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Query drug adverse effects and recalls based on natural language using Amazon Comprehend Medical

In this post, we demonstrate how to use Amazon Comprehend Medical to extract medication names and medical conditions to monitor drug safety and adverse events. Amazon Comprehend Medical is a natural language processing (NLP) service that uses machine learning (ML) to easily extract relevant medical information from unstructured text. We query the OpenFDA API (an open-source API published by […]

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In this post, we demonstrate how to use Amazon Comprehend Medical to extract medication names and medical conditions to monitor drug safety and adverse events. Amazon Comprehend Medical is a natural language processing (NLP) service that uses machine learning (ML) to easily extract relevant medical information from unstructured text. We query the OpenFDA API (an open-source API published by the FDA) and Clinicaltrials.gov API (another open-source API published by the National Library of Medicine (NLM) at the National Institutes of Health (NIH)) to get information on past adverse events, recalls, and clinical trials for the drug or medical condition in question. You can then use this data in population scale studies to further analyze the drug’s safety and efficacy.

Launching a new drug is an extensive process. By some estimates, it takes about 12 years to go from invention to launch. It involves various stages like preclinical testing, phase 1–3 clinical trials, and approvals by the Food and Drug Administration (FDA).In addition, new drugs require huge financial investments by pharmaceutical organizations. According to a new study published in JAMA Network, the median cost of bringing a drug to market is $918 million, with the range being between $314 million–$2.8 billion.

Even after launch, pharmaceutical companies continuously monitor for safety risks. Consumers can also directly report adverse drug reactions to the FDA. This could result in a drug recall, thereby jeopardizing millions of development dollars. Moreover, consumers who are taking these drugs and clinicians who are prescribing them need to be aware of such adverse reactions and decide whether corrective actions are necessary.

While no investment is guaranteed, drug manufacturers are starting to rely more on ML to achieve better outcomes and improve the chances of market success for new drugs they develop.

How can machine learning help?

To ensure drug safety, the FDA uses real-world data (RWD) and real-world evidence (RWE) to monitor post-market drug safety and adverse events. For more information, see real-world data (RWD) and real-world evidence (RWE) are playing an increasing role in health care decisions. This is also useful for healthcare professionals who develop guidelines and decision support tools based on RWD. Drug manufacturers can benefit from RWD analysis and use it to develop improved clinical trial designs and come up with new and innovative treatment approaches.

One of the major challenges with analyzing RWD effectively is that a lot of this data is unstructured—it doesn’t get stored in rows and columns that make it friendly to analytical queries. RWD can exist in multiple formats and span a variety of sources. It’s impracticable to use conventional analytical techniques to process unstructured data at the scale of a population. For more information, see Building a Real World Evidence Platform on AWS.

Advances in natural language processing (NLP) can help fill this gap. For example, you can use models trained on RWD to derive key entities (like medications and medical conditions) from adverse reactions reported by patients in natural language. After you extract these entities, you can store them in a database and integrate them into a variety of reporting applications. You can use them in population scale studies to determine cohorts susceptible to certain drugs or to analyze the drug’s safety and efficacy.

Solution architecture

The following diagram represents the overall architecture of the solution. In addition to Amazon Comprehend Medical, you use the following services:

The architecture includes the following steps:

  1. The demo solution is a simple html page which will be served via a lambda function on the first invocation of the api gateway url. The url will be in the output section of CloudFormation stack or it can be grabbed from api gateway.
  2. The submit buttons on the url will asynchronously invoke 2 other lambdas via apigateway
  3. The 2 Lambdas will use a common layer function to vet the free text entered by user by Comprehend Medical and return medication and medical conditions.
  4. The lambda functions process the entities from Comprehend Medical to query open source api’s clinicaltrail.gov and open.fda.gov. The HTML would render the output from these lambdas into respective tables

Prerequisites

To complete this walkthrough, you must have the following prerequisites:

Configuring the CloudFormation stack

To configure your CloudFormation stack, complete the following steps:

  1. Sign in to the Amazon Management Console.
  2. Choose us-east-1 as your Region.
  3. Launch the CloudFormation stack:
  4. Choose Next.
  5. For Stack name, enter a name; for example, drugsearch.
  6. In the Parameters section, update the API Gateway names as necessary.
  7. Provide the name of an S3 bucket in us-east-1 to store the CSV files.
  8. Choose Next.
  9. Select I acknowledge that AWS CloudFormation might create IAM resources.
  10. Choose Create stack.

The stack takes a few minutes to complete.

  1. On the Outputs tab, record the URL for the API Gateway.

Searching for information related to drugs and medical conditions

When you open the URL from the previous step, you can enter text related to drugs and medical conditions and choose Submit.

