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Image Search Results
Journal: JMIR Medical Informatics
Article Title: State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)–Based Data Model and Structure Implementations: Systematic Scoping Review
doi: 10.2196/58445
Figure Lengend Snippet: Dynamic models.
Article Snippet: Ismail et al [ ] , Patient, Observation, Condition, and Practitioner , MCHHJ bh and CRMHIS bi , Data elements to FHIR resources ,
Techniques: Plasmid Preparation, Biomarker Discovery, Comparison, Generated
Journal: JMIR Medical Informatics
Article Title: State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)–Based Data Model and Structure Implementations: Systematic Scoping Review
doi: 10.2196/58445
Figure Lengend Snippet: NLP2FHIR data normalization pipeline and its applications . cTAKES: Clinical Text Analysis and Knowledge Extraction System; EHR: Electronic Health Record; FHIR: Fast Healthcare Interoperability Resources; MedXN: Medication Extraction and Normalization; ML: Machine Learning; NLP: Natural Language Processing.
Article Snippet: Ismail et al [ ] , Patient, Observation, Condition, and Practitioner , MCHHJ bh and CRMHIS bi , Data elements to FHIR resources ,
Techniques: Extraction
Journal: JMIR Medical Informatics
Article Title: State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)–Based Data Model and Structure Implementations: Systematic Scoping Review
doi: 10.2196/58445
Figure Lengend Snippet: Frequency of Fast Healthcare Interoperability Resources (FHIR) resources mentions in the included articles (n=31).
Article Snippet: Ismail et al [ ] , Patient, Observation, Condition, and Practitioner , MCHHJ bh and CRMHIS bi , Data elements to FHIR resources ,
Techniques:
Journal: JMIR Medical Informatics
Article Title: State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)–Based Data Model and Structure Implementations: Systematic Scoping Review
doi: 10.2196/58445
Figure Lengend Snippet: The summary of previous reviews considering FHIR a .
Article Snippet: Ismail et al [ ] , Patient, Observation, Condition, and Practitioner , MCHHJ bh and CRMHIS bi , Data elements to FHIR resources ,
Techniques: Selection
Journal: The Journal of Supercomputing
Article Title: A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark
doi: 10.1007/s11227-021-03946-7
Figure Lengend Snippet: Architecture of GeoSpark
Article Snippet: Fig. 4 Architecture of
Techniques:
Journal: The Journal of Supercomputing
Article Title: A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark
doi: 10.1007/s11227-021-03946-7
Figure Lengend Snippet: Architecture of MongoDB-based GeoSpark
Article Snippet: Fig. 4 Architecture of
Techniques:
Journal: The Journal of Supercomputing
Article Title: A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark
doi: 10.1007/s11227-021-03946-7
Figure Lengend Snippet: Performance of HDFS-, MongoDB-, and S3-based GeoSpark according to the caching effects
Article Snippet: Fig. 4 Architecture of
Techniques:
Journal: The Journal of Supercomputing
Article Title: A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark
doi: 10.1007/s11227-021-03946-7
Figure Lengend Snippet: Performance of HDFS-, MongoDB-, and S3-based GeoSpark as the distribution of data set is varied
Article Snippet: Fig. 4 Architecture of
Techniques:
Journal: The Journal of Supercomputing
Article Title: A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark
doi: 10.1007/s11227-021-03946-7
Figure Lengend Snippet: Performance of HDFS- and MongoDB-, and S3-based GeoSpark as the size of data set is varied
Article Snippet: Fig. 4 Architecture of
Techniques:
Journal: The Journal of Supercomputing
Article Title: A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark
doi: 10.1007/s11227-021-03946-7
Figure Lengend Snippet: Performance of HDFS- and MongoDB-, and S3-based GeoSpark as the total number of executors in GeoSpark is varied
Article Snippet: Fig. 4 Architecture of
Techniques:
Journal: The Journal of Supercomputing
Article Title: A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark
doi: 10.1007/s11227-021-03946-7
Figure Lengend Snippet: Performance of HDFS-, MongoDB-, and S3-based GeoSpark as the selectivity of queries in GeoSpark is varied
Article Snippet: Fig. 4 Architecture of
Techniques: