learning algorithms were compared, and the best performing algorithm was used to build a machine learning prediction model. ...
were included in this study's prediction model, which helps to more fully predict the risk of infection among patients with newly diagnosed myeloma. ...
doi:10.3389/fninf.2022.1063610
pmid:36713288
pmcid:PMC9880856
fatcat:l5kjodyeujcf3nbmycnuqep7qq
Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning
2021
Contrast Media & Molecular Imaging
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Multiple myeloma is firmly identified by examining bone marrow samples under a microscope for myeloma cells. To diagnose myeloma cells, pathologists have to be very selective. ...
We have designed two models. One is for recognizing myeloma cells, and the other is for differentiating them from nonmyeloma cells. ...
Acknowledgments e authors are thankful for the support from Taif University Researchers Supporting Project (TURSP-2020/26), Taif University, Taif, Saudi Arabia. ...
doi:10.1155/2021/4954854
pmid:34955694
pmcid:PMC8660215
fatcat:2fnghtiwmrehldambhjs6ifmyi
COVID ‐19 special issue: Intelligent solutions for computer c ommunication‐assisted infectious disease diagnosis
2022
Expert systems
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By means of the CNN, the article 'Medical image analysis of multiple myeloma based on convolutional neural network', provides the application of neural network algorithm in multiple myeloma . ...
And finally, providing a prediction method for the COVID-19 outbreak using all the said models. ...
BIoT) networks for the prediction of medical diseases. ...
doi:10.1111/exsy.12946
pmid:35602648
pmcid:PMC9111672
fatcat:xzu3vvjjbjbcxeg5luskkuq2qm
How artificial intelligence revolutionizes the world of multiple myeloma
2024
Frontiers in Hematology
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Multiple myeloma is the second most frequent hematologic malignancy worldwide with high morbidity and mortality. ...
Overall, artificial intelligence has the potential to revolutionize multiple myeloma care, being necessary to validate in prospective clinical cohorts and develop models to incorporate into routine daily ...
By contrast, an ML model (32) including tumor burden, cytogenetic (del(17p13) and/or t(4;14)), and immune-related biomarkers predict MRD outcomes in up to 72% of newly diagnosed MM (NDMM) patients treated ...
doi:10.3389/frhem.2024.1331109
fatcat:4uz7x7uqdjbgfow7lt46hvdb2m
D6.1 - Literature mining and preprocessing
2021
Zenodo
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The software is available for the entire consortium in the GenoMed4all GitLab repository and will be made publicly available at a later stage in the project. ...
Study Population: Newly diagnosed, symptomatic, multiple myeloma, candidates for systemic treatment Inclusion Criteria:
Figure 7 . 7 Figure 7. ...
: A Prospective, Longitudinal, Observational Study in Newly Diagnosed Multiple Myeloma (MM) Patients to Assess the Relationship Between Patient Outcomes, Treatment Regimens and Molecular Profiles. ...
doi:10.5281/zenodo.5862467
fatcat:kvjrlnindrfrra5kgu2hfidd7u
Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin
2021
Frontiers in Public Health
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learning (ML) model based on clinical data.Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to ...
The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance.Conclusions: Infectious diseases are still ...
We used Python (version 3.7.3) for algorithm development.
Machine Learning This study was based on the aforementioned differences that were statistically significant indicators to build the model. ...
doi:10.3389/fpubh.2021.800549
pmid:35004599
pmcid:PMC8739804
fatcat:hdmbcvuh6jdkxeyeeyxlrpqgwe
Detecting Multiple Myeloma Infiltration of the Bone Marrow on CT Scans in Patients with Osteopenia: Feasibility of Radiomics Analysis
2022
Diagnostics
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It is difficult to detect multiple myeloma (MM) infiltration of the bone marrow on computed tomography (CT) scans of patients with osteopenia. ...
The developed models were applied to evaluate a temporal validation set. For comparison, three radiologists evaluated the CTs for the possibility of MM infiltration in the bone marrow. ...
The diagnostic performance of the radiomics-based machine learning model was as high as that of an experienced radiologist, and the specificity of the radiomics model was higher than that of an inexperienced ...
doi:10.3390/diagnostics12040923
pmid:35453971
pmcid:PMC9025143
fatcat:gjgkdfnzovb3nd7cpqc32e5dgu
Clinical Features and Risk Stratification of Multiple Myeloma Patients with COVID-19
2023
Cancers
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SARS-CoV-2 infection often results in a more severe COVID-19 disease course in multiple myeloma (MM) patients compared to immunocompetent individuals. ...
Study population includes 34 MM patients with a median age of 61 (range: 35–82 years) who tested positive for SARS-CoV-2 between 1 March 2020–15 August 2021. ...
By utilizing demographic, clinical, and laboratory parameters commonly available in MM patients, another study implemented machine learning algorithms [5] to create a multivariable predictive model for ...
doi:10.3390/cancers15143598
pmid:37509261
pmcid:PMC10377341
fatcat:ybqmi3i5g5b3vej2lgazoecxde
New Markers of Renal Failure in Multiple Myeloma and Monoclonal Gammopathies
2020
Journal of Clinical Medicine
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As we are entering the era of "big data", risk prediction models based on tools of artificial intelligence (machine learning) accrue more data as a sensitive tool, e.g., AKI prediction, which may provide ...
Keywords: biomarkers; kidney injure; monoclonal gammopathies; multiple myeloma
Multiple Myeloma and Renal Impairment-An Overview In the United States, it has been estimated that multiple myeloma (MM) ...
doi:10.3390/jcm9061652
pmid:32486490
pmcid:PMC7355449
fatcat:v36esgimnzgzpgjhzv72epxnoq
Functional multi-omics reveals genetic and pharmacologic regulation of surface CD38 in multiple myeloma
[article]
2021
bioRxiv
pre-print
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CD38 is a surface ectoenzyme expressed at high levels on myeloma plasma cells and is the target for the monoclonal antibodies (mAbs) daratumumab and isatuximab. ...
Genome-wide CRISPR-interference screens integrated with patient-centered epigenetic analysis confirmed known regulators of CD38, such as RARA, while revealing XBP1 and SPI1 as other key transcription factors ...
This work was supported by grants K08 CA184116, R01 CA226851, and the UCSF Stephen and Nancy Grand Multiple Myeloma Translational Initiative (to A.P.W.), NCI P30 CA082103 supporting the Preclinical Therapeutics ...
doi:10.1101/2021.08.04.455165
fatcat:vyuylentobfehf7yvzepmas6bu
Closing the Gap in Surveillance and Audit of Invasive Mold Diseases for Antifungal Stewardship Using Machine Learning
2019
Journal of Clinical Medicine
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We used machine learning-based natural language processing (NLP) to non-selectively screen chest tomography (CT) reports for pulmonary IMD, verified by clinical review against international definitions ...
This is the first successful use of applied machine learning for institutional IMD surveillance across an entire hematology population describing process and outcome measures relevant to AFS. ...
Acknowledgments: We would like to thank Sue Lee for statistical input as well as Elle Phillips and Karli Williamson for assisting with data collection and entry. ...
doi:10.3390/jcm8091390
pmid:31491944
pmcid:PMC6780614
fatcat:txgan4qcqffedchqrob5nxwbii
Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques
2021
PLoS ONE
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This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. ...
To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. ...
Acknowledgments We thank The Medical Biology Research Center, Kermanshah University of Medical Sciences for providing research facilities to conduct this study. ...
doi:10.1371/journal.pone.0254976
pmid:34288963
fatcat:axzm2af3hvaffl4asbqj4amjhi
Malignant clonal evolution drives multiple myeloma cellular ecological diversity and microenvironment reprogramming
2022
Molecular Cancer
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Background Multiple myeloma (MM) is a heterogeneous disease with different patterns of clonal evolution and a complex tumor microenvironment, representing a challenge for clinicians and pathologists to ...
Tumor cell stemness index score and pseudo-sequential clonal evolution analysis can be used to divide the evolution model of MM into two clonal origins: types I and IX. ...
NDMM, newly diagnosed multiple myeloma; RRMM, refractory or recurrent multiple myeloma; GRN, gene regulation network; CNV, copy number variations; SNV, single nucleotide variation; DEG, differentially ...
doi:10.1186/s12943-022-01648-z
pmid:36131282
pmcid:PMC9492468
fatcat:54b2hli4crfffmurxvl7w5z6de
Immunodiagnosis — the promise of personalized immunotherapy
2023
Frontiers in Immunology
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A comprehensive immunodiagnostic model integrating all these three dimensions by artificial intelligence would provide valuable information for predicting treatment response. ...
However, the majority of patients do not benefit from immunotherapy. ...
Virus infection Approximately 10-12% of all newly diagnosed cancer cases worldwide are associated with viral infections (86) . ...
doi:10.3389/fimmu.2023.1216901
pmid:37520576
pmcid:PMC10372420
fatcat:2jgg3qzfljalnaho7rjsdtnxnm
A REVIEW OF ARTIFICIAL INTELLIGENCE IN TREATMENT OF COVID-19
2022
Journal of Pharmaceutical Negative Results
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We discuss how to use AI models in precision medicine, such as how AI models can accelerate COVID-19 drug repurposing. ...
We present guidelines for using AI to accelerate drug repurposing or repositioning, for which AI approaches are formidable and required in this Review. ...
Ensuring the applicability of machine learning models across multiple settings is crucially dependent on machine learning data harmonization. ...
doi:10.47750/pnr.2022.13.s01.31
fatcat:flbcqwontbcr3kwk5fnlea7fxa
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