The output shows three tables with the following information:

  • Adverse effects of the related drugs and symptoms – This information is queried from clinicaltrial.gov, and records are limited to a maximum of 10.
  • Drug recall-related information – This information is queried from open.fda.gov, and records is limited to a maximum of 5 for every drug and symptom.
  • Clinical trials for the related symptoms and drugs – This information is queried from clinicaltrial.gov.

In addition to the tables, the page displays two hyperlinks to download clinical trial information and the OpenFDA in a CSV file. These files have a maximum of 100 records for clinical trials and 100 for every drug and medical condition in OpenFDA.

Conclusion

This post demonstrated a simple application that allows drug manufacturers, healthcare professionals, and consumers to look up useful information from trusted sources like the FDA and NIH. Using this architecture and the available code base, you can integrate this solution into other downstream applications related to the analysis and reporting of adverse events. We hope this lowers the barrier of entry and increases adoption of ML to improve patient outcomes and improve quality of care.


About the authors

Varad Ram is Senior Solutions Architect in Partner Team at Amazon Web Services. He likes to help customers adopt to cloud technologies and is particularly interested in artificial intelligence. He believes deep learning will power future technology growth. In his spare time, his daughter and son keep him busy biking and hiking.

Ujjwal Ratan is Principal Machine Learning Specialist Solution Architect in the Global Healthcare and Lifesciences team at Amazon Web Services. He works on the application of machine learning and deep learning to real world industry problems like medical imaging, unstructured clinical text, genomics, precision medicine, clinical trials and quality of care improvement. He has expertise in scaling machine learning/deep learning algorithms on the AWS cloud for accelerated training and inference. In his free time, he enjoys listening to (and playing) music and taking unplanned road trips with his family.

Babu Srinivasan is Senior cloud architect at Deloitte. He works closely with customers in building scalable and resilient cloud-based architectures and accelerate the adoption of AWS cloud to solve business problems. Babu is also an APN (AWS Partner Network) Ambassador, passionate about sharing his AWS technical expertise with the technical community. In his spare time, Babu loves to spend time performing close-up card magic to friends and colleagues, wood turning in his garage woodshop or working on his AWS DeepRacer car.

Source: https://aws.amazon.com/blogs/machine-learning/query-drug-adverse-effects-and-recalls-based-on-natural-language-using-amazon-comprehend-medical/

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A Quick Guide to Conversational AI And It’s Working Process

Customer support is an integral part of every business; without offering support services, it is difficult to achieve maximum customer satisfaction. To ensure the same, […]

The post A Quick Guide to Conversational AI And It’s Working Process appeared first on Quytech Blog.

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Customer support is an integral part of every business; without offering support services, it is difficult to achieve maximum customer satisfaction. To ensure the same, businesses hire professionals who work round the clock to deliver support services. No matter how efficiently a business handles this segment, they might have to face problems such as “delay in responding customers’ queries” or “making a customer wait to connect with the support professionals”, and more.

A Conversational AI is a perfect solution to this most common challenge that manufacturing, FMCG, retail, e-commerce, and other industries are facing. Never heard of this term?

Well, this is the latest trend in almost all the industries that are already using artificial intelligence technology or wanting to adopt the same in their business operations. Let’s read about the same in detail.

What is Conversational AI?

Conversational AI is a specific kind of artificial intelligence that makes software interact and converse with users in a highly intuitive way. It uses natural language processing (NLP) to understand and interact in human language.

The conversational AI, when integrated into chatbots, messengers, and voice assistants, enables businesses to deliver personalized customer experience and achieve 100% customer satisfaction. Google Home and Amazon Echo are the two popular examples of it.

Applications of Conversational AI

Conversational AI a new and automated way of offering customer support services. Healthcare, Adtech, logistics, insurance, travel, hospitality, finances, and other industries are using technology in the following:

Messaging applications

Conversational AI can be used in a messaging application to offer personalized support services through chat. Your customers can choose the “chat support” option and talk to the chatbot to get the support.

Speech-based virtual assistants

A conversational AI can use speech recognition technology so that you can offer a speech-based virtual assistant for your customers. These are the type of chatbots where users can get any information through voice commands.

Virtual customer assistants

This type of conversational AI helps in offering online support to customers; you can develop the same to offer support through Web, SMS, messaging applications, and more.

Virtual personal assistants

A virtual personal assistant, powered by conversational AI, minimizes the need of hiring a huge team to offer dedicated support services to each of your customers.

Virtual employee assistants

Employees working in big organizations might need various types of assistants. A conversational AI used to build a virtual employee assistant can be the point of contact for all such employees. They can find the required information just by interacting with that assistant.

Robotic process automation

Robotic process automation using the potential of conversational AI helps a machine to understand human conversations and their intent to perform automated tasks.

