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Even a modest change in serum creatinine has been associated with increased cost, length of stay and mortality, because of this, it is imperative to combat the development of AKI in patients.
The current diagnostic pathway for detecting AKI depend on a rise in serum creatinine level, but the rise in serum creatinine level lags behind renal injury.
Recent work using sequential AKI predictive models have not been clinically applicable and have focused on predictions across a short time horizon.
This study utilized an artificial intelligence (AI) method known as deep learning to assess the likelihood patients would development AKI within the next 48 hours.
The dataset was randomly divided into 4 sets: training (80%), validation (5%), calibration (5%), and testing sets (10%).
Using the electronic health records, the data is processed one step at a time and building on internal memory that keeps track of relevant information seen up to a specific time period.
Introducing New Einstein Search – Increasing Productivity With AI for Every Customer
If you’re one of the4.5 billionpeople connected to the internet today, you use a search engine to find, purchase, or learn about pretty much anything that comes to mind.
But when those same people try to search within their CRM at work, the experience is painfully underwhelming: too many clicks to find what you’re looking for and an interface that confuses more than it helps.
But in an enterprise setting, users have divergent goals that can range from salespeople trying to close deals to service agents solving customer cases and email marketers creating new campaigns.
For instance, when customers buy CRM platforms like Salesforce to customize it, they are creating an environment that is completely unique to their business, from the fields they use to the custom objects they create.
Under the hood, Einstein Search leverages innovative data mining and machine learning techniques to personalize search results, all while keeping specific user information anonymized.
11 Implementations Of AI And Machine Learning That Truly Benefit Sales Teams
Artificial intelligence (AI) and machine learning (ML) form two of the most powerful emerging technologies to enter the world of business today.
Below, 11 members of Forbes Business Development Council chime in on the ways sales teams can leverage AI and ML to increase the efficiency of their operations and overcome the most common hurdles they may encounter.
Use a predictive data tool like InsideSales.comto allow you to standardize and scale your processes, creating a more consistent performance from your sales reps.
While I personally feel there are continued improvements to be made in the data quality and weighting of the data by industry, I see great potential for this to assist high-volume salespeople to better manage their pipeline and prioritize their sales activities using this information.
Having Set Answers We have to make it easy for our customers to order products from us, and with AI we have developed a set of questions and answers that help the end user receive quick technical answers to their questions.
Front Loading Quality Into The Sales Funnel The best place for AI in the sales process is in helping fill the sales funnel with higher quality leads based on the characteristics of the target customers and personnel being contacted, then refining the message to be the best fit.
Intelligent Selling Technologies such as Salesforce Einstein score the health of our customers, the health of our pipeline and ultimately suggest the action sales should take to move deals forward in an efficient manner.
Leveraging ML and AI to automate much of the process of following up with sales leads allows prospects to move through the sales funnel while enabling salespeople to channel their time, energy or focus on the components of making a sale that require human touch.
- On 10. april 2021
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