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Big Data Mining and Analytics  2019, Vol. 2 Issue (4): 288-305    DOI: 10.26599/BDMA.2019.9020007
    
Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks
Ying Yu, Min Li, Liangliang Liu, Yaohang Li, Jianxin Wang*
Ying Yu is with the School of Computer Science and Engineering, Central South University, Changsha 410083, China, and the School of Computer Science and Technology, University of South China, Hengyang 421001, China. E-mail: yuying@mail.csu.edu.cn.
Min Li, Liangliang Liu, and Jianxin Wang are with the School of Computer Science and Engineering, Central South University, Changsha 410083, China. E-mail: limin@mail.csu.edu.cn; liuliang_double@mail.csu.edu.cn.
Yaohang Li is with the Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. E-mail: yaohang@odu.edu.
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Abstract  

The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine.



Key wordsdeep learning      clinical data      Electronic Health Record (EHR)      medical image      clinical note     
Received: 08 March 2019      Published: 06 January 2020
Corresponding Authors: Jianxin Wang   
About author:

? Jiangcheng Zhu and Shuang Hu contribute equally to this paper. This work was done when they were visiting researchers in Data Science Institute, Imperial College London, London SW7 2AZ, UK.

Cite this article:

Ying Yu, Min Li, Liangliang Liu, Yaohang Li, Jianxin Wang. Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks. Big Data Mining and Analytics, 2019, 2(4): 288-305.

URL:

http://bigdata.tsinghuajournals.com/10.26599/BDMA.2019.9020007     OR     http://bigdata.tsinghuajournals.com/Y2019/V2/I4/288

DatasetDescriptionData typeSizeWebsite
ADNIImaging data of Alzheimer’s diseaseMRI image, PET image, clinical data, biospecimen1070-2000 participants (ADNI3)http://adni.loni.usc.edu
OAIVarious data of OsteoarthritisClinical examination, radiological images, a biospecimen repository4796 participantshttps://oai.epi-ucsf.org/datarelease/
SOF20 years of prospective data about osteoporosisSelf-administered questionnaire, clinic interview, clinic examination10 366 older women, 9 visitshttps://sofonline.epi-ucsf.org/interface/
MIMICClinical data of ICUDemographic information, clinical note, physiological measurements46 520 patients (MIMIC III)https://mimic.physionet.org/about/mimic/
Grand-ChallengesChallenge datasets of medical and biomedical imagesAll kinds of biomedical images158 challenge datasetshttps://grand-challenge.org/challenges/
i2b2Challenge datasets in NLP for clinical dataClinical notes8 challenge datasetshttps://www.i2b2.org/
Table 1 Public clinical dataset sources.
Data typeApplicationMethodTask&Reference
OCTClassificationDeep CNNAge-related macular degeneration detection[23]
Diabetic retinopathy detection[24]
Macular degeneration and diabetic retinopathy classification[25]
Image detectionFast CNNHemorrhage detection[132]
Risk factor identificationDNNcardiovascular risk factors[112]
Image segmentationCNNOptic Disc (OD) and Optic Cup (OC) segmentation[86]
PETClassificationSAE&CNNDiagnosis of disease status[133]
DNNDiagnosis of Alzheimer’s disease[134]
CNN&RNNDiagnosis of Alzheimer’s disease[135]
MicroscopyClassificationCNNSkin cancer classification[12]
MRIClassificationDBNAD /Health Control (HC) classification[27]
Schizophrenia and Huntington’s disease classification[28]
AD/MCI/HC classification[136]
SAEAD and MCI classification[102]
CNNAD/MCI/HC classification[137]
ANNAD/MCI/HC classification[138]
Image detectionCNNLeft ventricle slice detection[29]
Automatic spine scoring, vertebral discs analysis, lesion hotspots detection[32]
Vertebrae detection and labeling[33]
RNNIdentification of end-diastole and end-systole frames[30]
Image segmentationDBNLeft ventricle segmentation[31]
CNNKnee cartilage segmentation[34]
Prostate lesions segmentation[87]
Image detection & segmentationCNNLocalization, identification, and segmentation of vertebrae[139]
Survival predictionDNNSurvival prediction of amyotrophic lateral sclerosis patients[120]
CTImage segmentationCNNSegmentation of liver, spleen, and kidneys[35]
Kidney segmentation[36]
Liver segmentation[37]
Pancreas segmentation[140]
Image detectionCNNColon polyp detection[141]
ClassificationCNNInterstitial lung texture classification[38]
2D interstitial pattern classification[39]
Lung texture pattern classification[40]
DBN&RBMClassification of lung texture and airways[142]
Mortality predictionCNNprediction of 5-year mortality in elderly individuals[119]
X-rayClassificationCNNTissue classification[44]
Detection of cardiovascular disease[143]
Discriminate malignant masses from (benign) cysts[45]
Pathology detection[41]
Five common abnormalities’ detection[42]
Tuberculosis detection[43]
Frontal/lateral classification[144]
SAEBreast density classification[145]
Image detectionCNNMass detection[46]
Table 2 Overview of deep learning methods applied on medical image.
Data typeApplicationMethodTask&Reference
Discharge summaryICD assignmentRNNAutomated ICD coding/ Diagnosis code assignment[49,50,110,111]
CNNAutomated ICD-9 coding[48]
ClassificationCNNPhenotype extraction[52]
Entity relation extractionRNNClassifying three categories’ relations[124]
Pathology reportClassificationRNN&CNNMultiple information extraction[53]
Death certificateICD assignmentRNNICD-10 codes assignment for the underlying cause of death[66]
Narrative medical recordDe-identificationRNNDe-identification[125,126]
NonspecificName entity recognitionRNNextracting medical events (medication, disease)[98]
Mortality predictionCNNICU mortality prediction[118]
Table 3 Overview of deep learning methods applied on clinical notes.
Data typeApplicationMethodTask&Reference
Radiology report & imageDisease predictionRNN&CNNRadiology image identification[54]
Retinal microaneurysm detection[55]
Admission notes and discharge summariesName entity recognitionDNNName entity recognition in Chinese clinical text[97]
Death certificates and autopsy reportsICD assignmentRNNAssignment of ICD-10 codes for causes of death[109]
R-Fmri & clinical textEarly warningDAEEarly diagnosis of Alzheimer’s disease[121]
Structural MR and FDG-PET imagesEarly warningDNNIdentify individuals at risk of developing Alzheimer’s disease[122]
EHR (medical codes)Disease predictionRNNDiagnosis prediction[96,106,108]
EHR (medical codes & demographic information)Disease predictionDNNDiagnosis prediction[83]
EHR (medical codes, demographic information, lab results)Risk factor identificationDBNTo identify the risk factors of osteoporosis[113]
Readmission predictionDNNTo assess patient readmission risk[115]
CNNUnplanned readmission following discharge prediction[116]
RNNFuture readmission prediction[117]
Early warningRNNEarly detection of heart failure[123]
PrognosisRNNTo predict in-hospital mortality, 30-day unplanned readmission, prolonged length of stay, and final diagnoses[13]
Table 4 Overview of deep learning methods applied on mixed clinical data.
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