scholarly journals The Sequence of Disease Modifying Anti-Rheumatic Drugs: Pathways to and Predictors of Tocilizumab Monotherapy

2021 ◽  
Author(s):  
Daniel Solomon ◽  
Chang Xu ◽  
Jamie Collins ◽  
Seoyoung C. Kim ◽  
Elena Losina ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Disease Duration ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Tnf Inhibitor ◽  
Discrete State ◽  
Prior Use ◽  
Disease Modifying ◽  
Tocilizumab Monotherapy

Abstract Background: There are numerous non-biologic and biologic disease modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice. Methods: We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every six-months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use.Results: 7,300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. 82% of TCZm use began within three years of starting any bDMARD. 93% of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with use of TCZm included: prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor.Conclusions: Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data.

scholarly journals The sequence of disease-modifying anti-rheumatic drugs: pathways to and predictors of tocilizumab monotherapy

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Daniel H. Solomon ◽  
Chang Xu ◽  
Jamie Collins ◽  
Seoyoung C. Kim ◽  
Elena Losina ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Disease Duration ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Tnf Inhibitor ◽  
Discrete State ◽  
Prior Use ◽  
Disease Modifying ◽  
Tocilizumab Monotherapy

Abstract Background There are numerous non-biologic and biologic disease-modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice. Methods We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every 6 months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use. Results 7300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. Eighty-two percent of TCZm use began within 3 years of starting any bDMARD. Ninety-three percent of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with the use of TCZm included prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor. Conclusions Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data.

scholarly journals The Sequence of Disease Modifying Anti-Rheumatic Drugs: Pathways to and Predictors of Tocilizumab Monotherapy

2020 ◽  
Author(s):  
Daniel Solomon ◽  
Chang Xu ◽  
Jamie Collins ◽  
Seoyoung C. Kim ◽  
Elena Losina ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Disease Duration ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Tnf Inhibitor ◽  
Discrete State ◽  
Prior Use ◽  
Disease Modifying ◽  
Tocilizumab Monotherapy

Abstract Background: There are numerous non-biologic and biologic disease modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice. Methods: We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every six-months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use. Results: 7,300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. 82% of TCZm use began within three years of starting any bDMARD. 93% of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with use of TCZm included: prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor. Conclusions: Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data.

scholarly journals The Sequence of Disease Modifying Anti-Rheumatic Drugs: Pathways to and Predictors of Tocilizumab Monotherapy

2020 ◽  
Author(s):  
Daniel Solomon ◽  
Chang Xu ◽  
Jamie Collins ◽  
Seoyoung C. Kim ◽  
Elena Losina ◽  
...  
Keyword(s):  
Disease Duration ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Tnf Inhibitor ◽  
Factors Associated ◽  
Markov Transition ◽  
Prior Use ◽  
Disease Modifying ◽  
Tocilizumab Monotherapy

Abstract Background: There are numerous non-biologic and biologic disease modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA) Typical sequences of bDMARDs are not clear.Methods: We used data from Corrona, a large real-world RA registry, to examine the sequence of bDMARDs in patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a Markov transition state model, assessing treatments every six-months, to determine if they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use.Results: 7,300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 71.8% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. 82% of TCZm use began within three years of starting any bDMARD. 93% of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with use of TCZm included: prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor.Conclusions: Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using big data.

scholarly journals Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data

2020 ◽  
Vol 34 (04) ◽  
pp. 6853-6860
Author(s):  
Xuchao Zhang ◽  
Xian Wu ◽  
Fanglan Chen ◽  
Liang Zhao ◽  
Chang-Tien Lu
Keyword(s):  
Real World ◽  
Large Scale ◽  
Learning Algorithm ◽  
Noisy Data ◽  
Training Set ◽  
Robust Learning ◽  
Robust Model ◽  
Small Set ◽  
Real World Datasets ◽  
Theoretical Analyses

The success of training accurate models strongly depends on the availability of a sufficient collection of precisely labeled data. However, real-world datasets contain erroneously labeled data samples that substantially hinder the performance of machine learning models. Meanwhile, well-labeled data is usually expensive to obtain and only a limited amount is available for training. In this paper, we consider the problem of training a robust model by using large-scale noisy data in conjunction with a small set of clean data. To leverage the information contained via the clean labels, we propose a novel self-paced robust learning algorithm (SPRL) that trains the model in a process from more reliable (clean) data instances to less reliable (noisy) ones under the supervision of well-labeled data. The self-paced learning process hedges the risk of selecting corrupted data into the training set. Moreover, theoretical analyses on the convergence of the proposed algorithm are provided under mild assumptions. Extensive experiments on synthetic and real-world datasets demonstrate that our proposed approach can achieve a considerable improvement in effectiveness and robustness to existing methods.

scholarly journals A supervised machine-learning method for optimizing the automatic transmission system of wind turbines

2022 ◽  
Vol 10 (1) ◽  
pp. 35-56 ◽  
Author(s):  
Habeeb A. H. R. Aladwani ◽  
Mohd Khairol Anuar Ariffin ◽  
Faizal Mustapha
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Turbines ◽  
Large Scale ◽  
Learning Algorithm ◽  
Automatic Transmission ◽  
Transmission System ◽  
Supervised Machine Learning ◽  
Small Scale ◽  
Low Efficiency

