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Unlocking the Potential of Clinical NLP: A Comprehensive Overview

John Snow Labs

The impact of Natural Language Processing in everyday life is hard to ignore as it is the main driver of emerging technologies like Robotics, Big Data, Internet of Things, etc. It enables machines to process massive amounts of data and make informed decisions. the clinical NLP system should be able to detect it.

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Enable Transparent Tracking with NextGen Technologies for Cold Chain Logistics

Trigent

For the range of supplies labeled as perishables, particularly pharmaceuticals and food (produces), quality expires with time as they maintain chemical reactions, which can mostly be alleviated with lower temperatures. Also, over $161 billion worth of losses were reported in 2010 due to food waste.

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How Natural Language Processing Is Helping Doctors Make Better Diagnoses

John Snow Labs

Healthcare NLP saves the time and effort of physicians, and makes the information of use by: Using specialized engines that scrub large sets of unstructured data and discover improperly coded or previously missed patient conditions. Allowing physicians to extract critical insights rather than wasting time in reviewing complex EHRs.

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Startups have about $1 trillion worth of reasons to love the Biden infrastructure plan

TechCrunch

Big data analytics, monitoring and sensing tools, and robotics are also becoming fixtures on the farm. The vast majority of those are nowhere up to modern codes. “It would present a huge opportunity for a variety of climate tech sectors, ranging from clean electricity to carbon management to vehicle electrification.”

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Metadata Management: Process, Tools, Use Cases, and Best Practices

Altexsoft

Another example is labeling and categorizing objects in any scientific field, be it archeological finds, chemical samples, or living things in biology. Such uniform usage enables interoperability and integration between disparate systems. Usually such files are kept in a database system – a metadata repository.

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Predictive Maintenance: Employing IIoT and Machine Learning to Prevent Equipment Failures

Altexsoft

Source: Tibbo Systems. Major cons: the need for organizational changes, large investments in hardware, software, expertise, and staff training. Predictive maintenance became possible due to the arrival of Industry 4.0, the fourth industrial revolution driven by automation, machine learning, real-time data, and interconnectivity.

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Generative AI in Healthcare

John Snow Labs

To fully realize the benefits of Generative AI, BCG recommends that healthcare leaders create an enterprise-wide strategy, invest in data systems and capabilities, forge strategic partnerships, and integrate with the broader industry ecosystem​​. Optimizes healthcare resources. Not does this approach enhance treatment effectiveness.