scholarly journals GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

Author(s):  
Junyuan Shang ◽  
Cao Xiao ◽  
Tengfei Ma ◽  
Hongyan Li ◽  
Jimeng Sun

Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.

2021 ◽  
Author(s):  
Riyad Elsaadi ◽  
Mahmoud Shafik

Healthcare and NHS faces many challenges in monitoring health conditions specially for patients with long term health conditions and the elderly. The forward view for healthcare providers and the NHS is moving from the hospital routine medical checks towards home environment care with the use of smart IoT and AI. Medication errors and missed medication across the globe, is the main source of harm to the public health. Technologies, specifically wireless health technologies are potential solutions for medication error and medication nonadherence in tracking patients’ medication. This paper presented a solution by developing a real-time wireless sensor network to monitor and check patient’s health condition using devices that transmits data from homes wirelessly to the relevant (caregiver, GP, Hospitals and specialist doctors). The proposed system benefits form the use of algorithms, which is used to provide the quality and quick health care advice to the patients at home. The algorithm processes the data from the database webserver. The database stores the patient health history conditions with all measurement obtained from the devices, such as blood pressure, blood glucose, heart rate and body temperature. This data is processed in machine learning algorithm to generate notifications for any changes occur in user’s health and by checking their history records. ML can detect patterns within patient healthcare records and inform clinicians of any anomalies.


2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.


SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A214-A214
Author(s):  
Yoav Nygate ◽  
Sam Rusk ◽  
Chris Fernandez ◽  
Nick Glattard ◽  
Jessica Arguelles ◽  
...  

Abstract Introduction Electroencephalogram (EEG) provides clinically relevant information for personalized patient health evaluation and comprehensive assessment of sleep. EEG-based indices have been associated with neurodegenerative conditions, psychiatric disorders, and metabolic and cardiovascular disease, and hold promise as a biomarker for brain health. Methods A deep neural network (DNN) model was trained to predict the age of patients using raw EEG signals recorded during clinical polysomnography (PSG). The DNN was trained on N=126,241 PSGs, validated on N=6,638, and tested on a holdout set of N=1,172. The holdout dataset included several categories of patient demographic and diagnostic parameters, allowing us to examine the association between brain age and a variety of medical conditions. Brain age was assessed by subtracting the individual’s chronological brain age from their EEG-predicted brain age (Brain Age Index; BAI), and then taking the absolute value of this variable (Absolute Brain Age Index; ABAI). We then constructed two regression models to test the relationship between BAI/ABAI and the following list of patient parameters: sex, BMI, depression, alcohol/drug problems, memory/concentration problems, epilepsy/seizures, diabetes, stroke, severe excessive daytime sleepiness (e.g., Epworth Sleepiness Scale ≥ 16; EDS), apnea-hypopnea index (AHI), arousal index (ArI), and sleep efficiency (SE). Results The DNN brain age model produced a mean absolute error of 4.604 and a Pearson’s r value of 0.933 which surpass the performance of prior research. In our regression analyses, we found a statistically significant relationship between the ABAI and: epilepsy and seizure disorders, stroke, elevated AHI, elevated ArI, and low SE (all p<0.05). This demonstrates these health conditions are associated with deviations of one’s predicted brain age from their chronological brain age. We also found patients with diabetes, depression, severe EDS, hypertension, and/or memory and concentration problems showed, on average, an elevated BAI compared to the healthy population sample (all p<0.05). Conclusion We show DNNs can accurately predict the brain age of healthy patients based on their raw, PSG derived, EEG recordings. Furthermore, we reveal indices, such as BAI and ABAI, display unique characteristics within different diseased populations, highlighting their potential value as novel diagnostic biomarker and potential “vital sign” of brain health. Support (if any):


Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Shushan Arakelyan ◽  
Sima Arasteh ◽  
Christophe Hauser ◽  
Erik Kline ◽  
Aram Galstyan

AbstractTackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks – functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results, and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).


