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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Rare Diseases
Last data update: Apr 19, 2024
. (Total: 63627 Documents since 2012)
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Harnessing Big Data in Amyotrophic Lateral Sclerosis: Machine Learning Applications for Clinical Practice and Pharmaceutical Trials.
Ee Ling Tan et al. J Integr Neurosci 2024 23(3) 58
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Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis.
Fabiano Papaiz et al. BMC Med Inform Decis Mak 2024 24(1) 80
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Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning.
Muzammil Arif Din Abdul Jabbar et al. Amyotroph Lateral Scler Frontotemporal Degener 2023 1-14
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At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis
AS Gupta et al, Nature Comm. August 21, 2023
PROSA-a multicenter prospective observational study to develop low-burden digital speech biomarkers in ALS and FTD.
Johannes Tröger et al. Amyotroph Lateral Scler Frontotemporal Degener 2023 1-10
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Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures
SA Johnson et al, NPJ Digital Medicine, March 6, 2023
A machine-learning based objective measure for ALS disease severity
FG Vieira et al, NPJ Digital Medicine, April 8, 2022
Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study.
Faghri Faraz et al. The Lancet. Digital health 2022
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A Clinical Decision Support System for the Prediction of Quality of Life in ALS.
Antoniadi Anna Markella et al. Journal of personalized medicine 2022 12(3)
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Evidence for generalizability of edaravone efficacy using a novel machine learning risk-based subgroup analysis tool.
Brooks Benjamin Rix et al. Amyotrophic lateral sclerosis & frontotemporal degeneration 2021 1-9
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Current and Future Projections of Amyotrophic Lateral Sclerosis in the United States Using Administrative Claims Data.
Miller Chris et al. Neuroepidemiology 2021 1-11
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Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques.
Tautan Alexandra-Maria et al. Artificial intelligence in medicine 2021 117102081
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Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.
Antoniadi Anna Markella et al. Scientific reports 2021 11(1) 12237
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Joint and interactive effects between health comorbidities and environmental exposures in predicting amyotrophic lateral sclerosis.
Bellavia Andrea et al. International journal of hygiene and environmental health 2020 Oct 231113655
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Evaluation of Vertical Ground Reaction Forces Pattern Visualization in Neurodegenerative Diseases Identification Using Deep Learning and Recurrence Plot Image Feature Extraction.
Lin Che-Wei et al. Sensors (Basel, Switzerland) 2020 Jul 20(14)
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Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study.
Antoniadi Anna Markella et al. BMJ open 2020 Feb 10(2) e033109
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Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective.
Gordon Jonathan et al. Journal of clinical medicine 2019 Oct 8(10)
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Multidisciplinary amyotrophic lateral sclerosis telemedicine care: The store and forward method.
Pulley Michael T et al. Muscle & nerve 2019 59(1) 34-39
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Adjusted Cost Analysis of Video Televisits for the Care of People with ALS.
Paganoni Sabrina et al. Muscle & nerve 2019 May
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Nutritional counseling with or without mobile health technology: a randomized open-label standard-of-care-controlled trial in ALS.
Wills Anne Marie et al. BMC neurology 2019 May 19(1) 104
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 19, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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