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Optimizing Healthcare Through Digital Health and Wellness Solutions to Meet the Needs of Patients With Chronic Disease During the COVID-19 Era
2021
Frontiers in Public Health
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It is feared that the addition of COVID-19 survivors to the pool of chronic disease patients will burden an already precarious healthcare system struggling to meet the needs of chronic disease patients ...
, and behavioral science algorithms, data, evidence, and theories to ground treatments. ...
Fifth, create personalized and precise algorithms (models of adherence) through the use of Artificial Intelligence and Machine Learning for each individual, generally executed through cloud computing to ...
doi:10.3389/fpubh.2021.667654
fatcat:o47y7ycrzjhhtfiaq2q5fw63im
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts
[article]
2022
arXiv
pre-print
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Existing and planned legislation stipulates various obligations to provide information about machine learning algorithms and their functioning, often interpreted as obligations to "explain". ...
As a consequence, post-hoc explanation algorithms are unsuitable to achieve the transparency objectives inherent to the legal norms. ...
We trained a gradient boosted tree which achieved a test accuracy of 83%. Diabetes. The Diabetes dataset is a dataset of diabetes patient records. ...
arXiv:2201.10295v1
fatcat:ulepwzs7jbaarmlnax5nl5zzgm
A predictive analytics approach to reducing avoidable hospital readmission
[article]
2014
arXiv
pre-print
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2014 pre-print arXiv:1402.5991v1 fatcat:y3vfkbkztnbrjefwm7u4s4m6ca
Based on the literature, most current risk prediction models fail to reach an acceptable accuracy level; none of them considers patient's history of readmission and impacts of patient attribute changes ...
Medicare anticipates that nearly 17 billion is paid out on the 20 of patients who are readmitted within 30 days of discharge. ...
Jha, 2011; Shulan, Gao, & Moore, 2013) , we do not exclude recurrent (re)admissions of the same patient from the analyses. ...
arXiv:1402.5991v2
fatcat:fkgjyrvjarbodi2klknz6vkb5i
Developing a deep learning system to drive the work of the critical care outreach team
[article]
2020
medRxiv
pre-print
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We propose a novel automated 'watch-list' to identify patients at high risk of deterioration, to help prioritise the work of the outreach team. ...
proof of concept deep learning systems requiring significantly more input data. ...
Following from these techniques, we implemented a data augmentation algorithm that can be applied to discrete time-series events such as those present in the EMR. ...
doi:10.1101/2020.07.07.20148064
fatcat:nuhr732lovg7zexnv535nn6rhm
Integrating the STOP-BANG score and clinical data to predict cardiovascular events after infarction: A machine learning study
2020
Chest
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Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks. ...
ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip-class, GRACE score and LVEF) to identify those patients who developed an in-hospital cardiovascular ...
Discussion In the present study, we implemented an ML algorithm to explore select and integrate a large number of clinical variables and scores (STOP-BANG, Killip, and GRACE) available in patients who ...
doi:10.1016/j.chest.2020.03.074
pmid:32343966
fatcat:dgflwngjkneqdhxccu6cdwjz2m
Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury
2021
Frontiers in Medicine
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We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI.Methods: Using clinical data from patients with sepsis in the ICU at Beijing ...
Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: Recurrent Neural ...
of the ICU admission, but we limited our study population to those who
patients (34). ...
doi:10.3389/fmed.2021.792974
pmid:34957162
pmcid:PMC8703139
fatcat:4jvbwvlfovcuhkpdgicsxno6je
Deep learning in pharmacogenomics: from gene regulation to patient stratification
2018
Pharmacogenomics (London)
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Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance ...
and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. ...
of final diagnosis, patient risk level, and outcome (e.g. mortality, re-admission) (Table 3 ). ...
doi:10.2217/pgs-2018-0008
pmid:29697304
pmcid:PMC6022084
fatcat:tkhmrqkevjfqxdty6ttbw33jam
Quality Improvement Report: Linking guideline to regular feedback to increase appropriate requests for clinical tests: blood gas analysis in intensive care
2001
BMJ (Clinical Research Edition)
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Problem Need to decrease the number of requests for arterial blood gas analysis and increase their appropriateness to reduce the amount of blood drawn from patients, the time wasted by nurses, and the ...
Blood gas analysis is performed at the patient's bedside with three Stat Profile Ultra machines (NOVA biomedical, Waltham, MA) located in the unit. ...
