Extending DICOM imaging to new clinical specialties in the healthcare enterprise

2002 ◽  
Author(s):  
Peter M. Kuzmak ◽  
Ruth E. Dayhoff
Keyword(s):  
Healthcare Enterprise ◽  
Dicom Imaging

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2021 ◽  
pp. 42-47
Author(s):  
Ramesh Ponnala ◽  
K. Sai Sowjanya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Linear Model ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Healthcare Enterprise ◽  
Learning Techniques ◽  
Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

Standards to Support Information Systems Integration in Anatomic Pathology

2009 ◽  
Vol 133 (11) ◽  
pp. 1841-1849 ◽  
Author(s):  
Christel Daniel ◽  
Marcial García Rojo ◽  
Karima Bourquard ◽  
Dominique Henin ◽  
Thomas Schrader ◽  
...  
Keyword(s):  
Information Systems ◽  
Information Exchange ◽  
Tissue Bank ◽  
Clinical Information ◽  
Diagnostic Process ◽  
Anatomic Pathology ◽  
Research Activities ◽  
Healthcare Enterprise ◽  
Integrating The Healthcare Enterprise ◽  
Technical Framework

Abstract Context.—Integrating anatomic pathology information— text and images—into electronic health care records is a key challenge for enhancing clinical information exchange between anatomic pathologists and clinicians. The aim of the Integrating the Healthcare Enterprise (IHE) international initiative is precisely to ensure interoperability of clinical information systems by using existing widespread industry standards such as Digital Imaging and Communication in Medicine (DICOM) and Health Level Seven (HL7). Objective.—To define standard-based informatics transactions to integrate anatomic pathology information to the Healthcare Enterprise. Design.—We used the methodology of the IHE initiative. Working groups from IHE, HL7, and DICOM, with special interest in anatomic pathology, defined consensual technical solutions to provide end-users with improved access to consistent information across multiple information systems. Results.—The IHE anatomic pathology technical framework describes a first integration profile, “Anatomic Pathology Workflow,” dedicated to the diagnostic process including basic image acquisition and reporting solutions. This integration profile relies on 10 transactions based on HL7 or DICOM standards. A common specimen model was defined to consistently identify and describe specimens in both HL7 and DICOM transactions. Conclusion.—The IHE anatomic pathology working group has defined standard-based informatics transactions to support the basic diagnostic workflow in anatomic pathology laboratories. In further stages, the technical framework will be completed to manage whole-slide images and semantically rich structured reports in the diagnostic workflow and to integrate systems used for patient care and those used for research activities (such as tissue bank databases or tissue microarrayers).

Digital Pathology and Virtual Microscopy Integration in E-Health Records

2010 ◽  
pp. 457-484 ◽  
Author(s):  
Marcial García Rojo ◽  
Christel Daniel
Keyword(s):  
Biomedical Research ◽  
Digital Pathology ◽  
Virtual Microscopy ◽  
Limiting Factors ◽  
Snomed Ct ◽  
Health Records ◽  
Anatomic Pathology ◽  
Healthcare Enterprise ◽  
Information Management Systems ◽  
Research And Education

In anatomic pathology, digital pathology integrates information management systems to manage both digital images and text-based information. Digital pathology allows information sharing for diagnosis, biomedical research and education. Virtual microscopy resulting in digital slides is an outreaching technology in anatomic pathology. Limiting factors in the expansion of virtual microscopy are formidable storage dimension, scanning speed, quality of image and cultural change. Anatomic pathology data and images should be an important part of the patient electronic health records as well as of clinical datawarehouses, epidemiological or biomedical research databases, and platforms dedicated to translational medicine. Integrating anatomic pathology to the “healthcare enterprise” can only be achieved using existing and emerging medical informatics standards like Digital Imaging and Communications in Medicine (DICOM®1), Health Level Seven (HL7®), and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT®), following the recommendations of Integrating the Healthcare Enterprise (IHE®).

scholarly journals 2016 ACC/ASE/ASNC/HRS/SCAI Health Policy Statement on Integrating the Healthcare Enterprise

2016 ◽  
Vol 68 (12) ◽  
pp. 1348-1364 ◽  
Author(s):  
John R. Windle ◽  
Alan S. Katz ◽  
J. Paul Dow ◽  
Edward T.A. Fry ◽  
Andrew M. Keller ◽  
...  
Keyword(s):  
Health Policy ◽  
Policy Statement ◽  
Healthcare Enterprise ◽  
Integrating The Healthcare Enterprise
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