scholarly journals Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach

2020 ◽  
Vol 10 (6) ◽  
pp. 2137 ◽  
Author(s):  
David Perpetuini ◽  
Antonio Maria Chiarelli ◽  
Daniela Cardone ◽  
Sergio Rinella ◽  
Simona Massimino ◽  
...  
Keyword(s):  
Ankle Brachial Index ◽  
Absolute Pressure ◽  
Cardiovascular Risks ◽  
Learning Framework ◽  
Non Invasive ◽  
Machine Learning Approach ◽  
Data Driven Approach ◽  
Status Assessment ◽  
Blood Pressures ◽  
Commercial Instrument

Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks.

Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates

Synlett ◽  
2020 ◽  
Author(s):  
Akira Yada ◽  
Kazuhiko Sato ◽  
Tarojiro Matsumura ◽  
Yasunobu Ando ◽  
Kenji Nagata ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Reaction Rates ◽  
Initial Reaction Rate ◽  
Training Dataset ◽  
Initial Reaction ◽  
Learning Approach ◽  
Learning Framework ◽  
Machine Learning Approach ◽  
Reasonable Prediction ◽  
Epoxidation Of Alkenes

AbstractThe prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset.

Magnetresonanzphasenkontrastgeschwindigkeitsmessungen als noninvasive Nachbeobachtungsmaßnahme nach perkutaner transluminaler Angioplastie

VASA ◽  
2002 ◽  
Vol 31 (4) ◽  
pp. 235-240 ◽  
Author(s):  
Heverhagen ◽  
Wagner ◽  
Bandorski ◽  
Hoppe ◽  
Alfke
Keyword(s):  
Phase Contrast ◽  
Ankle Brachial Index ◽  
Transluminal Angioplasty ◽  
Walking Distance ◽  
Slice Thickness ◽  
Non Invasive ◽  
The Mean ◽  
Percutaneous Transluminal ◽  
Phase Contrast Sequence

Background: The aim of this study was to evaluate magnetic resonance phase contrast velocimetry (MRVL) as a non-invasive follow up tool to assess restenosis after percutaneous transluminal angioplasty (PTA). Patients and methods: We prospectively investigated 51 consecutive patients who underwent PTA of the femoropopliteal region. MRVL was conducted prior, one day, six weeks, twelve weeks and 24 weeks after PTA using a circular polarized extremity coil and a gradient echo sequence (TR/TE 600/6 ms, flip angle 30°, slice thickness 10 mm). Hemodynamic data, derived from the MR phase contrast sequence, allowed to calculate the degree of area stenosis of the lesion treated with PTA. These data were correlated with clinical hemodynamic parameters (ankle-brachial index and walking distance). Results: The mean grade of area stenosis was 69% ± 27% before PTA, 30% ± 20% one day, 29% ± 23% six weeks, 39% ± 17% twelve weeks and 42% ± 18% 24 weeks after PTA and correlated well with clinical data and the post angioplasty clinical course of the patients. Conclusions: Follow up measurements using MRVL are suitable to assess restenosis after PTA and allow quantifying the grade of recurrent stenosis as well as the hemodynamic consequences.

scholarly journals A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3159
Author(s):  
Niccolò Pancino ◽  
Caterina Graziani ◽  
Veronica Lachi ◽  
Maria Lucia Sampoli ◽  
Emanuel Ștefǎnescu ◽  
...  
Keyword(s):  
Statistical Tests ◽  
Size Variation ◽  
Non Invasive ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Successful Execution ◽  
Specific Order ◽  
Trail Making ◽  
Patient Groups ◽  
Clustering Approach

Eye-tracking can offer a novel clinical practice and a non-invasive tool to detect neuropathological syndromes. In this paper, we show some analysis on data obtained from the visual sequential search test. Indeed, such a test can be used to evaluate the capacity of looking at objects in a specific order, and its successful execution requires the optimization of the perceptual resources of foveal and extrafoveal vision. The main objective of this work is to detect if some patterns can be found within the data, to discern among people with chronic pain, extrapyramidal patients and healthy controls. We employed statistical tests to evaluate differences among groups, considering three novel indicators: blinking rate, average blinking duration and maximum pupil size variation. Additionally, to divide the three patient groups based on scan-path images—which appear very noisy and all similar to each other—we applied deep learning techniques to embed them into a larger transformed space. We then applied a clustering approach to correctly detect and classify the three cohorts. Preliminary experiments show promising results.

