Robust priors for regularized regression

Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01463-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lisha Yu ◽

Yang Zhao ◽

Hailiang Wang ◽

Tien-Lung Sun ◽

Terrence E. Murphy ◽

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Keyword(s):

Predictive Models ◽

Cross Validation ◽

Motor Coordination ◽

Predictive Accuracy ◽

Short Form ◽

Community Dwelling ◽

Regularized Regression ◽

Balance Assessment ◽

Timed Up And Go ◽

Functional Balance

Abstract Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

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Learning performance of regularized regression with multiscale kernels based on Markov observations

Applied Mathematics and Computation ◽

10.1016/j.amc.2021.126386 ◽

2021 ◽

Vol 409 ◽

pp. 126386

Author(s):

Lu Liu ◽

Wei Huang ◽

Li Shen

Keyword(s):

Learning Performance ◽

Regularized Regression

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Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Scientific Reports ◽

10.1038/s41598-021-86114-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jun Pyo Kim ◽

Jonghoon Kim ◽

Hyemin Jang ◽

Jaeho Kim ◽

Sung Hoon Kang ◽

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Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Apoe Genotype ◽

Texture Features ◽

Structural Mri ◽

Regularized Regression ◽

Mr Images ◽

Baseline Model ◽

Imaging Model ◽

T1 And T2

AbstractPredicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71–0.74, AUC for validation = 0.68–0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.

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