Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm

Aviv Nahon; Boaz Lerner

Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm

Aviv Nahon, Boaz Lerner

Department of Industrial Engineering and Management

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

ALS is a neurodegenerative disease where factors such as disease progression rate and pattern vary greatly among patients. Since patient functionality deteriorates over time, we model ALS temporally to mimic the physician's reasoning by incorporating old with new information using a long-short term memory (LSTM) network. We demonstrate that the LSTM achieves a higher accuracy than a random forest in disease state prediction, and improves accuracy with data from additional clinic visits. Being an anytime predictor, our model can help physicians and caregivers to adjust patients' treatment and living environment along the disease period, improving patients' life quality.

Original language	American English
Title of host publication	ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages	609-614
Number of pages	6
ISBN (Electronic)	9782875870476
State	Published - 1 Jan 2018
Event	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 - Bruges, Belgium Duration: 25 Apr 2018 → 27 Apr 2018

Conference

Conference	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
Country/Territory	Belgium
City	Bruges
Period	25/04/18 → 27/04/18

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Information Systems

Cite this

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title = "Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm",

abstract = "ALS is a neurodegenerative disease where factors such as disease progression rate and pattern vary greatly among patients. Since patient functionality deteriorates over time, we model ALS temporally to mimic the physician's reasoning by incorporating old with new information using a long-short term memory (LSTM) network. We demonstrate that the LSTM achieves a higher accuracy than a random forest in disease state prediction, and improves accuracy with data from additional clinic visits. Being an anytime predictor, our model can help physicians and caregivers to adjust patients' treatment and living environment along the disease period, improving patients' life quality.",

author = "Aviv Nahon and Boaz Lerner",

note = "Publisher Copyright: {\textcopyright} ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.; 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 ; Conference date: 25-04-2018 Through 27-04-2018",

year = "2018",

month = jan,

day = "1",

language = "American English",

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booktitle = "ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

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Nahon, A & Lerner, B 2018, Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm. in ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 609-614, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018, Bruges, Belgium, 25/04/18.

TY - GEN

T1 - Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm

AU - Nahon, Aviv

AU - Lerner, Boaz

PY - 2018/1/1

Y1 - 2018/1/1

N2 - ALS is a neurodegenerative disease where factors such as disease progression rate and pattern vary greatly among patients. Since patient functionality deteriorates over time, we model ALS temporally to mimic the physician's reasoning by incorporating old with new information using a long-short term memory (LSTM) network. We demonstrate that the LSTM achieves a higher accuracy than a random forest in disease state prediction, and improves accuracy with data from additional clinic visits. Being an anytime predictor, our model can help physicians and caregivers to adjust patients' treatment and living environment along the disease period, improving patients' life quality.

AB - ALS is a neurodegenerative disease where factors such as disease progression rate and pattern vary greatly among patients. Since patient functionality deteriorates over time, we model ALS temporally to mimic the physician's reasoning by incorporating old with new information using a long-short term memory (LSTM) network. We demonstrate that the LSTM achieves a higher accuracy than a random forest in disease state prediction, and improves accuracy with data from additional clinic visits. Being an anytime predictor, our model can help physicians and caregivers to adjust patients' treatment and living environment along the disease period, improving patients' life quality.

UR - http://www.scopus.com/inward/record.url?scp=85069490335&partnerID=8YFLogxK

M3 - Conference contribution

SP - 609

EP - 614

BT - ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

T2 - 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018

Y2 - 25 April 2018 through 27 April 2018

ER -

Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm

Abstract

Conference

All Science Journal Classification (ASJC) codes

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