Detail publikace

Automatic Patient Functionality Assessment from Multimodal Data using Deep Learning Techniques - Development and Feasibility Evaluation

SUKEI, E. DE LEON MARTINEZ, S. OLMOS, M. ARTES, A.

Originální název

Automatic Patient Functionality Assessment from Multimodal Data using Deep Learning Techniques - Development and Feasibility Evaluation

Typ

článek v časopise ve Scopus, Jsc

Jazyk

angličtina

Originální abstrakt

Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.

Klíčová slova

In-situ patient monitoring, Digital phenotyping, Ecological momentary, Assessment, Time-series modelling, Attention models, Transfer learning

Autoři

SUKEI, E.; DE LEON MARTINEZ, S.; OLMOS, M.; ARTES, A.

Vydáno

8. 8. 2023

ISSN

2214-7829

Periodikum

Internet Interventions

Ročník

33

Číslo

100657

Stát

Nizozemsko

Strany od

1

Strany do

9

Strany počet

9

URL

https://www.sciencedirect.com/science/article/pii/S221478292300057X

BibTex

@article{BUT184781,
  author="SUKEI, E. and DE LEON MARTINEZ, S. and OLMOS, M. and ARTES, A.",
  title="Automatic Patient Functionality Assessment from Multimodal Data using Deep Learning Techniques - Development and Feasibility Evaluation",
  journal="Internet Interventions",
  year="2023",
  volume="33",
  number="100657",
  pages="1--9",
  doi="10.1016/j.invent.2023.100657",
  issn="2214-7829",
  url="https://www.sciencedirect.com/science/article/pii/S221478292300057X"
}