A machine learning approach for mortality prediction only using non-invasive parameters

2020 ◽  
Vol 58 (10) ◽  
pp. 2195-2238
Author(s):  
Guang Zhang ◽  
JiaMeng Xu ◽  
Ming Yu ◽  
Jing Yuan ◽  
Feng Chen
Keyword(s):  
Machine Learning ◽  
Mortality Prediction ◽  
Learning Approach ◽  
Non Invasive ◽  
Machine Learning Approach

Ankle-Brachial Index and cardiovascular events in atrial fibrillation

2016 ◽  
Vol 115 (04) ◽  
pp. 856-863 ◽  
Author(s):  
Giovanni Davi ◽  
Marco Proietti ◽  
Daniele Pastori ◽  
William R. Hiatt ◽  
Gino Roberto Corazza ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular Events ◽  
Hazard Analysis ◽  
Ankle Brachial Index ◽  
Vascular Event ◽  
Vascular Events ◽  
Non Invasive ◽  
Vascular Death ◽  
Online Appendix ◽  
Male Patients

SummaryAtrial fibrillation (AF) patients are at high risk for thrombotic and vascular events related to their cardiac arrhythmia and underlying systemic atherosclerosis. Ankle-Brachial Index (ABI) is a non-invasive tool in evaluating systemic atherosclerosis, useful in predicting cardiovascular events in general population; no data are available in AF patients. ARAPACIS is a prospective multicentre observational study performed by the Italian Society of Internal Medicine, analysing association between low ABI (≤0.90) and vascular events in NVAF out- or in-patients, enrolled in 136 Italian centres. A total of 2,027 non-valvular AF (NVAF) patients aged > 18 years from both sexes followed for a median time of 34.7 (interquartile range: 22.0–36.0) months, yielding a total of 4,614 patient-years of observation. Mean age was 73 ± 10 years old with 55% male patients. A total of 176 patients (8.7%) experienced a vascular event, with a cumulative incidence of 3.81%/patient-year. ABI≤ 0.90 was more prevalent in patients with a vascular event compared with patients free of vascular events (32.2 vs 20.2%, p< 0.05). On Cox proportional hazard analysis, ABI≤ 0.90 was an independent predictor of vascular events (hazard ratio (HR): 1.394, 95% confidence interval (CI): 1.042–1.866; p=0.02), vascular death (HR: 2.047, 95% CI: 1.255-3.338; p=0.004) and MI (HR: 2.709, 95%> CI: 1.485-5.083; p=0.001). This latter association was also confirmed after excluding patients with previous MI (HR: 2.901, 95% CI: 1.408-5.990, p=0.004). No association was observed between low ABI and stroke/transient ischaemic attack (p=0.91). In conclusion, low ABI is useful to predict MI and vascular death in NVAF patients and may independently facilitate cardiovascular risk assessment in NVAF patients.Note: The review process for this paper was fully handled by C. Weber, Editor in Chief.Listed in the Supplementary Online Appendix Material which is available online at www.thrombosis-online.com.

scholarly journals Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy

2021 ◽  
Author(s):  
Kalum J. Ost ◽  
David W. Anderson ◽  
David W. Cadotte
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
New Technologies ◽  
Machine Learning Techniques ◽  
Massive Datasets ◽  
Learning Framework ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Spinal Cord Disorders ◽  
Degenerative Cervical Myelopathy

With the common adoption of electronic health records and new technologies capable of producing an unprecedented scale of data, a shift must occur in how we practice medicine in order to utilize these resources. We are entering an era in which the capacity of even the most clever human doctor simply is insufficient. As such, realizing “personalized” or “precision” medicine requires new methods that can leverage the massive amounts of data now available. Machine learning techniques provide one important toolkit in this venture, as they are fundamentally designed to deal with (and, in fact, benefit from) massive datasets. The clinical applications for such machine learning systems are still in their infancy, however, and the field of medicine presents a unique set of design considerations. In this chapter, we will walk through how we selected and adjusted the “Progressive Learning framework” to account for these considerations in the case of Degenerative Cervical Myeolopathy. We additionally compare a model designed with these techniques to similar static models run in “perfect world” scenarios (free of the clinical issues address), and we use simulated clinical data acquisition scenarios to demonstrate the advantages of our machine learning approach in providing personalized diagnoses.

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