FTP (File Transfer Protocol) is a network protocol for transmitting files over TCP/IP connections in a network (including the Internet) configured to transfer files via this protocol. In an FTP transaction, a file sender is called a local host. A file receiver involved in FTP is a remote host, and it is usually a server. Although many file transfers can be conducted using Hypertext Transfer Protocol (HTTP), FTP is still commonly used to transfer files behind the scenes for other applications, such as banking services.

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Transform custom healthcare messages to SDA

EHR (Electronic Health Record) systems are modeled in a proprietary format/structure and are not based on market models such as FHIR or HL7. Some of these systems can interoperate data in a proprietary format for FHIR and other market models, but others can not. InterSystems has two platforms that can interoperate proprietary formats for market ones: InterSystems HealthShare Connect and InterSystems IRIS for Health.

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In this article you will have access to the curated base of articles from the InterSystems Developer Community of the most relevant topics to learning InterSystems IRIS. Find top published articles ranked by Machine Learning, Embedded Python, JSON, API and REST Applications, Manage and Configure InterSystems Environments, Docker and Cloud, VSCode, SQL, Analytics/BI, Globals, Security, DevOps, Interoperability, Native API. Learn and Enjoy!

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Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python.

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Kidney Disease can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Akshay Singh published a very useful dataset for training ML algorithms in the detection/prediction of kidney disease. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/akshayksingh/kidney-disease-dataset.

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