Cargando…

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature...

Descripción completa

Detalles Bibliográficos
Autores principales:	Miotto, Riccardo, Li, Li, Kidd, Brian A., Dudley, Joel T.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group 2016
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4869115/ https://www.ncbi.nlm.nih.gov/pubmed/27185194 http://dx.doi.org/10.1038/srep26094

Ejemplares similares

Deep representation learning of electronic health records to unlock patient stratification at scale
por: Landi, Isotta, et al.
Publicado: (2020)

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records
por: De Freitas, Jessica K., et al.
Publicado: (2021)

The application of unsupervised deep learning in predictive models using electronic health records
por: Wang, Lei, et al.
Publicado: (2020)

Predicting hypertension onset from longitudinal electronic health records with deep learning
por: Datta, Suparno, et al.
Publicado: (2022)

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection
por: Li, Shicheng, et al.
Publicado: (2021)

Reflecting health: smart mirrors for personalized medicine
por: Miotto, Riccardo, et al.
Publicado: (2018)

Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation
por: Khaligh-Razavi, Seyed-Mahdi, et al.
Publicado: (2014)

The Rapid Prediction of Carbapenem Resistance in Patients With Klebsiella pneumoniae Bacteremia Using Electronic Medical Record Data
por: Sullivan, Timothy, et al.
Publicado: (2018)

Unsupervised Facial Action Representation Learning by Temporal Prediction
por: Wang, Chongwen, et al.
Publicado: (2022)

Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records
por: Berge, Geir Thore, et al.
Publicado: (2023)

Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials
por: Miotto, Riccardo, et al.
Publicado: (2015)

Representation learning for clinical time series prediction tasks in electronic health records
por: Ruan, Tong, et al.
Publicado: (2019)

Unsupervised Representation Learning for Proteochemometric Modeling
por: Kim, Paul T., et al.
Publicado: (2021)

Lyme Disease Patient Trajectories Learned from Electronic Medical Data for Stratification of Disease Risk and Therapeutic Response
por: Ichikawa, Osamu, et al.
Publicado: (2019)

Formal representation of patients’ care context data: the path to improving the electronic health record
por: Colicchio, Tiago K, et al.
Publicado: (2020)

Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
por: Nagamine, Tasha, et al.
Publicado: (2020)

Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning
por: Lekschas, Fritz, et al.
Publicado: (2020)

Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs
por: Noble, Peter-John Mäntylä, et al.
Publicado: (2021)

Unsupervised anomaly detection of implausible electronic health records: a real-world evaluation in cancer registries
por: Röchner, Philipp, et al.
Publicado: (2023)

Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases
por: Anetta, Kristof, et al.
Publicado: (2022)

Desiderata for computable representations of electronic health records-driven phenotype algorithms
por: Mo, Huan, et al.
Publicado: (2015)

Multi-layer Representation Learning and Its Application to Electronic Health Records
por: Yang, Shan, et al.
Publicado: (2021)

Learning and visualizing chronic latent representations using electronic health records
por: Chushig-Muzo, David, et al.
Publicado: (2022)

Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics
por: Kidd, Brian, et al.
Publicado: (2023)

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model
por: Glicksberg, Benjamin S, et al.
Publicado: (2019)

Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients’ narratives
por: Grissette, Hanane, et al.
Publicado: (2021)

Learning unsupervised contextual representations for medical synonym discovery
por: Schumacher, Elliot, et al.
Publicado: (2019)

Spatial position constraint for unsupervised learning of speech representations
por: Humayun, Mohammad Ali, et al.
Publicado: (2021)

Unsupervised classification of plethysmography signals with advanced visual representations
por: Germain, Thibaut, et al.
Publicado: (2023)

Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases
por: Yun, Taedong, et al.
Publicado: (2023)

Identifying and evaluating clinical subtypes of Alzheimer’s disease in care electronic health records using unsupervised machine learning
por: Alexander, Nonie, et al.
Publicado: (2021)

Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records
por: Bolt, Hikaru, et al.
Publicado: (2023)

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19
por: Vaid, Akhil, et al.
Publicado: (2020)

Refining electronic medical records representation in manifold subspace
por: Wang, Bolin, et al.
Publicado: (2022)

SCOPE: predicting future diagnoses in office visits using electronic health records
por: Mukherjee, Pritam, et al.
Publicado: (2023)

Treatment effect prediction with adversarial deep learning using electronic health records
por: Chu, Jiebin, et al.
Publicado: (2020)

Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations
por: Weegar, Rebecka, et al.
Publicado: (2020)

An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records
por: Koshman, Varvara, et al.
Publicado: (2022)

Unsupervised generative and graph representation learning for modelling cell differentiation
por: Bica, Ioana, et al.
Publicado: (2020)

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature
por: Frisoni, Giacomo, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_q1oad1uk94onncu0ju7ea38c93