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Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests

Narges Razavian, Jake Marcus, David A. Sontag
2016 Machine Learning in Health Care  
We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in clinical data, to predict disease onsets  ...  In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for 133 conditions based on 18 common  ...  Dropout: A simple way to prevent neural networks from overfitting. volume 15, for Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests pages 1929-1958.  ... 
dblp:conf/mlhc/RazavianMS16 fatcat:vedwq3dqlbbita6nz4lt3bgduy

Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [article]

Fernando Andreotti, Frank S. Heldt, Basel Abu-Jamous, Ming Li, Avelino Javer, Oliver Carr, Stojan Jovanovic, Nadezda Lipunova, Benjamin Irving, Rabia T. Khan, Robert Dürichen
2020 arXiv   pre-print
In this work, we propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records (EHRs) at different time horizons.  ...  Our results indicate that the recurrent neural network approach benefits from the hospital longitudinal information and demonstrates how machine learning techniques can be applied to secondary care.  ...  Acknowledgments This work uses data provided by patients and collected by the NHS as part of their care and support.  ... 
arXiv:2007.08491v1 fatcat:m624vzlntzbulpnkcd47mgo3ne

Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests [article]

Narges Razavian, Jake Marcus, David Sontag
2016 arXiv   pre-print
We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in clinical data, to predict disease onsets  ...  In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for 133 conditions based on 18 common  ...  Supplementary Materials for Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests Cross-validation results For the convolutional models, we set the number of filters to be 64 for all the  ... 
arXiv:1608.00647v3 fatcat:dkjs4fjokjce3ja7u4olotwuri

Biomarkers for Huntington's disease: an update

Rachael I Scahill, Ed J Wild, Sarah J Tabrizi
2012 Expert Opinion in Medical Diagnostics  
UCLH/UCL receives a proportion of its funding from the Department of Health's NIHR Biomedical Research Centres funding scheme.  ...  the UK Dementia and Neurodegenerative Diseases Network.  ...  Future directions Neuroimaging It is clear from studies such as TRACK-HD and PREDICT-HD that there are striking neuropathological changes up to 15 years prior to the onset of manifest disease [2, 3]  ... 
doi:10.1517/17530059.2012.701205 pmid:23480802 fatcat:a7rnrit75nhyxc4o5reymqptha

Quantitative longitudinal predictions of Alzheimer's disease by multi-modal predictive learning [article]

Mithilesh Prakash, Mahmoud Abdelaziz, Linda Zhang, Bryan A Strange, Jussi Tohka
2020 bioRxiv   pre-print
We hypothesize that multi-modal data and predictive learning models can be employed for longitudinally predicting ADAS-cog scores.  ...  Quantitatively predicting the progression of Alzheimers disease (AD) in an individual on a continuous scale, such as AD assessment scale-cognitive (ADAS-cog) scores, is informative for a personalized approach  ...  (8193, 6227) as well as The Academy of Finland (grant 316258 to JT).  ... 
doi:10.1101/2020.06.04.133645 fatcat:vjprnlyp7nfxtet6xecaljzwxa

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Cao Xiao, Edward Choi, Jimeng Sun
2018 JAMIA Journal of the American Medical Informatics Association  
Results: We surveyed and analyzed multiple aspects of the 98 articles we found and identified the following analytics tasks: disease detection/classification, sequential prediction of clinical events,  ...  We summarize them according to these axes: types of analytics tasks, types of deep learning model architectures, special challenges arising from health data and tasks and their potential solutions, as  ...  In the reviewed articles, some were conducted to predict the future onset of a new disease condition such as heart failure (HF) onset prediction using RNN on longitudinal outpatient data from Sutter Health  ... 
doi:10.1093/jamia/ocy068 pmid:29893864 fatcat:ne7weiw7xvc2lp7hfgkzltdnri

Plasma Biomarkers of AD Emerging as Essential Tools for Drug Development: An EU/US CTAD Task Force Report

