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Breast Cancer Prognostication and Risk Prediction in the Post-Genomic Era [chapter]

Xi Zhao, Ole Christian, Anne-Lise Brresen-Dale
2012 The Continuum of Health Risk Assessments  
molecular classifiers and integrative strategies to improve risk stratification for breast cancer patients.  ...  better breast cancer risk stratification and further to improve treatment decisions in breast cancer patients.  ... 
doi:10.5772/38741 fatcat:baetjzazhzaq7d7pugtzwz4pay

Predicting clinical outcomes in neuroblastoma with genomic data integration

Ilyes Baali, D Alp Emre Acar, Tunde W. Aderinwale, Saber HafezQorani, Hilal Kazan
2018 Biology Direct  
To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis.  ...  Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis.  ...  Acknowledgements We would like to thank Shankar Vembu for discussions on the MVKKM method.  ... 
doi:10.1186/s13062-018-0223-8 pmid:30621745 pmcid:PMC6889397 fatcat:qavn3bpxzjdpld375zjtg3eczm

Applied Machine Learning and Artificial Intelligence in Rheumatology

Maria Hügle, Patrick Omoumi, Jaap van Laar, Joschka Boedecker, Thomas Hügle
2020 Rheumatology Advances in Practice  
Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences.  ...  This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning, and deep learning.  ...  Chin et al. carried out a large-scale, early-disease risk assessment of RA patients using an EMR in Taiwan 32 .  ... 
doi:10.1093/rap/rkaa005 pmid:32296743 pmcid:PMC7151725 fatcat:76uspjabtfamfm42cmlsx4c5g4

Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers

Aurore Lyon, Rina Ariga, Ana Mincholé, Masliza Mahmod, Elizabeth Ormondroyd, Pablo Laguna, Nando de Freitas, Stefan Neubauer, Hugh Watkins, Blanca Rodriguez
2018 Frontiers in Physiology  
Our aim was to identify distinct HCM phenotypes based on ECG computational analysis, and characterize differences in clinical risk factors and anatomical differences using cardiac magnetic resonance (CMR  ...  Aims: Ventricular arrhythmia triggers sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM), yet electrophysiological biomarkers are not used for risk stratification.  ...  A large scale longitudinal study with cardiovascular end-point data will allow robust assessment of ECG phenotyping as an independent tool for accurate risk stratification.  ... 
doi:10.3389/fphys.2018.00213 pmid:29593570 pmcid:PMC5859357 fatcat:rzs4ushafrhg5mz4u746znecny

Parallel and Distributed Processing for Unsupervised Patient Phenotype Representation [chapter]

John Anderson García Heano, Frédéric Precioso, Pascal Staccini, Michel Riveill
2019 Communications in Computer and Information Science  
As a case of study, we have used a clinical dataset from admission and hospital services to build a general purpose inpatient phenotype representation to be used in different medical targets, the first  ...  Likewise, processing unsupervised learning models into a growing clinical data raises many issues, in terms of algorithmic complexity, such as time to model convergence and memory capacity.  ...  In this context, inferring common patient phenotype patterns that could depict disease variations, disease classification and patient stratification, requires massive clinical datasets and computationally  ... 
doi:10.1007/978-3-030-16205-4_1 fatcat:snkhizx2rzcsfnbczfpywtezby

Modelling the distribution of domestic ducks in Monsoon Asia

Thomas P. Van Boeckel, Diann Prosser, Gianluca Franceschini, Chandra Biradar, William Wint, Tim Robinson, Marius Gilbert
2011 Agriculture, Ecosystems & Environment  
Domestic ducks are considered to be an important reservoir of highly pathogenic avian influenza (HPAI), as shown by a number of geospatial studies in which they have been identified as a significant risk  ...  Despite their importance in HPAI epidemiology, their large-scale distribution in monsoon Asia is poorly understood.  ...  Naturally, these differences can potentially be more easily captured by a livestock-oriented stratification system than by unsupervised, data driven stratification systems.  ... 
doi:10.1016/j.agee.2011.04.013 pmid:21822341 pmcid:PMC3148691 fatcat:6bgxzzquczalbcpjote7fryjwy

Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications

Allison Zimmerman, Dinesh Kalra
2020 Reviews in cardiovascular medicine  
stratification.  ...  coronavirus disease 2019 (COVID-19) has rapidly become a global concern, and its cardiovascular manifestations have highlighted the need for fast, sensitive and specific tools for early identification and risk  ...  Acknowledgments We would like to express my gratitude to all those who helped me during the writing of this manuscript. Conflict of interest The authors declare no conflicts of interest statement.  ... 
doi:10.31083/j.rcm.2020.03.120 pmid:33070540 fatcat:uiwanbws2nc6tg4teqkuccj7vy

Subtyping brain diseases from imaging data [article]