Large-scale wind turbines mostly use Continuously Variable Transmission (CVT) as the transmission system, which is highly efficient. However, it comes with high complexity and cost too. In contrast, the small-scale wind turbines that are available in the market offer a one-speed gearing system only where no gear ratios are varied, resulting in low efficiency of harvesting energy and leading to gears failure. In this research, an unsupervised machine-learning algorithm is proposed to address the energy efficiency of the automatic transmission system in vertical axis wind turbines (VAWT), to increase its efficiency in harvesting energy. The aim is to find the best adjustment for VAWT while the automatic transmission system is taken into account. For this purpose, the system is simulated and tested under various gear ratios conditions while a centrifugal clutch is applied to automatic gear shifting. The outcomes indicated that the automatic transmission system could successfully adjust the spinning in line with the wind speed. As a result, the obtained level of harvested voltage and power by VAWT with the automatic transmission system are improved significantly. Consequently, it is concluded that automatic VAWTs, equipped with the machine-learning capability can readjust themselves with the wind speed more efficiently.

scholarly journals A Whole-Brain Functional Connectivity Model of Alzheimer’s Disease Pathology

2021 ◽  
Author(s):  
Ruchika S. Prakash ◽  
Michael R. McKenna ◽  
Oyetunde Gbadeyan ◽  
Anita R. Shankar ◽  
Rebecca Andridge ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Predictive Modeling ◽  
Large Scale ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Whole Brain ◽  
Functional Connections ◽  
Model Of Alzheimer’S Disease

AbstractEarly detection of Alzheimer’s disease (AD) is a necessity as prognosis is poor upon symptom onset. Although previous work diagnosing AD from protein-based biomarkers has been encouraging, cerebrospinal (CSF) biomarker measurement of AD proteins requires invasive lumbar puncture, whereas assessment of direct accumulation requires radioactive substance exposure in positron emission tomography (PET) imaging. Functional magnetic resonance imaging (fMRI)-based neuromarkers, offers an alternative, especially those built by capitalizing on variance distributed across the entire human connectome. In this study, we employed connectome-based predictive modeling (CPM) to build a model of functional connections that would predict CSF p-tau/Aβ42 (PATH-fc model) in individuals diagnosed with Mild Cognitive Impairment (MCI) and AD dementia. fMRI, CSF-based biomarker data, and longitudinal data from neuropsychological testing from the Alzheimer’s Disease NeuroImaging Initiative (ADNI) were utilized to build the PATH-fc model. Our results provide support for successful in-sample fit of the PATH-fc model in predicting AD pathology in MCI and AD dementia individuals. The PATH-fc model, distributed across all ten canonical networks, additionally predicted cognitive decline on composite measures of global cognition and executive functioning. Our highly distributed pathology-based model of functional connectivity disruptions had a striking overlap with the spatial affinities of amyloid and tau pathology, and included the default mode network as the hub of such network-based disruptions in AD. Future work validating this model in other external datasets, and to midlife adults and older adults with no known diagnosis, will critically extend this neuromarker development work using fMRI.Significance StatementAlzheimer’s disease (AD) is clinical-pathological syndrome with multi-domain amnestic symptoms considered the hallmark feature of the disease. However, accumulating evidence from autopsy studies evince support for the onset of pathophysiological processes well before the onset of symptoms. Although CSF- and PET-based biomarkers provide indirect and direct estimates of AD pathology, both methodologies are invasive. In here, we implemented a supervised machine learning algorithm – connectome-based predictive modeling – on fMRI data and found support for a whole-brain model of functional connectivity to predict AD pathology and decline in cognitive functioning over a two-year period. Our study provides support for AD pathology dependent functional connectivity disturbances in large-scale functional networks to influence the trajectory of key cognitive domains in MCI and AD patients.

scholarly journals Unsupervised functional neurocartography of the Aplysia buccal ganglion

2021 ◽  
Author(s):  
Renan M Costa ◽  
Vijay A Dharmaraj ◽  
Ryota Homma ◽  
Curtis L Neveu ◽  
William B Kristan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Motor Pattern ◽  
Pattern Generation ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Buccal Ganglion ◽  
Multiple Features ◽  
Selection Of

A major limitation of large-scale neuronal recordings is the difficulty in locating the same neuron in different subjects, referred to as the "correspondence" issue. This issue stems, at least in part, from the lack of a unique feature that unequivocally identifies each neuron. One promising approach to this problem is the functional neurocartography framework developed by Frady et al. (2016), in which neurons are identified by a semi-supervised machine learning algorithm using a combination of multiple selected features. Here, the framework was adapted to the buccal ganglia of Aplysia. Multiple features were derived from neuronal activity during motor pattern generation, responses to peripheral nerve stimulation, and the spatial properties of each cell. The feature set was optimized based on its potential usefulness in discriminating neurons from each other, and then used to match putatively homologous neurons across subjects with the functional neurocartography software. A matching method was developed based on a cyclic matching algorithm that allows for unsupervised extraction of groups of neurons, thereby enhancing scalability of the analysis. Cyclic matching was also used to automate the selection of high-quality matches, which allowed for unsupervised implementation of the machine learning algorithm. This study paves the way for investigating the roles of both well-characterized and previously uncharacterized neurons in Aplysia, as well as helps to adapt this framework to other systems.

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1588-P ◽  
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
AMBRISH MITHAL ◽  
SHASHANK JOSHI ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nationwide Study

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2258-PUB
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
SHASHANK JOSHI ◽  
AMBRISH MITHAL ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real World ◽  
Large Scale ◽  
Nationwide Study
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