An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction

Dong, Xinyu; Wong, Rachel; Lyu, Weimin; Abell-Hart, Kayley; Deng, Jianyuan; Liu, Yinan; Hajagos, Janos; Rosenthal, Richard N.; Chen, Chao; Wang, Fusheng

doi:10.1016/j.artmed.2022.102439

Cited by 17 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most related are those studies that leveraged language data from social media, sometimes examining counts of opioid-related words (e.g. fentanyl) and use rates 15 and increasingly using more sophisticated AI-based or machine learning methods, to predict opioid use and outcome rates 16,17 . Many of these studies are focused on specific regions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Opioid death projections with AI-based forecasts using social media language

et al. 2023

View full text Add to dashboard Cite

Targeting of location-specific aid for the U.S. opioid epidemic is difficult due to our inability to accurately predict changes in opioid mortality across heterogeneous communities. AI-based language analyses, having recently shown promise in cross-sectional (between-community) well-being assessments, may offer a way to more accurately longitudinally predict community-level overdose mortality. Here, we develop and evaluate, TrOP (Transformer for Opiod Prediction), a model for community-specific trend projection that uses community-specific social media language along with past opioid-related mortality data to predict future changes in opioid-related deaths. TOP builds on recent advances in sequence modeling, namely transformer networks, to use changes in yearly language on Twitter and past mortality to project the following year’s mortality rates by county. Trained over five years and evaluated over the next two years TrOP demonstrated state-of-the-art accuracy in predicting future county-specific opioid trends. A model built using linear auto-regression and traditional socioeconomic data gave 7% error (MAPE) or within 2.93 deaths per 100,000 people on average; our proposed architecture was able to forecast yearly death rates with less than half that error: 3% MAPE and within 1.15 per 100,000 people.

show abstract

Section: Introductionmentioning

confidence: 99%

Opioid death projections with AI-based forecasts using social media language

et al. 2023

View full text Add to dashboard Cite

show abstract

“…It was found that recent papers can be classified into three types of databases, namely clinical [13], [18], [27], [33], registry [10], [12], [20], [24], [25], [29], [34], and knowledge [14], [15], [16], [17], [19], [21], [22], [23], [26], [28], [30], [31], [32], [35]. Additionally, based on review papers of existing XAI models [48], [49], the XAI algorithms used were divided into the following four categories: surrogate The main advantage of an explanatory technique such as SHAP is that it has solid roots in game theory, which ensures that the explanation of a prediction instance is fairly distributed across the features.…”

Section: ) Xai Methodsmentioning

confidence: 99%

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

et al. 2023

View full text Add to dashboard Cite

Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this research is to identify studies in the field of pharmacovigilance using XAI. Though there have been many previous attempts to select papers, with a total of 781 papers being confirmed, only 25 of them manually met the selection criteria. This study presents an intuitive review of the potential of XAI technologies in the field of pharmacovigilance. In the included studies, clinical data, registry data, and knowledge data were used to investigate drug treatment, side effects, and interaction studies based on tree models, neural network models, and graph models. Finally, key challenges for several research issues for the use of XAI in pharmacovigilance were identified. Although artificial intelligence (AI) is actively used in drug surveillance and patient safety, gathering adverse drug reaction information, extracting drug-drug interactions, and predicting effects, XAI is not normally utilized. Therefore, the potential challenges involved in its use alongside future prospects should be continuously discussed.

show abstract

“…Graph neural networks (GNNs) are well-suited for processing non-Euclidean data [59][60][61]. There are several kinds of GNNs [62][63][64][65][66][67][68][69]: Recurrent graph neural networks, convolutional graph neural networks, graph attention networks and so on.…”

Section: Literature Review 1pedestrian Trajectory Predictionmentioning

confidence: 99%

InfoSTGCAN: An Information-Maximizing Spatial-Temporal Graph Convolutional Attention Network for Heterogeneous Human Trajectory Prediction

Ruan,

2024

Computers

View full text Add to dashboard Cite

Predicting the future trajectories of multiple interacting pedestrians within a scene has increasingly gained importance in various fields, e.g., autonomous driving, human–robot interaction, and so on. The complexity of this problem is heightened due to the social dynamics among different pedestrians and their heterogeneous implicit preferences. In this paper, we present Information Maximizing Spatial-Temporal Graph Convolutional Attention Network (InfoSTGCAN), which takes into account both pedestrian interactions and heterogeneous behavior choice modeling. To effectively capture the complex interactions among pedestrians, we integrate spatial-temporal graph convolution and spatial-temporal graph attention. For grasping the heterogeneity in pedestrians’ behavior choices, our model goes a step further by learning to predict an individual-level latent code for each pedestrian. Each latent code represents a distinct pattern of movement choice. Finally, based on the observed historical trajectory and the learned latent code, the proposed method is trained to cover the ground-truth future trajectory of this pedestrian with a bi-variate Gaussian distribution. We evaluate the proposed method through a comprehensive list of experiments and demonstrate that our method outperforms all baseline methods on the commonly used metrics, Average Displacement Error and Final Displacement Error. Notably, visualizations of the generated trajectories reveal our method’s capacity to handle different scenarios.

show abstract

An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction

Cited by 17 publications

References 33 publications

Opioid death projections with AI-based forecasts using social media language

Opioid death projections with AI-based forecasts using social media language

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

InfoSTGCAN: An Information-Maximizing Spatial-Temporal Graph Convolutional Attention Network for Heterogeneous Human Trajectory Prediction

Contact Info

Product

Resources

About