| 1. |
- Fleyeh, Hasan, et al.
(författare)
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Extracting Body Landmarks from Videos for Parkinson Gait Analysis
- 2019
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Ingår i: Proceedings - IEEE Symposium on Computer-Based Medical Systems. - 9781728122861 ; , s. 379-384
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Konferensbidrag (refereegranskat)abstract
- Patients with Parkinson disease (PD) exhibit a gait disorder called festinating gait which is caused by deficiency of dopamine in the basal ganglia. To analyze gait of patients with PD, different spatiotemporal parameters such as stride length, cadence, and walking speed should be calculated. This paper aims to present a method to extract useful information represented by the positions of certain landmarks on the human body that can be used for analysis of PD patients’ gait. This method is tested using 132 videos collected from 7 PD patients and 7 healthy controls. The positions of 4 body landmarks, namely body’s center of gravity (COG), the position of the head, and the position of the feet, was computed using a total of more than 41000 of video frames. Results of object’s movement plots show high level of accuracy in the calculation of the body landmarks.
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| 2. |
- Lorenzo, Hadrien, et al.
(författare)
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High-dimensional multi-block analysis of factors associated with thrombin generation potential
- 2019
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Ingår i: Proceedings 2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS). - : IEEE. - 9781728122861 - 9781728122878 ; , s. 453-458
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Konferensbidrag (refereegranskat)abstract
- The identification of novel biological factors associated with thrombin generation, a key biomarker of the coagulation process, remains a relevant strategy to disentangle pathophysiological mechanisms underlying the risk of venous thrombosis (VT). As part of the MARseille THrombosis Association Study (MARTHA), we measured whole blood DNA methylation levels, plasma levels of 300 proteins, 3 thrombin generation biomarkers (endogeneous thrombin potential, peak and lagtime), clinical and genetic data in 700 patients with VT. The application of a novel high-dimensional multi-levels statistical methodology we recently developed, the data driven sparse Partial Least Square method (ddsPLS), on the MARTHA datasets enabled us 1/ to confirm the role of a known mutation of the variability of endogenous thrombin potential and peak, 2/ to identify a new signature of 7 proteins strongly associated with lagtime.
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| 3. |
- Rebane, Jonathan, et al.
(författare)
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An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction
- 2019
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Ingår i: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems. - : IEEE. - 9781728122878 - 9781728122861
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Konferensbidrag (refereegranskat)abstract
- A variety of deep learning architectures have been developed for the goal of predictive modelling in regards to detecting health diagnoses in medical records. Several models have placed strong emphases on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability. In this study we utilise such models with a novel Electronic Patient Record (EPR) data set consisting of both diagnoses and medication data for the purpose of Adverse Drug Event (ADE) prediction. As such, a main contribution of this work is an empirical evaluation of two state-of-the-art deep learning architectures in terms of objective performance metrics for ADE prediction. We also assess the importance of attention mechanisms in regards to their usefulness for medical code-level interpretability, which may facilitate novel insights pertaining to the nature of ADE occurrence within the health care domain.
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