scholarly journals Pruritus and prurigo: a significant advancement on diagnosis, classification, pathogenesis and treatment

2021 ◽  
Vol 35 (11) ◽  
pp. 2261-2262
Author(s):  
A. Reich
Keyword(s):  
Diagnosis Classification

Development of Fault Diagnosis Classification Method System using Mahalanobis Distance in Micro Wind Turbine

2015 ◽  
Vol 135 (9) ◽  
pp. 577-578
Author(s):  
Toshio Bouno ◽  
Shinichi Arimori ◽  
Toshifumi Yuji ◽  
Hiroyuki Kinoshita
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Mahalanobis Distance ◽  
Classification Method ◽  
Diagnosis Classification

Traumatic brain injury diagnosis classification and evaluation

2015 ◽  
pp. 36-45
Author(s):  
Yevgeny Zadov ◽  
Nikole Zadov
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Diagnosis Classification

scholarly journals Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3139
Author(s):  
Julian Varghese ◽  
Catharina Marie van Alen ◽  
Michael Fujarski ◽  
Georg Stefan Schlake ◽  
Julitta Sucker ◽  
...  
Keyword(s):  
Low Noise ◽  
Disease Classification ◽  
Multilayer Perceptrons ◽  
Sensor Validation ◽  
Diagnostic Potential ◽  
A Prospective Study ◽  
Learning Architectures ◽  
Diagnosis Classification ◽  
Healthy Participants ◽  
Digital Biomarkers

Smartwatches provide technology-based assessments in Parkinson’s Disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. The most advanced task of distinguishing PD vs. DD was evaluated with 74.1% balanced accuracy, 86.5% precision and 90.5% recall by Multilayer Perceptrons. Deep-learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle tremor signs with low noise. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers, but it remains challenging for distinguishing similar disorders.

Chronic pancreatitis: Diagnosis, classification, and new genetic developments

2001 ◽  
Vol 120 (3) ◽  
pp. 682-707 ◽  
Author(s):  
Babak Etemad ◽  
David C. Whitcomb
Keyword(s):  
Chronic Pancreatitis ◽  
Diagnosis Classification

scholarly journals Broken Energy Homeostasis and Obesity Pathogenesis: The Surrounding Concepts

2018 ◽  
Vol 7 (11) ◽  
pp. 453 ◽  
Author(s):  
Abdelaziz Ghanemi ◽  
Mayumi Yoshioka ◽  
Jonny St-Amand
Keyword(s):  
Energy Balance ◽  
Population Health ◽  
Clinical Evaluation ◽  
Health Problem ◽  
Psychological Factors ◽  
Energy Homeostasis ◽  
Fat Accumulation ◽  
Underlying Mechanisms ◽  
Diagnosis Classification

Obesity represents an abnormal fat accumulation resulting from energy imbalances. It represents a disease with heavy consequences on population health and society economy due to its related morbidities and epidemic proportion. Defining and classifying obesity and its related parameters of evaluation is the first challenge toward understanding this multifactorial health problem. Therefore, within this review we report selected illustrative examples of the underlying mechanisms beyond the obesity pathogenesis which is systemic rather than limited to fat accumulation. We also discuss the gut-brain axis and hormones as the controllers of energy homeostasis and report selected impacts of obesity on the key metabolic tissues. The concepts of “broken energy balance” is detailed as the obesity starting key step. Sleep shortage and psychological factors are also reported with influences on obesity development. Importantly, describing such mechanistic pathways would allow clinicians, biologists and researchers to develop and optimize approaches and methods in terms of diagnosis, classification, clinical evaluation, treatment and prognosis of obesity.

scholarly journals Nurses' perception of power regarding their clinical role

2009 ◽  
Vol 17 (2) ◽  
pp. 234-239 ◽  
Author(s):  
Diná de Almeida Lopes Monteiro da Cruz ◽  
Cibele Andrucioli de Mattos Pimenta ◽  
Maria Fernanda Vita Pedrosa ◽  
Antônio Fernandes da Costa Lima ◽  
Raquel Rapone Gaidzinski
Keyword(s):  
Clinical Practice ◽  
Classification Systems ◽  
Clinical Role ◽  
Clinical Nurses ◽  
Nursing Diagnosis ◽  
Before And After ◽  
Power Perception ◽  
The Impact ◽  
Diagnosis Classification ◽  
Reliability Coefficients

This article reports on a study on nurses' perception of power regarding their clinical role before and after implementation of a nursing diagnosis classification. Sixty clinical nurses (average age = 37.2 ± 7.0 years) from a Brazilian teaching hospital answered the Power as Knowing Participation in Change Tool (PKPCT) before and after the implementation of a diagnosis classification. PKPCT has four domains and provides total and partial scores. Reliability coefficients ranged from 0.88 to 0.98. Total scores were not statistically different between assessments (p=0.21), although scores in the "Involvement in Creating Change" domain were higher in the second assessment (p=0.04). Further studies providing sound evidence regarding the impact of nursing classification systems on nurses' power perception are needed to guide decisions on teaching and clinical practice.

Enteroviruses: Vaccines, epidemiology, diagnosis, classification

1970 ◽  
Vol 1 (1) ◽  
pp. 87-94 ◽  
Author(s):  
Joseph L. Melnick ◽  
Charles A. Phillips
Keyword(s):  
Diagnosis Classification
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