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Prediction of Decline in Activities of Daily Living Through Deep Artificial Neural Networks and Domain Adaptation

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AI*IA 2019 – Advances in Artificial Intelligence (AI*IA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11946))

Abstract

In order to improve information available at the clinical level and to better focus resources for preventive interventions, it is paramount to estimate the general exposure to risk of adverse health events, commonly referred as frailty. This study compares the performance of shallow and deep multilayer perceptrons (sMLP and dMLP), and of long short-term memories (LSTM), on the prediction of a subject decline in activities of daily living, with and without a previous autoencoder based domain adaptation from an external dataset. Samples originates from two large epidemiological datasets: the English Longitudinal Study of Ageing (ELSA) and The Irish Longitudinal Study on Ageing, with 107879 and 15710 eligible samples, respectively. Deep networks performed better than shallow ones, while dMLP and LSTM performance were similar. Domain adaptation improved predictive ability in all comparisons. On the bigger ELSA dataset, sMLP attains a Brier score of 0.32 without domain adaptation, and 0.15 with domain adaptation, while dMLP attains 0.20 and 0.11, respectively. Thus, experimental results support the use of deep architectures in the prediction of functional decline, and of domain adaptation when data from another similar domain is available. These results may help improving the state of the art in predictive models for clinical practice and population screening.

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Acknowledgment

The data relative to ELSA were made available through the United Kingdom Data Archive - www.data-archive.ac.uk. ELSA was developed by a team of researchers based at the NatCen Social Research, University College London and the Institute for Fiscal Studies. The data were collected by NatCen Social Research. The funding is provided by the National Institute of Aging in the United States, and a consortium of United Kingdom government departments coordinated by the Office for National Statistics.

TILDA is an interinstitutional initiative led by Trinity College Dublin. TILDA data have been co-funded by the Government of Ireland through the Office of the Minister for Health and Children, by Atlantic Philanthropies, and by Irish Life; have been collected under the Statistics Act, 1993, of the Central Statistics Office. The project has been designed and implemented by the TILDA study team, Department of Health and Children. Copyright and all other intellectual property rights relating to the data are vested in TILDA. Ethical approval for each wave of data collection is granted by the Trinity College Research Ethics Committee. TILDA data is accessible for free from the following sites: Irish Social Science Data Archive at University College Dublin http://www.ucd.ie/issda/data/tilda/; Interuniversity Consortium for Political and Social Research at the University of Michigan (http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315).

The original data creators, depositors or copyright holders, the funders of the data collections and the archives of the datasets bear no responsibility for their further analysis or interpretation presented here.

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  1. Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

    Lorenzo Donati, Daniele Fongo, Luca Cattelani & Federico Chesani

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  1. Lorenzo Donati
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  2. Daniele Fongo
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Correspondence to Luca Cattelani .

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  1. University of Calabria, Rende, Italy

    Mario Alviano

  2. University of Calabria, Rende, Italy

    Gianluigi Greco

  3. University of Calabria, Rende, Italy

    Francesco Scarcello

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Donati, L., Fongo, D., Cattelani, L., Chesani, F. (2019). Prediction of Decline in Activities of Daily Living Through Deep Artificial Neural Networks and Domain Adaptation. In: Alviano, M., Greco, G., Scarcello, F. (eds) AI*IA 2019 – Advances in Artificial Intelligence. AI*IA 2019. Lecture Notes in Computer Science(), vol 11946. Springer, Cham. https://doi.org/10.1007/978-3-030-35166-3_27

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