Additional file 3 of A scoping review of machine learning models to predict risk of falls in elders, without using sensor data

Supplementary Material 3. Supplemental Material S3: All variables used and the highest performing used in the included studies

Institutions

Publication Details

Journal
Figshare
Published
2025-01-01
DOI
https://doi.org/10.6084/m9.figshare.28935466.v1
Primary Topic
Plant Pathogens and Resistance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Additional file 3 of A scoping review of machine learning models to predict risk of falls in elders, without using sensor data

Figshare
Plant Pathogens and Resistance
article

Additional file 3 of A scoping review of machine learning models to predict risk of falls in elders, without using sensor data

article en

Abstract

Supplementary Material 3. Supplemental Material S3: All variables used and the highest performing used in the included studies

Figshare
Scuola Superiore Sant'Anna (IT), VA Palo Alto Health Care System (US), ETH Zurich (CH), Istituto Ortopedico Rizzoli (IT), Fondazione Toscana Gabriele Monasterio (IT), Stanford University (US)
Openalex Percentile: Top 43%
Plant Pathogens and Resistance
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.