R.J. Bateman, K. Blennow, R. Doody, S. Hendrix, S. Lovestone, S. Salloway, R. Schindler, M. Weiner, H. Zetterberg, P. Aisen, B. Vellas, And The EU/US CTAD Task Force
2019 The journal of prevention of Alzheimer's disease  
The European Union-North American Clinical Trials in Alzheimer's Disease Task Force (EU/US CTAD Task Force) discussed the current status of blood-based AD biomarker development at its 2018 annual meeting  ...  Blood samples analyzed using -omics and other approaches are also in development and may provide important insight into disease mechanisms as well as biomarker profiles for disease prediction.  ...  Recognizing the urgency of advancing the development of blood-based biomarkers for AD, the European Union-North American Clinical Trials in Alzheimer's Disease Task Force (EU/US CTAD Task Force) addressed  ... 
doi:10.14283/jpad.2019.21 pmid:31062827 fatcat:eoifmy5ruzc5piw6kcn244mfky

A Gamified Assessment Platform for Predicting the Risk of Dementia +Parkinson's disease (DPD) Co-Morbidity

Zhiwei Zeng, Hongchao Jiang, Yanci Zhang, Zhiqi Shen, Jun Ji, Martin J. Mckeown, Jing Jih Chin, Cyril Leung, Chunyan Miao
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
In this paper, we demonstrate a novel AI-powered solution to provide early detection of the onset of Dementia + Parkinson's disease (DPD) co-morbidity, a condition which severely limits a senior's ability  ...  We investigate useful in-game behaviour markers which can support machine learning-based predictive analytics on seniors' risk of developing DPD co-morbidity.  ...  The diagnosis of PD and dementia is usually based on multi-source data, including laboratory, clinical and behavioral data, and requires the knowledge and opinions from multiple healthcare professionals  ... 
doi:10.24963/ijcai.2020/775 dblp:conf/ijcai/ZengJZSJMCLM20 fatcat:wnuzdnj7fvcn5kqtaul65xd3vm

Biological and clinical manifestations of Huntington's disease in the longitudinal TRACK-HD study: cross-sectional analysis of baseline data

Sarah J Tabrizi, Douglas R Langbehn, Blair R Leavitt, Raymund AC Roos, Alexandra Durr, David Craufurd, Christopher Kennard, Stephen L Hicks, Nick C Fox, Rachael I Scahill, Beth Borowsky, Allan J Tobin (+5 others)
2009 Lancet Neurology  
Many parameters differ from age-matched controls in a graded fashion and show changes of increasing magnitude across our cohort, who range from about 16 years from predicted disease diagnosis to early  ...  Interpretation-We show the feasibility of rapid data acquisition and the use of multi-site 3T MRI and neurophysiological motor measures in a large multicentre study.  ...  of Neuro Imaging UCLA (LONI) and IXICO for all their help in enabling all aspects of TRACK-HD to move forward, and also to Ray Young for graphics and the PREDICT-HD investigators for helpful advice.  ... 
doi:10.1016/s1474-4422(09)70170-x pmid:19646924 pmcid:PMC3725974 fatcat:2dgun6nv7rbijkpvlktzx7dkka

Machine Learning Prediction of Parkinson's Disease Onset and Subtype Using Germline Variants [article]

Saya R Dennis, Tanya Simuni, Yuan Luo
2021 medRxiv   pre-print
Our best models achieved an ROC of 0.77 and 0.61 for disease onset and subtype prediction, respectively.  ...  In our study, we develop two machine learning models that predict the onset as well as the progression subtype of Parkinson's Disease based on subjects' germline mutations.  ...  The database includes genomic sequences, longitudinal clinical test results, and biospecimen data from PD patients as well as healthy controls from multiple, multi-institute studies.  ... 
doi:10.1101/2021.06.14.21258631 fatcat:dxgtbq3rjnabzcedv4hwopc3uy

