π’
|
Machine learning to predict mortality after rehabilitation among patients with severe stroke
9 auth.
D. Scrutinio,
C. Ricciardi,
Leandro Donisi,
E. Losavio,
P. Battista,
Pietro Guida,
...
M. Cesarelli,
G. Pagano,
G. D'addio
|
5 |
2020 |
5 π’
|
π
|
Classifying Different Stages of Parkinsonβs Disease Through Random Forests
9 auth.
C. Ricciardi,
M. Amboni,
Chiara De Santis,
G. Ricciardelli,
G. Improta,
L. Iuppariello,
...
G. D'addio,
P. Barone,
M. Cesarelli
|
5 |
2019 |
5 π
|
π¦
|
Reproducibility of short- and long-term Poincare plot parameters compared with frequency-domain HRV indexes in congestive heart failure
7 auth.
G. D'addio,
D. Acanfora,
G. Pinna,
R. Maestri,
G. Furgi,
C. Picone,
...
F. Rengo
|
5 |
1998 |
5 π¦
|
π¬
|
Testing the presence of non stationarities in short heart rate variability series
A. Porta,
G. D'addio,
S. Guzzetti,
D. Lucini,
M. Pagani
|
5 |
2004 |
5 π¬
|
π¬
|
Efficacy of Machine Learning in Predicting the Kind of Delivery by Cardiotocography
G. Improta,
C. Ricciardi,
Francesco Amato,
G. D'addio,
M. Cesarelli,
M. Romano
|
5 |
2019 |
5 π¬
|
π¦
|
Feasibility of Machine Learning in Predicting Features Related to Congenital Nystagmus
G. D'addio,
C. Ricciardi,
G. Improta,
P. Bifulco,
M. Cesarelli
|
5 |
2019 |
5 π¦
|
π’
|
Benchmarking between two wearable inertial systems for gait analysis based on a different sensor placement using several statistical approaches
Leandro Donisi,
G. Pagano,
Giuseppe Cesarelli,
A. Coccia,
Federica Amitrano,
G. D'addio
|
5 |
2020 |
5 π’
|
π’
|
Positive impact of short-term gait rehabilitation in Parkinson patients: a combined approach based on statistics and machine learning.
7 auth.
Leandro Donisi,
Giuseppe Cesarelli,
P. Balbi,
V. Provitera,
B. Lanzillo,
A. Coccia,
...
G. D'addio
|
4 |
2021 |
4 π’
|