Clinical Data Management and Analysis – CDMA

Topics

• Studies proposing computing and modeling of multi-source large-scale data that integrates clinical data with biological and genomics, such as bioinformatics for disease prediction, diagnosis, and individualized treatment; public health bio-surveillance systems; screening tools/screening algorithms; and management of integrated database related to clinical use.
• Studies to develop and validate systems, software, or technologies for clinical use including computing architecture design; data security in the clinical research network; data collecting; storage and sharing (cloud); data standardization and integration; and data privacy.
• Studies to develop data mining and analytics methods including natural language processing; machine learning (deep learning)/artificial intelligence; ontologies; data simulation and modeling; computer visualization; and spatial/temporal modeling. Data sources would include, but are not limited to: clinical data (such as electronic health records, administrative data, claims data, patient/disease registries, health surveys, clinical trials data); clinical images (pathological, radiological, etc.) integrated with other data (such as EHRs); physiologic data (such as electrocardiograms (EKGs) and electroencephalograms (EEGs); omics data; environmental data; behavioral phenotypes; social media analytics; mHealth data; and clinical trials data.

Shared Interests and Overlaps

CDMA and Clinical Informatics and Digital Health (CIDH) have shared interests in informatics and computing methods and technology to support clinical decisions. Applications that emphasize the development of computing technology, simulation models, and software are reviewed in CDMA. Applications that emphasize clinical utility, scalable health IT interventions, and development of informatics platforms or evaluation of human-machine interface in the workflow towards implementation are reviewed in CIDH.

CDMA and Biodata Management and Analysis (BDMA) have shared interests in data integration, analysis and validation. Applications that emphasize the clinical translation or application of computational data mining methods/technology for clinical decision support are reviewed in CDMA. Applications that emphasize the analysis of biological data are reviewed in BDMA.

There are shared interests in clinical data analysis from human populations with Analytics and Statistics for Population Research Panels A and B (ASPA/B). Applications that emphasize the clinical data analysis and methodology development for eventual translation to clinical use are reviewed in CDMA.  Applications that emphasize the development of this statistical methodology to support epidemiological studies and public health decision making using clinical biological or biomedical data are reviewed in ASPA. Applications that emphasize the development of statistical methodology to support epidemiological studies and public health decision making using clinical behavioral or social data are reviewed in ASPB.   

CDMA and Clinical Translational Imaging Science (CTIS) have shared interests in the use of imaging datasets for clinical decision support. Applications that emphasize the development, validation, and use of computational methodology to integrate data from multiple sources, which may include imaging datasets, to support healthcare delivery and clinical decisions are reviewed in CDMA. Applications that emphasize the development, optimization, or integration of imaging components and systems for clinical translation, or extract information from imaging data for clinical decision making are reviewed in CTIS.

CDMA and Emerging Imaging Technologies and Applications (EITA) have shared interests in the use of artificial intelligence and machine learning approaches with imaging technologies. Applications that emphasize the development, validation, and use of computing systems to integrate data from multiple datasets, including imaging, for clinical decision making are reviewed in CDMA. Applications that emphasize the newly developed imaging analysis/methods and tools for imaging processing, feature detection, and classification in imaging datasets are reviewed in EITA.

There are shared interests in imaging and computational approaches to medical imaging with Neuro Informatics, Computational and Data Analysis (NICD). Applications that emphasize imaging from electronic health records and clinical data for clinical use are reviewed in CDMA.  Applications that emphasize creating or developing multimodal imaging or computational approaches to medical imaging based on TMS, tDCS and optogenetics are reviewed in NICD.

There are shared interests in imaging and computational approaches to medical imaging with Neuromodulation and Imaging of Neuronal Circuits (NINC). Applications focus on imaging from electronic health records and with clinical outcomes are reviewed in CDMA. Applications that focus on creating or developing multimodal imaging or computational approaches to medical imaging based on TMS, tDCS and optogenetics are reviewed in NINC.

There are shared interests with Translational Investigations of Pulmonary and Immunological Diseases (RCCS (81)). Applications that study the use of EMR to generate patient or disease profiles associated with respiratory or immune-related conditions may be reviewed in RCCS (81). Applications which propose to generate EMR may be reviewed in CDMA.