Overview of the first natural language processing challenge for extracting medication, indication, and adverse drug events from electronic health record notes (MADE …
Introduction This work describes the Medication and Adverse Drug Events from Electronic
Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 …
Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 …
Detecting adverse drug events with rapidly trained classification models
Introduction Identifying occurrences of medication side effects and adverse drug events
(ADEs) is an important and challenging task because they are frequently only mentioned in …
(ADEs) is an important and challenging task because they are frequently only mentioned in …
A long short-term memory-based framework for crash detection on freeways with traffic data of different temporal resolutions
Traffic crash detection is a major component of intelligent transportation systems. It can
explore inner relationships between traffic conditions and crash risk, prevent potential …
explore inner relationships between traffic conditions and crash risk, prevent potential …
Adverse drug event detection from electronic health records using hierarchical recurrent neural networks with dual-level embedding
Introduction Adverse drug event (ADE) detection is a vital step towards effective
pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs …
pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs …
Spatial and temporal prediction of secondary crashes combining stacked sparse auto-encoder and long short-term memory
H Li, Q Gao, Z Zhang, Y Zhang, G Ren - Accident Analysis & Prevention, 2023 - Elsevier
Secondary crashes occur within the spatial and temporal impact area of primary crashes,
resulting in traffic delays and safety problems. While most existing studies focus on the …
resulting in traffic delays and safety problems. While most existing studies focus on the …
An ensemble of neural models for nested adverse drug events and medication extraction with subwords
Objective This article describes an ensembling system to automatically extract adverse drug
events and drug related entities from clinical narratives, which was developed for the 2018 …
events and drug related entities from clinical narratives, which was developed for the 2018 …
[PDF][PDF] Electronic medical records and machine learning in approaches to drug development
A Shinozaki - Artificial intelligence in Oncology drug discovery and …, 2020 - library.oapen.org
Electronic medical records (EMRs) were primarily introduced as a digital health tool in
hospitals to improve patient care, but over the past decade, research works have …
hospitals to improve patient care, but over the past decade, research works have …
[HTML][HTML] Medication event extraction in clinical notes: Contribution of the WisPerMed team to the n2c2 2022 challenge
In this work, we describe the findings of the 'WisPerMed'team from their participation in Track
1 (Contextualized Medication Event Extraction) of the n2c2 2022 challenge. We tackle two …
1 (Contextualized Medication Event Extraction) of the n2c2 2022 challenge. We tackle two …
[HTML][HTML] Identification of pediatric respiratory diseases using a fine-grained diagnosis system
Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract
infection (RTI), are among the most common diseases in clinics. The similarities among the …
infection (RTI), are among the most common diseases in clinics. The similarities among the …
[HTML][HTML] Natural language processing-based quantification of the mental state of psychiatric patients
SS Mukherjee, J Yu, Y Won, MJ McClay… - Computational …, 2020 - cpsyjournal.org
Psychiatric practice routinely uses semistructured and/or unstructured free text to record the
behavior and mental state of patients. Many of these data are unstructured, lack …
behavior and mental state of patients. Many of these data are unstructured, lack …