2015 ◽  
Vol 719-720 ◽  
pp. 1177-1183
Author(s):  
Wei Zheng ◽  
Long Ye ◽  
Jing Ling Wang ◽  
Qin Zhang

Intra prediction is a key step in H.264/AVC to improve the coding performance with the idea that removing the directional redundancy among neighboring blocks. In order to cover more directional information existed in the image frames, there are usually many prediction modes can be selected in the state-of-the-art coding frameworks, but more bits are also needed to encode the prediction mode index information, then how to achieve the maximum overall bit-rate reduction became a problem. In this paper, 16 kinds of prediction modes are adopted by considering the direction information for 8x8 image blocks. Through calculating the bit-rate both for the mode index and residual image under different number of prediction modes, we obtain the most suitable prediction mode number relatively from the graphs. Experimental results show that, with the increase of prediction mode number, the residual information decreases obviously, and the sum of residual information and prediction mode index information also decreases but levels off after reaching a certain mode number, even has an obviously rising trend.


Author(s):  
Pengcheng Wang ◽  
Jonathan Rowe ◽  
Wookhee Min ◽  
Bradford Mott ◽  
James Lester

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.


2019 ◽  
Author(s):  
Assad Hayat ◽  
Brian J. Piper

AbstractAimsA substitution effect occurs when patients substitute Medical Cannabis (MC) for another drug. Over three-quarters (76.7%) of New England dispensary members reported reducing their use of opioids and two-fifths (42.0%) decreased their use of alcohol after starting MC (Piper et al. 2017). The objective of this exploratory study was to identify any factors which differentiate alcohol substituters from those that do not modify their alcohol use after starting MC (non-substituters).MethodsAmong dispensary patients (N=1,477), over two-thirds with chronic pain, that completed an online survey, 7.4% indicated that they regularly consumed alcohol. Comparisons were made to identify any demographic or health history characteristics which differentiated alcohol substituters (N=47) from non-substituters (N=65). Respondents selected from among a list of 37 diseases and health conditions (e.g. diabetes, sleep disorders) and the total number was calculated.ResultsSubstituters and non-substituters were indistinguishable in terms of sex, age, or prior drug history. Substituters were significantly more likely to be employed (68.1%) than non-substituters (51.1%). Substituters also reported having significantly more health conditions and diseases (3.3±2.0) than non-substituters (2.4±1.4).ConclusionsThis small study offers some insights into the profile of patients whose self-reported alcohol intake decreased following initiation of MC. Alcohol substituters had more other health conditions but also were more likely to be employed which may indicate that they fit a social drinker profile. Additional prospective or controlled research into the alcohol substitution effect following MC with a sample with more advanced alcohol misuse may be warranted.Short summaryA substitution effect with medical cannabis replacing prescription opioids has been reported but less is known for alcohol. This study evaluated characteristics which might differentiate alcohol substituters (N=47) from non-substituters (N=65) among dispensary members. Substituters were significantly more likely to be employed and have more health conditions than non-substituters.


2018 ◽  
Author(s):  
Marinka Zitnik ◽  
Monica Agrawal ◽  
Jure Leskovec

AbstractMotivation: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.Results: Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.Availability: Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.Contact:[email protected]


Author(s):  
Zhichao Huang ◽  
Xutao Li ◽  
Yunming Ye ◽  
Michael K. Ng

Graph Convolutional Networks (GCNs) have been extensively studied in recent years. Most of existing GCN approaches are designed for the homogenous graphs with a single type of relation. However, heterogeneous graphs of multiple types of relations are also ubiquitous and there is a lack of methodologies to tackle such graphs. Some previous studies address the issue by performing conventional GCN on each single relation and then blending their results. However, as the convolutional kernels neglect the correlations across relations, the strategy is sub-optimal. In this paper, we propose the Multi-Relational Graph Convolutional Network (MR-GCN) framework by developing a novel convolution operator on multi-relational graphs. In particular, our multi-dimension convolution operator extends the graph spectral analysis into the eigen-decomposition of a Laplacian tensor. And the eigen-decomposition is formulated with a generalized tensor product, which can correspond to any unitary transform instead of limited merely to Fourier transform. We conduct comprehensive experiments on four real-world multi-relational graphs to solve the semi-supervised node classification task, and the results show the superiority of MR-GCN against the state-of-the-art competitors.


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