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Antibiotic resistance is a growing global health crisis, putting patients at risk and complicating the treatment of common infections such as pneumonia. Healthcare providers need efficient tools to identify and manage antibiotic resistance effectively.

If left unchecked, antibiotic resistance can lead to prolonged hospital stays, increased healthcare costs, and even death. The need for a comprehensive solution to identify and manage antibiotic-resistant infections has never been more critical.

EHR clinical data analytics with Natural Language SQL (NLSQL) technology offers a powerful approach to address this challenge by providing a bird's-eye view of patient data, particularly in cases where patients have been diagnosed with pneumonia and antibiotic resistance to cefazolin or ampicillin. This technology can lead to improved patient outcomes and contribute to the global fight against antibiotic resistance.

EHR clinical data analytics with NLSQL technology is a transformative solution for addressing the growing problem of antibiotic resistance, especially for patients …

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Natural Language SQL (NLSQL) technology has become an indispensable tool for businesses of all sizes, allowing users to quickly access data without requiring specialized technical skills. One area where NLSQL can be particularly valuable is in planning negotiations, where having access to relevant data and information can make a significant difference in the outcome.

One model for planning negotiations that can be enhanced by NLSQL is the 4-step "Keyhole" model. This model provides a structured framework for negotiating, with the following steps: preparation, opening, bargaining, and closing. Let's take a closer look at how NLSQL can support each of these steps.

Preparation: The first step in the 4-step "Keyhole" model is preparation, which involves gathering information about the other party, identifying your goals and priorities, and anticipating potential obstacles or challenges. NLSQL can be used to quickly and easily query databases for relevant information, such as past business indicators, market …

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As organisations generate more data, the ability to extract insights from that data becomes increasingly important. SQL is a powerful tool for querying databases, but creating SQL queries can be time-consuming and difficult, especially for non-technical users. Natural Language to SQL (NLSQL) technology offers a solution to this problem, allowing users to generate SQL queries using natural language.

The advantages of NLSQL go beyond simply reducing the time and effort required to create SQL queries. NLSQL is highly customizable, allowing users to train the technology to understand specific terms and phrases relevant to their business. This means that NLSQL can be tailored to the unique needs of each organization, making it a highly versatile tool.

Additionally, NLSQL can work with a variety of data sources, including structured, semi-structured, and unstructured data. This makes it a valuable tool for organizations that need to analyze data from multiple sources. By using NLSQL, …

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In today's fast-paced and ever-evolving business world, making informed decisions about the future of your company is more critical than ever. From determining the number of new employees to hire, forecasting demand and product units to order, and predicting when equipment is likely to break down, business leaders must leverage data to make well-informed decisions.

Gone are the days when only data scientists and business analysts were expected to know how to use and process data. With the increasing availability of vast amounts of data from various sources, managers and leaders are expected to understand how to assess the quality and usefulness of data and justify decisions based on insights drawn from the data.

In this post, we explore the importance of using data for business decision-making and the key steps involved in maximizing its value.

Step 1: Understanding the Nature of Your Data
It is essential to understand whether …

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Generative Pre-trained Transformer (GPT) models, specifically the well-known GPT-3 and the GPT-4, have shown great potential in the field of Natural Language Processing (NLP) and can be used in a variety of business scenarios.

GPT-3, developed by OpenAI, is a powerful language model that has been trained on a vast amount of data. It can generate human-like text, making it suitable for a wide range of Natural Language Processing tasks such as language translation, text summarization, and conversation generation. In the business world, GPT-3 can be used to automatically generate product descriptions, customer service responses, and marketing materials. It can also be fine-tuned to understand specific business-domain language, making it even more accurate in understanding the context of a text.

The GPT-4 could potentially have even more capabilities than GPT-3. It would likely be trained on larger and more diverse data sets, and may have additional capabilities such as improved …

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