PRISMA flow chart for studies selection. by Arsalan Humayun (19192927)

datasheet1_Spatio-Temporal Inversion Using the Selection Kalman Model.zip by Maxime Conjard (10710708)

“…An efficient recursive algorithm for assessing the selection Kalman model is specified. …”

DataSheet1_Spatio-Temporal Inversion Using the Selection Kalman Model.pdf by Maxime Conjard (10710708)

“…An efficient recursive algorithm for assessing the selection Kalman model is specified. …”

Image_1_On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell.TIF by Milagros Marín (7858850)

“…We overcome the intrinsic limitations of the extant optimization methods by proposing an alternative optimization component based on multimodal algorithms. This approach can natively explore a diverse population of neuron model configurations. …”

Data Sheet 1_Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned.pdf by Marketa Ciharova (8991782)

“…</p>Conclusions<p>The complexity of input features needed for machine-learning detection of stress severity based on multimodal data requires large sample sizes with wide variability of stress reactions and inputs among participants. …”

Table 1_Combinations of multimodal neuroimaging biomarkers and cognitive test scores to identify patients with cognitive impairment.docx by Yuriko Nakaoku (17874681)

“…The structural, perfusion, and diffusion MRI-derived biomarkers remained in the identification model with variable selection with the elastic net algorithm, and were thus considered important variables.…”

Image1_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.tiff by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table2_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.xlsx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table5_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.docx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table3_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.xlsx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table2_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.xlsx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table5_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.docx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table1_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.docx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table4_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.xlsx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table4_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.xlsx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Image1_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.tiff by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table1_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.docx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Table3_Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment.xlsx by Fredrick Zmudzki (15428639)

“…Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.…”

Data_Sheet_1_Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach.pdf by Anna Michela Gaeta (6298433)

“…Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers.…”

Supplementary file 1_Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia: a retrospective cohort study.pdf by Mengdie Liu (21416774)

“…Feature selection was performed using Lasso Regression, XGBoost, and Random Forest machine learning algorithms, and a nomogram model was developed using univariate and multivariate Cox regression analyses, with validation of its accuracy, concordance, and clinical applicability.…”