Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

Lü, Chang; Reddy, Chandan K.; Ning, Yue

doi:10.1109/tcyb.2021.3109881

Cited by 14 publications

(10 citation statements)

References 33 publications

(45 reference statements)

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“…However, from Fig. 4, we can see that the generated disease types of MTGAN stabilize at a high number and are close to the real number of disease types in MIMIC-III (4,880) and MIMIC-IV (6,102). Furthermore, even though MTGAN is also based on the Wasserstein distance, the average disease number per visit does not dramatically drop, but gradually decreases to the real data.…”

Section: Analysis Of Gan Trainingmentioning

confidence: 68%

“…T HE application of electronic health records (EHR) in healthcare facilities not only automates access to key clinical information of patients, but also provides valuable data resources for researchers. To analyze EHR data, deep learning has achieved great success on various tasks, including representation learning for patients and medical concepts [1], [2], [3], predicting health events such as diagnoses and mortality [4], [5], [6], [7], [8], clinical note analysis [9], privacy protection [10], [11], and phenotyping [12], [13], [14]. Although EHR data are significant in healthcare applications, it is typically arduous for researchers to access them.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Label Clinical Time-Series Generation via Conditional GAN

Lu¹,

Reddy²,

Wang³

et al. 2022

Preprint

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With wide applications of electronic health records (EHR), deep learning methods have been adopted to analyze EHR data on various tasks such as representation learning, clinical event prediction, and phenotyping. However, due to privacy constraints, limited access to EHR becomes a bottleneck for deep learning research. Recently, generative adversarial networks (GANs) have been successful in generating EHR data. However, there are still challenges in high-quality EHR generation, including generating time-series EHR and uncommon diseases given imbalanced datasets. In this work, we propose a Multi-label Time-series GAN (MTGAN) to generate EHR data and simultaneously improve the quality of uncommon disease generation. The generator of MTGAN uses a gated recurrent unit (GRU) with a smooth conditional matrix to generate sequences and uncommon diseases. The critic gives scores using Wasserstein distance to recognize real samples from synthetic samples by considering both data and temporal features. We also propose a training strategy to calculate temporal features for real data and stabilize GAN training. Furthermore, we design multiple statistical metrics and prediction tasks to evaluate the generated data. Experimental results demonstrate the quality of the synthetic data and the effectiveness of MTGAN in generating realistic sequential EHR data, especially for uncommon diseases.

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Section: Analysis Of Gan Trainingmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Multi-Label Clinical Time-Series Generation via Conditional GAN

Lu¹,

Reddy²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Through continuous multi-sensor data collected from patients, electronic health records (EHR) can be modeled using graph-based methods and representation learning [ 150 ]. Learning meaningful medical ontology representations within the EHR database can alleviate the data insufficiency problem, and the learned embeddings can cluster nicely into particular groups of diseases [ 151 ]. With simulation and motion data synthesis, digital twin models can be established for patient performance estimation and rehabilitation program planning to individual needs [ 152 , 153 ].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Yerebakan

Dai

et al. 2022

Healthcare

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With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

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“…3) Healthcare representation and digital twin Through continuous multi-sensor data collected from patients, electronic health records (EHR) can be modeled using graph-based methods and representation learning [149]. Learning meaningful medical ontology representations within the EHR database can alleviate the data insufficiency problem and the learned embeddings can cluster nicely into particular groups of diseases [150]. With simulation and motion data synthesis, digital twin models can be established for patient performance estimation and rehabilitation program planning to individual needs [151], [152].…”

Section: B Data Processing Methodsmentioning

confidence: 99%

IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review

Fan¹,

Yerebakan²,

Dai³

et al. 2022

Preprint

View full text Add to dashboard Cite

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnosis carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement units (IMUs) based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML) based techniques have been proposed to smartly map IMU captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. To help readers comprehensively understand the fundamentals and state-of-the-art techniques, this article systematically reviews this field, by introducing and explaining relevant application scenarios, discussing challenges, and predicting foreseeable future trends. <br>

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Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

Cited by 14 publications

References 33 publications

Multi-Label Clinical Time-Series Generation via Conditional GAN

Multi-Label Clinical Time-Series Generation via Conditional GAN

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review

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