A Large-Scale CNN Ensemble for Medication Safety Analysis
نویسندگان
چکیده
Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing drug surveillance, and data from health-related forums and medical communities can be of a great significance for estimating such effects. In this paper, we propose an end-to-end CNNbased method for predicting drug safety on user comments from healthcare discussion forums. We present an architecture that is based on a vast ensemble of CNNs with varied structural parameters, where the prediction is determined by the majority vote. To evaluate the performance of the proposed solution, we present a large-scale dataset collected from a medical website that consists of over 50 thousand reviews for more than 4000 drugs. The results demonstrate that our model significantly outperforms conventional approaches and predicts medicine safety with an accuracy of 87.17% for binary and 62.88% for multi-classification tasks.
منابع مشابه
Psychometric characteristics of nursing care complexity scale in medication errors, Iran
Twenty-first century challenges of nursing work is increasing complexity of care in the workplace. On the other word, medical errors is major challenge threaten for patient safety in all countries. The most common medical errors that identified are medication errors. With changing patterns of health services, the complexity increases in all workplaces. Since the medication administration is the...
متن کاملFacial Expression Recognition Using a Hybrid CNN-SIFT Aggregator
Recognizing facial expression has remained a challenging task in computer vision. Deriving an effective facial expression recognition is an important step for successful human-computer interaction systems. This paper describes a novel approach towards facial expression recognition task. It is motivated by the success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike ot...
متن کاملYedrouj-Net: An efficient CNN for spatial steganalysis
For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches. In this paper, we propose a CNN that outperforms the...
متن کاملImage Recognition Using Scale Recurrent Neural Networks
Convolutional Neural Network(CNN) has been widely used for image recognition with great success. However, there are a number of limitations of the current CNN based image recognition paradigm. First, the receptive field of CNN is generally fixed, which limits its recognition capacity when the input image is very large. Second, it lacks the computational scalability for dealing with images with ...
متن کاملInfyNLP at SMM4H Task 2: Stacked Ensemble of Shallow Convolutional Neural Networks for Identifying Personal Medication Intake from Twitter
This paper describes Infosys’s participation in the “2nd Social Media Mining for Health Applications Shared Task at AMIA, 2017, Task 2”. Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research. This task targets at developing automated classification models for identifying tweets containing descriptions of personal int...
متن کامل