Cognitive Reserve and Brain Reserve in Prodromal Huntington's Disease

Aaron Bonner-Jackson, Jeffrey D. Long, Holly Westervelt, Geoffrey Tremont, Elizabeth Aylward, Jane S. Paulsen
2013 Journal of the International Neuropsychological Society  
This relationship was not observed among those estimated to be further from motor disease onset.  ...  , putamen) for those estimated to be closest to motor disease onset.  ...  Acknowledgments We thank the PREDICT-HD sites, the study participants, and the National Research Roster for Huntington Disease Patients and Families.  ... 
doi:10.1017/s1355617713000507 pmid:23702309 pmcid:PMC3720793 fatcat:ssuaaswc4neevddksolj5b2r6q

Predicting COVID-19 malignant progression with AI techniques [article]

Xiang Bai, Cong Fang, Yu Zhou, Song Bai, Zaiyi Liu, Qianlan Chen, Yongchao Xu, Tian Xia, Shi Gong, Xudong Xie, Dejia Song, Ronghui Du (+8 others)
2020 medRxiv   pre-print
The clinical and laboratory data at admission, the first CT, and the follow-up CT at severe/critical stage of the two groups were compared with Chi-square test, Fisher's exact test, and t test.  ...  Methods: A total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved  ...  task, which attempts to learn high-level hierarchical features from mass data, and expands the search space of the features for specific tasks.  ... 
doi:10.1101/2020.03.20.20037325 fatcat:smtun2wdizhetb4gff424rnhci

Is Mild Cognitive Impairment Prodromal for Vascular Dementia Like Alzheimer's Disease?

John Stirling Meyer, Gelin Xu, John Thornby, Munir H. Chowdhury, Minh Quach
2002 Stroke  
and Purpose-Individuals with mild cognitive impairment (MCI) are at increased risk of Alzheimer's disease (AD) and probably other forms of dementia.  ...  The remaining 12 patients with VaD (44.4%) were diagnosed directly from a cognitively normal status without preceding MCI.  ...  Some support was provided from a collaborative clinical trial of galanthamine versus placebo in the treatment of VaD by Janssen Pharmaceuticals after the present study was completed without any known conflict  ... 
doi:10.1161/01.str.0000024432.34557.10 pmid:12154249 fatcat:z6flcch6mrcmffuhkasr245kfq

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

Gorka Lasso, Saad Khan, Stephanie A. Allen, Margarette Mariano, Catalina Florez, Erika P. Orner, Jose A. Quiroz, Gregory Quevedo, Aldo Massimi, Aditi Hegde, Ariel S. Wirchnianski, Robert H. Bortz (+21 others)
2022 PLoS Computational Biology  
Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality  ...  the importance of contextualizing clinical parameters according to their time post-symptom onset.  ...  Unlike admission clinical and laboratory values, longitudinal information mirroring the evolution of disease during hospitalization might be better suited to predict disease outcome far downstream [28  ... 
doi:10.1371/journal.pcbi.1009778 pmid:35041647 pmcid:PMC8812869 fatcat:swotl62r7nejleiqyc7ozjhobu

Unsupervised pre-training of graph transformers on patient population graphs [article]

Chantal Pellegrini, Nassir Navab, Anees Kazi
2022 arXiv   pre-print
In this paper, we propose novel unsupervised pre-training techniques designed for heterogeneous, multi-modal clinical data for patient outcome prediction inspired by masked language modeling (MLM), by  ...  An immense amount of clinical records are recorded, but still, data and labels can be scarce for data collected in small hospitals or dealing with rare diseases.  ...  It includes longitudinal EHR data, including vital signs and laboratory values, from patient stays in two different hospitals, as well as the patient's demographics.  ... 
arXiv:2207.10603v1 fatcat:zvrspnxwynd5ncjlg3kh6v27ki
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