Cargando…

Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records

PURPOSE: Predictively diagnosing infectious diseases helps in providing better treatment and enhances the prevention and control of such diseases. This study uses actual data from a hospital. A multiple infectious disease diagnostic model (MIDDM) is designed for conducting multi-classification of in...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Mengying, Wei, Zhenhao, Jia, Mo, Chen, Lianzhong, Ji, Hong
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2022
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8848865/ https://www.ncbi.nlm.nih.gov/pubmed/35168624 http://dx.doi.org/10.1186/s12911-022-01776-y

Ejemplares similares

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

Intelligent virtual case learning system based on real medical records and natural language processing
por: Wang, Mengying, et al.
Publicado: (2022)

Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression
por: Geraci, Joseph, et al.
Publicado: (2017)

Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records
por: Brouwer, Emily S., et al.
Publicado: (2021)

Learning Signals of Adverse Drug-Drug Interactions from the Unstructured Text of Electronic Health Records
por: Iyer, Srinivasan V, et al.
Publicado: ( 201)

Learning Signals of Adverse Drug-Drug Interactions from the Unstructured Text of Electronic Health Records
por: Iyer, Srinivasan V, et al.
Publicado: (2013)

A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance
por: Lu, Hongxia, et al.
Publicado: (2022)

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records
por: Assale, Michela, et al.
Publicado: (2019)

Clinical assistant decision-making model of tuberculosis based on electronic health records
por: Wang, Mengying, et al.
Publicado: (2023)

Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system
por: Wang, Mengying, et al.
Publicado: (2023)

FasTag: Automatic text classification of unstructured medical narratives
por: Venkataraman, Guhan Ram, et al.
Publicado: (2020)

Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning
por: Wang, Zhuoran, et al.
Publicado: (2012)

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study
por: Kosowan, Leanne, et al.
Publicado: (2022)

Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records
por: González-Castro, Lorena, et al.
Publicado: (2023)

Deep learning approach to detection of colonoscopic information from unstructured reports
por: Seong, Donghyeong, et al.
Publicado: (2023)

Ascertainment of Aspirin Exposure Using Structured and Unstructured Large-scale Electronic Health Record Data
por: Bustamante, Ranier, et al.
Publicado: (2019)

Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records
por: Shao, Yijun, et al.
Publicado: (2019)

DeepSSR: a deep learning system for structured recognition of text images from unstructured paper-based medical reports
por: Liu, Hao, et al.
Publicado: (2022)

Combining structured and unstructured data for predictive models: a deep learning approach
por: Zhang, Dongdong, et al.
Publicado: (2020)

Deep learning to convert unstructured CT pulmonary angiography reports into structured reports
por: Spandorfer, Adam, et al.
Publicado: (2019)

Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method
por: Bright, Roselie A, et al.
Publicado: (2021)

A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study
por: Park, Jimyung, et al.
Publicado: (2021)

Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan
por: Ando, Kenichiro, et al.
Publicado: (2022)

Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study
por: Mitra, Avijit, et al.
Publicado: (2021)

Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance
por: Keller, Mikaela, et al.
Publicado: (2009)

A tree based approach for multi-class classification of surgical procedures using structured and unstructured data
por: Khaleghi, Tannaz, et al.
Publicado: (2021)

Categorizing Medications from Unstructured Clinical Notes
por: Farooq, Faisal, et al.
Publicado: (2013)

Identifying individual social risk factors using unstructured data in electronic health records and their relationship with adverse clinical outcomes
por: Rikard, S. Michaela, et al.
Publicado: (2022)

Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
Publicado: (2021)

Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
Publicado: (2021)

Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
por: Yu, Haiyan
Publicado: (2021)

Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
por: Antoniou, Mark
Publicado: (2021)

Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data
por: Hong, Na, et al.
Publicado: (2019)

Predicting decompression surgery by applying multimodal deep learning to patients’ structured and unstructured health data
por: Jujjavarapu, Chethan, et al.
Publicado: (2023)

Obstacle Classification and 3D Measurement in Unstructured Environments Based on ToF Cameras
por: Yu, Hongshan, et al.
Publicado: (2014)

Scalable and accurate deep learning with electronic health records
por: Rajkomar, Alvin, et al.
Publicado: (2018)

The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records
por: Shah, Anoop D, et al.
Publicado: (2012)

Development and Application of Pharmacological Statin-Associated Muscle Symptoms Phenotyping Algorithms Using Structured and Unstructured Electronic Health Records Data
por: Sun, Boguang, et al.
Publicado: (2023)

Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured electronic health records data
por: Sun, Boguang, et al.
Publicado: (2023)

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

Cannot write session to /tmp/vufind_sessions/sess_gsiktgkcpf5g82el5u9p2aq16j