Cargando…

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts...

Descripción completa

Detalles Bibliográficos
Autores principales:	Taewijit, Siriwon, Theeramunkong, Thanaruk, Ikeda, Mitsuru
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Hindawi 2017
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635478/ https://www.ncbi.nlm.nih.gov/pubmed/29090077 http://dx.doi.org/10.1155/2017/7575280

Ejemplares similares

Corrigendum to “Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records”
por: Taewijit, Siriwon, et al.
Publicado: (2018)

Effects of digital devices and online learning on computer vision syndrome in students during the COVID-19 era: an online questionnaire study
por: Seresirikachorn, Kasem, et al.
Publicado: (2022)

Categorization of adverse drug reactions in electronic health records
por: Foreman, Caroline, et al.
Publicado: (2020)

Characteristics of road traffic mortality and distribution of healthcare resources in Thailand
por: Seresirikachorn, Kasem, et al.
Publicado: (2022)

Distant supervision for medical concept normalization
por: Pattisapu, Nikhil, et al.
Publicado: (2020)

Factoid Question Answering with Distant Supervision
por: Zhang, Hongzhi, et al.
Publicado: (2018)

Distant Supervision for Extractive Question Summarization
por: Ishigaki, Tatsuya, et al.
Publicado: (2020)

Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris
por: Wanichwecharungruang, Boonsong, et al.
Publicado: (2021)

Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach
por: Chen, Jinying, et al.
Publicado: (2017)

Dose-Specific Adverse Drug Reaction Identification in Electronic Patient Records: Temporal Data Mining in an Inpatient Psychiatric Population
por: Eriksson, Robert, et al.
Publicado: (2014)

Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic
por: Seresirikachorn, Kasem, et al.
Publicado: (2023)

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records
por: Bean, Daniel M., et al.
Publicado: (2017)

Erratum to: Dose-Specific Adverse Drug Reaction Identification in Electronic Patient Records: Temporal Data Mining in an Inpatient Psychiatric Population
por: Eriksson, Robert, et al.
Publicado: (2014)

Literature mining of protein-residue associations with graph rules learned through distant supervision
por: Ravikumar, KE, et al.
Publicado: (2012)

Subsequence and distant supervision based active learning for relation extraction of Chinese medical texts
por: Ye, Qi, et al.
Publicado: (2023)

Accuracy of Adverse Drug Reaction Documentation upon Implementation of an Ambulatory Electronic Health Record System
por: Hui, Caleb, et al.
Publicado: (2016)

High-throughput framework for genetic analyses of adverse drug reactions using electronic health records
por: Zheng, Neil S., et al.
Publicado: (2021)

Identifying adverse drug reactions from free‐text electronic hospital health record notes
por: Wasylewicz, Arthur, et al.
Publicado: (2021)

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction
por: Gupta, Shashank, et al.
Publicado: (2018)

Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records
por: Wang, Ni, et al.
Publicado: (2021)

Extracting geographic locations from the literature for virus phylogeography using supervised and distant supervision methods
por: Weissenbacher, Davy, et al.
Publicado: (2017)

Author Correction: Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records
por: Bean, Daniel M., et al.
Publicado: (2018)

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)

Integrating information retrieval with distant supervision for Gene Ontology annotation
por: Zhu, Dongqing, et al.
Publicado: (2014)

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)

Analysis of Characteristics and Clinical Outcomes for Crisis Management during the Four Waves of the COVID-19 Pandemic
por: Amasiri, Watchara, et al.
Publicado: (2021)

Forecasting COVID-19 cases using time series modeling and association rule mining
por: Somyanonthanakul, Rachasak, et al.
Publicado: (2022)

Detection of unknown ototoxic adverse drug reactions: an electronic healthcare record-based longitudinal nationwide cohort analysis
por: Lee, Suehyun, et al.
Publicado: (2021)

Deep neural networks and distant supervision for geographic location mention extraction
por: Magge, Arjun, et al.
Publicado: (2018)

Using distant supervision to augment manually annotated data for relation extraction
por: Su, Peng, et al.
Publicado: (2019)

Weakly supervised temporal model for prediction of breast cancer distant recurrence
por: Sanyal, Josh, et al.
Publicado: (2021)

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

ML2 Supervised Machine Learning Predicts Mortality in COVID-19 Patients Using Electronic Health Records
por: Marinaro, X., et al.
Publicado: (2021)

Ontology-driven weak supervision for clinical entity classification in electronic health records
por: Fries, Jason A., et al.
Publicado: (2020)

Ontology-driven weak supervision for clinical entity classification in electronic health records
por: Fries, Jason A., et al.
Publicado: (2021)

Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods
por: Christopoulou, Fenia, et al.
Publicado: (2019)

Chemical-induced disease relation extraction via attention-based distant supervision
por: Gu, Jinghang, et al.
Publicado: (2019)

Adverse events recording in electronic health record systems in primary care
por: de Hoon, Sabine E. M., et al.
Publicado: (2017)

Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels
por: Tiftikci, Mert, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_gbq88slpk80qga088ajvfgfqkl