Junhao Wen, Erdem Varol, Zhijian Yang, Gyujoon Hwang, Dominique Dwyer, Anahita Fathi Kazerooni, Paris Alexandros Lalousis, Christos Davatzikos
2022 arXiv   pre-print
Our goal is to provide the readers with a broad overview in terms of methodology and clinical applications.  ...  The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment.  ...  Heterogeneity of its clinical presentation has sparked massive research efforts to find subtypes to better delineate its diagnosis [44, 79] .  ... 
arXiv:2202.10945v1 fatcat:3sqq4lrilnguzgbm4iwtzm7wc4

Deep learning in pharmacogenomics: from gene regulation to patient stratification

Alexandr A Kalinin, Gerald A Higgins, Narathip Reamaroon, Sayedmohammadreza Soroushmehr, Ari Allyn-Feuer, Ivo D Dinov, Kayvan Najarian, Brian D Athey
2018 Pharmacogenomics (London)  
, epidemiological, clinical, and demographic datasets.  ...  pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of drugs, targets, and their interactions.  ...  , and stratification in clinical trials on a massive scale as soon as convincing validation emerges from current efforts.  ... 
doi:10.2217/pgs-2018-0008 pmid:29697304 fatcat:tkhmrqkevjfqxdty6ttbw33jam

Application of Artificial Intelligence in the Prevention, Diagnosis and Treatment of Alzheimer's Disease: New Hope for Dealing with Aging in China [chapter]

Hangtian Wang, Guofu Wang
2021 Advances in Transdisciplinary Engineering  
More researches should be performed to improve the prognosis of patients with AD in the future.  ...  Over the past decades, artificial intelligence (AI) was more and more widely used in the prevention, diagnosis and treatment of AD, which might be helpful to deal with the aging of population in China.  ...  However, the investigation of these scales was complicated and time-consuming.  ... 
doi:10.3233/atde210248 fatcat:6veickavmbhzzf6gktcnh3e3sa

Artificial intelligence and machine learning in emergency medicine: a narrative review

Brianna Mueller, Takahiro Kinoshita, Alexander Peebles, Mark A. Graber, Sangil Lee
2022 Acute Medicine & Surgery  
This review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations  ...  Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI.  ...  Massive efforts should also be made for anonymization and de-identification of the data to protect patients' privacy.  ... 
doi:10.1002/ams2.740 pmid:35251669 pmcid:PMC8887797 fatcat:otj7fjb3uzfg5h2x2vnqwpacom

Evaluation of Biomarkers in Critical Care and Perioperative Medicine

Sabri Soussi, Gary S. Collins, Peter Jüni, Alexandre Mebazaa, Etienne Gayat, Yannick Le Manach
2020 Anesthesiology  
Biomarkers studies are often presented with flaws in the statistical analysis that preclude them from providing a scientifically valid and clinically relevant message for clinicians.  ...  This Readers' Toolbox article aims to be a starting point to nonexpert readers and investigators to understand traditional and emerging research methods to assess biomarkers in critical care and perioperative  ...  in clinical studies. 45 While machine learning algorithms are often declared to perform well, they require very large datasets, massive computations, and sufficient expertise. 46 s such, they should  ... 
doi:10.1097/aln.0000000000003600 pmid:33216849 fatcat:yyemw3wtbnbs7fppw7vzqrv3eu

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

Konstantinos C. Siontis, Peter A. Noseworthy, Zachi I. Attia, Paul A. Friedman
2021 Nature Reviews Cardiology  
In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making  ...  Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation  ...  to clinical risk stratification.  ... 
doi:10.1038/s41569-020-00503-2 pmid:33526938 pmcid:PMC7848866 fatcat:jc2b2lb7qjcs3nnk5c5t5t2gry

Metabolomics - the stethoscope for the 21st century

Hutan Ashrafian, Viknesh Sounderajah, Robert Glen, Timothy Ebbels, Benjamin J. Blaise, Dipak Kalra, Kim Kultima, Ola Spjuth, Leonardo Tenori, Reza Salek, Namrata Kale, Kenneth Haug (+7 others)
2020 Medical Principles and Practice  
This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice and discuss the translational challenges that  ...  This field could provide clinicians with new sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualised level.  ...  This rigidity understandably leads to several practical challenges when attempting to apply this approach to large datasets.  ... 
doi:10.1159/000513545 pmid:33271569 pmcid:PMC8436726 fatcat:epd2weiif5bdbhuz23aenccury

Clinically useful brain imaging for neuropsychiatry: How can we get there?

Michael P. Milham, R. Cameron Craddock, Arno Klein
2017 Depression and Anxiety  
We assert that there is no single advance that currently has the potential to drive the field of clinical brain imaging forward.  ...  In particular, we focus on advances that are helping to: 1) elucidate the research agenda for biological psychiatry (e.g., neuroscience focus, precision medicine), 2) shift research models for clinical  ...  Scaling up data resources The natural prerequisite to successful implementation of the big data model is the accrual of massive-scale, heterogeneous and deeply phenotyped datasets.  ... 
doi:10.1002/da.22627 pmid:28426908 fatcat:57bxeldmuvcpros4bx4dfzad6u
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