In the public healthcare system a network is being created to enable sharing of medical records. ...
doi:10.1136/bmj.323.7313.620
pmid:11557715
pmcid:PMC1121188
fatcat:owk7ktjoezgcjgi5kjydhjope4
DP-SMOTE: Integrating Differential Privacy and Oversampling Technique to Preserve Privacy in Smart Homes
2024
Al-Azhar Bulletin of Science
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The proposed method employs the SMOTe algorithm and applies Gaussian noise to generate data. Subsequently, it employs a k-anonymity function to assess re-identification risk before sharing the data. ...
This approach is particularly effective in smart homes, offering substantial utility in privacy at a re-identification risk of 30%, with Gaussian noise set to 0.3, SMOTe at 500%, and the application of ...
In addition, it investigates the implemented machine learning methods, such as K-nearest neighbours (KNN), Support vector machines (SVMs), and Naive bayes (NB). ...
doi:10.58675/2636-3305.1669
fatcat:rr4qtkatvfak7op7lmsxt6zdt4
Classification of patients with embolic stroke of undetermined source into cardioembolic and non‐cardioembolic profile subgroups
2022
European journal of neurology ene.15356 (2022). doi:10.1111/ene.15356
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We aimed to determine whether a machine-learning (ML) model could discriminate between ESUS patients with cardioembolic and those with non-cardioembolic profiles using baseline demographic and laboratory ...
When applied to ESUS patients, the model classified 40.3% as having cardioembolic profiles. ...
Because of their mathematical complexity, automated solutions require critical scientific judgement for correct implementation and interpretation [27] . ...
doi:10.18154/rwth-2022-04580
fatcat:gvc2q7rwp5fhbfx2rb5s5ynt4q
Improving In-Hospital Care For Older Adults: A Mixed Methods Study Protocol to Evaluate a System-Wide Sub-Acute Care Intervention in Canada
2022
International Journal of Integrated Care
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implementation outcomes (e.g., facilitators and barriers to success, strategies to better integrate care) using provider and patient interviews. ...
Interventions that seek to improve this transition process are usually evaluated using healthcare use outcomes (e.g., hospital re-visit rates) only, and do not gather provider and patient perspectives ...
Health) for their strong contributions and commitment to this project. ...
doi:10.5334/ijic.5953
pmid:35431701
pmcid:PMC8973798
fatcat:53rde5uebrbcraib6rzchxrrdi
Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review
2020
Pulmonary Therapy
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Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. ...
In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes. ...
No funding or sponsorship was received for this study or publication of this article. ...
doi:10.1007/s41030-020-00110-z
pmid:32048244
fatcat:zpptmomvkzdenlbv4he4eh5gmu
Digital Pathology: The Time Is Now to Bridge the Gap between Medicine and Technological Singularity
[chapter]
2019
Interactive Multimedia [Working Title]
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The purpose of specialist recertification or re-validation for the Royal College of Pathologists of Canada belonging to the Royal College of Physicians and Surgeons of Canada and College of American Pathologists ...
Quantum computing may well represent the technological singularity to create new classifications and taxonomic rules in medicine. ...
Acknowledgements This chapter is dedicated to the 73rd birthday of Professor Kim Solez, who is an American pathologist and co-founder of the Banff Classification, the first standardized international classification ...
doi:10.5772/intechopen.84329
fatcat:ng2qouuzzbd3bn7jzxruyyltv4
Role of Technology for the Management of AKI in Critically Ill Patients: From Adoptive Technology to Precision Continuous Renal Replacement Therapy
2016
Blood Purification
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and IT that will permit the integration of patient care and decisionmaking processes for years to come. ...
We discuss technological aspects of the decision to initiate CRRT and the components of the treatment prescription and delivery, the 249 integration of information technology (IT) on overall patient management ...
In figure 2 , we propose a simple effective algorithm to manage critically ill patients from admission to AKI resolution. ...
doi:10.1159/000448527
pmid:27562206
fatcat:42aukufpgvhppjjielmoiecjwy
Population scale proteomics enables adaptive digital twin modelling in sepsis
[article]
2024
medRxiv
pre-print
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Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. ...
Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. ...
Predict Predict patient probabilities for protein panels to create ILS FPR TPR Figure 2 : machine learning uncovers specific molecular panels in sepsis: a All timeof-admission plasma samples from the ...
doi:10.1101/2024.03.20.24304575
fatcat:qikxveobyjekhd464u2dok5qpy
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