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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149092" target="_blank" >RIV/00216305:26230/23:PU149092 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S221478292300057X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S221478292300057X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.invent.2023.100657" target="_blank" >10.1016/j.invent.2023.100657</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Internet Interventions

ISSN

2214-7829

e-ISSN

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Volume of the periodical

33

Issue of the periodical within the volume

100657

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

9

Pages from-to

1-9

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85166982926