Temporal Pattern and Association Discovery of Diagnosis Codes Using Deep Learning

Mehrabi, Saeed; Sohn, Sunghwan; Li, Dingheng; Pankratz, Joshua J.; Therneau, Terry M.; Sauver, Jennifer L. St.; Liu, Hongfang; Palakal, Mathew

doi:10.1109/ichi.2015.58

Cited by 39 publications

(31 citation statements)

References 26 publications

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“…The reason for using RNNs is that their ability to memorize sequential events could improve the modeling of the varying time delays between the onsets of emergency clinical events, such as respiratory distress and asthma attack and the appearance of symptoms. In a related study, Mehrabi et al [104] proposed the use DBN to discover common temporal patterns and characterize disease progression. The authors highlighted that the ability to discern and interpret the newly discovered patterns requires further investigation.…”

Section: Medical Informaticsmentioning

confidence: 99%

Deep Learning for Health Informatics

Ravì

Wong

Deligianni

et al. 2017

IEEE J. Biomed. Health Inform.

1,563

776

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With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.

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Section: Medical Informaticsmentioning

confidence: 99%

Deep Learning for Health Informatics

Ravì

Wong

Deligianni

et al. 2017

IEEE J. Biomed. Health Inform.

1,563

776

View full text Add to dashboard Cite

show abstract

“…One of the main rich sources of patient information are electronic health records (EHR), which include medical history details such laboratory test results, allergies, radiology images, sensors multivariate times series (such as EEG), medications and treatment plans [7]. The analysis of such clinical information against temporal dimensions provides a valuable opportunity for deep learning in healthcare decision making [96], developing knowledge-distillation approach [97], for temporal pattern discovery over Rochester epidemiology project data [98], or to classify diagnoses given multivariate pediatric intensive care unit (PICU) time series [99]. A novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitate clinical predictive modeling was developed by Lipton et al [99].…”

Section: Public and Medical Health Managementmentioning

confidence: 99%

Deep Learning in the Biomedical Applications: Recent and Future Status

2019

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Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

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“…In this paper, the longitudinal records of each patient are expressed as diagnostic matrices. Deep learning algorithm is used to discover conventional patterns between patients, providing new ideas for discovering new potential correlations and generating new hypotheses [50].…”

Section: Deep Belief Networkmentioning

confidence: 99%

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Sun

Zhang

2019

Teh. vjesn.

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Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described.

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Temporal Pattern and Association Discovery of Diagnosis Codes Using Deep Learning

Cited by 39 publications

References 26 publications

Deep Learning for Health Informatics

Deep Learning for Health Informatics

Deep Learning in the Biomedical Applications: Recent and Future Status

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

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