scholarly journals Compressed Sensing and Classification of Cardiac Beats using Patient Specific Dictionaries

2016 ◽  
Author(s):  
Monica Fira ◽  
Liviu Goras ◽  
Victor-Andrei Maiorescu ◽  
Mihaela Catalina Luca
Keyword(s):  
Compressed Sensing ◽  
Patient Specific

scholarly journals Contextualizing the Impact of Snakebite Envenoming on Patients: A Qualitative Content Analysis of Patient-Specific Functional Scale Activities Using the International Classification of Functioning, Disability and Health

2021 ◽  
Vol 18 (18) ◽  
pp. 9608
Author(s):  
Anna Tupetz ◽  
Ashley J. Phillips ◽  
Patrick E. Kelly ◽  
Loren K. Barcenas ◽  
Eric J. Lavonas ◽  
...  
Keyword(s):  
Content Analysis ◽  
Treatment Approach ◽  
Qualitative Content Analysis ◽  
The United States ◽  
International Classification ◽  
Patient Specific ◽  
Functional Scale ◽  
Icf Model ◽  
The Impact

To categorize the Patient-specific Functional Scale (PSFS) activities in snakebite envenoming (SBE) using the International Classification of Function (ICF) model in order to describe the impact of SBE on patients’ activities and daily lives and to develop a theoretical SBE model of functioning, we performed a post-hoc analysis of two multi-center, prospective studies, conducted at 14 clinical sites in the United States with consecutive SBE patients presenting to the emergency department. Qualitative content analysis and natural language processing were used to categorize activities reported in the PSFS using the ICF model. Our sample included 93 patients. The mean age was 43.0 (SD 17.9) years, most had lower extremity injuries (59%). A total of 99 unique activities representing eight domains came within the Activity and Participation component of the ICF model, with the majority in the Mobility and General Tasks and Demands domains. The main concerns of SBE patients are the ability to perform daily activities and to engage within their social environment. Applying the ICF model to SBE can facilitate the creation of a patient-centered treatment approach, moving beyond body-structural impairments towards a function-based treatment approach and facilitate early integration of rehabilitation services.

Acute Liver Failure

2017 ◽  
Author(s):  
Cynthia Wang ◽  
Michelle Y. Braunfeld
Keyword(s):  
Supportive Care ◽  
Liver Failure ◽  
Acute Liver Failure ◽  
Intraoperative Monitoring ◽  
Multiorgan Failure ◽  
Anesthetic Management ◽  
Specific Treatment ◽  
Patient Specific ◽  
Drug Induced

Acute liver failure produces widespread physiologic derangements including encephalopathy, coagulopathy, peripheral vasodilation, a systemic inflammatory response, and multiorgan failure. Morbidity is significant, and mortality is 50%. The classification of liver failure and the various etiologies, including viral hepatitis, drug-induced, toxins, and autoimmunity are reviewed here. The multisystem effects of acute liver failure influence all aspects of perioperative care and adequate supportive care during this time is crucial to providing the best possible outcome for the patient. Specific treatment objectives and recommendations are discussed, and the anesthetic management with regard to drug choices, hemodynamic goals, and intraoperative monitoring is reviewed.

scholarly journals Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability*

2017 ◽  
Vol 45 (7) ◽  
pp. e683-e690 ◽  
Author(s):  
Sunil B. Nagaraj ◽  
Siddharth Biswal ◽  
Emily J. Boyle ◽  
David W. Zhou ◽  
Lauren M. McClain ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Patient Specific ◽  
Icu Sedation

scholarly journals A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2902 ◽  
Author(s):  
Mehrdad Davoudi ◽  
Seyyed Mohammadreza Shokouhyan ◽  
Mohsen Abedi ◽  
Narges Meftahi ◽  
Atefeh Rahimi ◽  
...  
Keyword(s):  
High Risk ◽  
Sagittal Plane ◽  
Inertial Sensor ◽  
Patient Specific ◽  
Clinical Settings ◽  
Low Back ◽  
Linear Discriminant ◽  
Significant Difference ◽  
The Right

The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0° in the sagittal plane, as well as 15° and 30° lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each patient. An ANOVA mixed model was conducted on the maximum and average angular velocity, linear acceleration and maximum jerk, respectively. The effect of the three-way interaction of Subgroup by direction by PLM on the mean trunk acceleration was significant. Subgrouping by STarT had no main effect on the kinematic indices in the sagittal plane, although significant effects were observed in the asymmetric directions. A significant difference was also identified during pre-rotation in the transverse plane, where the velocity and acceleration decreased while the jerk increased with increasing asymmetry. The acceleration during trunk flexion was significantly higher than that during extension, in contrast to the velocity, which was higher in extension. A Linear Discriminant Analysis, utilized for classification purposes, demonstrated that 51% of the total performance classifying the three STarT subgroups (65% for high risk) occurred at a position of 15° of rotation to the right during extension. Greater discrimination (67%) was obtained in the classification of the high risk vs. low-medium risk. This study provided a smart “sensor-based” practical methodology for quantitatively assessing and classifying NSLBP patients in clinical settings. The outcomes may also be utilized by leveraging cost-effective inertial sensors, already available in today’s smartphones, as objective tools for various health applications towards personalized precision medicine.

Feature selection and classification of noisy proteomics mass spectrometry data based on one-bit perturbed compressed sensing

2020 ◽  
Vol 36 (16) ◽  
pp. 4423-4431
Author(s):  
Wenbo Xu ◽  
Yan Tian ◽  
Siye Wang ◽  
Yupeng Cui
Keyword(s):  
Mass Spectrometry ◽  
Feature Selection ◽  
Compressed Sensing ◽  
Extreme Case ◽  
Classification Performance ◽  
Mass Spectrometry Data ◽  
Practical Significance ◽  
Supplementary Information ◽  
Prohibitive Cost

Abstract Motivation The classification of high-throughput protein data based on mass spectrometry (MS) is of great practical significance in medical diagnosis. Generally, MS data are characterized by high dimension, which inevitably leads to prohibitive cost of computation. To solve this problem, one-bit compressed sensing (CS), which is an extreme case of quantized CS, has been employed on MS data to select important features with low dimension. Though enjoying remarkably reduction of computation complexity, the current one-bit CS method does not consider the unavoidable noise contained in MS dataset, and does not exploit the inherent structure of the underlying MS data. Results We propose two feature selection (FS) methods based on one-bit CS to deal with the noise and the underlying block-sparsity features, respectively. In the first method, the FS problem is modeled as a perturbed one-bit CS problem, where the perturbation represents the noise in MS data. By iterating between perturbation refinement and FS, this method selects the significant features from noisy data. The second method formulates the problem as a perturbed one-bit block CS problem and selects the features block by block. Such block extraction is due to the fact that the significant features in the first method usually cluster in groups. Experiments show that, the two proposed methods have better classification performance for real MS data when compared with the existing method, and the second one outperforms the first one. Availability and implementation The source code of our methods is available at: https://github.com/tianyan8023/OBCS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Automated patient-specific classification of long-term Electroencephalography

2014 ◽  
Vol 49 ◽  
pp. 16-31 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Turker Ince ◽  
Morteza Zabihi ◽  
Dilek Ince
Keyword(s):  
Patient Specific
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