Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

BACKGROUND: Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs. OBJECTIVE: We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps. METHODS: We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively. RESULTS: Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance. CONCLUSIONS: Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning

The Influence of Fixed and Moving NPC on Pedestrians’ Avoidance Behaviors: VR-Based Experiments

Pedestrians have to take actions when crossing other pedestrians to avoid collisions. In this work, we focus on the differences of avoidance behaviors when a pedestrian crosses a moving and fixed intruder (NPC) in the virtual environment. The avoidance process is divided into three stages using the start avoidance point and maximum lateral deviation point. In moving NPC experiments, the distance from start avoidance point to the potential collision point (CP) first decreases and then increases as the intrusion angle increases. In standing NPC experiments, pedestrians start avoidance closer to the CP (average distance: 3.73m). In moving NPC experiments, the average maximum lateral offset distance (MLD) for the pedestrians to detour decreases with the intrusion angles decreases (Behind MLD ∈[1.09 m, 1.94 m], Front MLD ∈[1.13 m, 1.56 m]). In standing NPC experiments, the average MLD is 1.01m (left: 1.04m, right: 0.98m), which is the closest to the MLD of pedestrians at 180° intrusion angles. What’s more, at 30°, 60°, 90° and 120° intrusion angles, pedestrians avoiding behind the NPC require higher MLD than others avoiding in front of the NPC. Thus, more subjects prefer to avoid in front of the NPC under these conditions (88%, 86%, 78%, 69% of all). But the preference weakens and disappears at 150° and 180° intrusion angles due to the decrease of MLD. In standing NPC experiments, significant left-right preference is not found in pedestrians’ avoidance strategies (right: 46%, left: 54%). This article quantitatively analyses the difference between the influence of fixed and movement NPC on pedestrians’ avoidance strategies. The mechanism of pedestrian’s avoidance behavior is obtained by analyzing characteristic parameters, which is helpful to adjust pedestrian avoidance prediction models and design humanoid robots

Reinforcement Learning for Self-exploration in Narrow Spaces

In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises. Additionally, it is disabled when mapless. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a map while avoiding collisions. Specifically, based on our Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we propose the rectangular safety region to represent states and detect collisions for rectangular-shaped robots, and a carefully crafted reward function for reinforcement learning that does not require the destination information. Then we benchmark five reinforcement learning algorithms including DDPG, DQN, SAC, PPO, and PPO-discrete, in a simulated narrow track. After training, the well-performed DDPG and DQN models can be transferred to three brand new simulated tracks, and furthermore to three real-world tracks