Working of conversational AI

Machine learning, deep learning, and natural language processing are the three main technologies behind conversational AI. Here is how it works:

  1. Collection of unstructured data from various sources
  2. Data preprocessing and feature engineering
  3. Creating an AI model
  4. Training the model to automatically improve from experiences
  5. Testing the model
  6. Detecting patterns and making decisions
  7. AI deployment

What benefits businesses can get by using conversational AI?

Apart from helping businesses to deliver an unmatched customer experience, conversational AI can offer a plethora of other benefits that include:

Saves Time

Having an automated chatbot would help you save a considerable amount of time. The saved time can be used to perform other tasks or focus on your business’ marketing and promotion.

Helps in providing Real-Time Support Services

A conversational AI can handle multiple queries at one time, without even letting other customers wait. In short, every customer will feel like they are getting dedicated support services in real-time.

Improves business efficiency

With a conversational AI, you can have the assurance that your customers are being taken care of properly. In short, their queries are being handled and resolved immediately. You can focus on other segments of your business and improve efficiency.

Helps in lowering down Customers’ Complaints

Since a conversational AI can immediately respond to queries of the customers, it can help to reduce the number of complaints. Resolving customers’ complaints without making them call a support professional would increase customer loyalty and increase your brand reputation.

Increases chances of sales

By providing a persistent communication channel that precedes the context further, you can make your customers explore more and shop more. Moreover, a conversational AI can also help in reducing the cart abandonment rate as it can provide immediate assistance regarding the issues a user is facing while making the payment, applying a discount code, or at any other time.

The user would not even have to contact the support center. To understand this better, let’s take an example- a user has added one or more products to the cart, but he/she is unable to find his/her preferred mode of payment. After a few minutes, what would the customer do, either visit the contact us section to get the customer support number and connect to a professional (which is a time taking process) or simply get the support through a conversational AI. The latter would automatically send a message to the customer to ask the query and provide a resolution.

Now when you know everything about conversational AI and want to build one for you, then contact Quytech, a trusted AI app development company. Quytech has more than a decade of experience in working on artificial intelligence, machine learning, and other latest technologies.

Final Words

Are you curious to know about conversational artificial intelligence? Give this article a read to know the definition and working of conversational AI in detail. We have also mentioned the reasons why this technology is becoming the talk of the town among businesses of all sizes and types. After reading the article, if you want to develop a tailor-made conversational AI for your business, then reach out to a reliable and experienced AI development company or hire AI developers with considerable experience.

Source: https://www.quytech.com/blog/what-is-conversational-ai-and-how-does-it-work/

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Are Legal chatbots worth the time and effort?

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KLoBot — the Best AI-Chatbot builder platform

The legal industry is always known for its resistance to change, but technology in the legal landscape has seen rapid growth from the past few years. The global Coronavirus pandemic has also accelerated the pace of investments in legal technology, which is likely to transform the legal marketplace.

Several law firms are majorly focusing on the adoption of innovative technology, which has the capability to modernize the practice of law. Innovative technologies, including Artificial Intelligence, Analytics, and Blockchain, among others, prioritizes the speed and efficiency of legal services.

Technology in the legal sector is an enabler that empowers attorneys and paralegals to perform their jobs better.

The use of AI and its applications in the legal industry is moving higher and is becoming the next big thing for legal firms. By 2024 the legal AI software market is expected to reach $1,236 million and is forecasted to grow at a CAGR of 31.3% during 2019–2024. (2)

Incorporating AI into legal practice can augment the workflows and streamline the work processes. AI-powered chatbots are disrupting the legal industry and are poised to become a preferred mode of communication for internal as well as external users. Leveraging NLP and NLU algorithm power, which are one of the prominent fields in AI, chatbots can understand intents, contexts, and further handle end to end human-machine interactions.

Law firms, as well as corporate legal departments, continue to look for new ways to enhance efficiency and drive productivity. AI-enabled chatbots are one such way that has the potential to revolutionize the law firm operations. These chatbots are a new approach for law firms to imitate human conversations and automatically respond to clients as well as attorneys’ queries.

Legal chatbots have the capabilities to make better and quicker decisions when compared to human agents. It reduces the burden on attorneys and paralegals to repetitively answer the same queries, which further brings consistency to users’ responses.

1. 8 Proven Ways to Use Chatbots for Marketing (with Real Examples)

2. How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

Internal Chatbots

Internal chatbots are nothing but the chatbots for internal operations and communications, helping law firms manage enterprise collaboration. Internal legal chatbots help law firms automate the contract review process, which is one of the most tedious tasks for attorneys and in-house counsel.

Legal chatbots for attorneys come with a predefined set of policies to review & analyze documents, perform due diligence, and automate other monotonous tasks that attorneys perform. Other basic tasks comprising scheduling meetings, setting up reminders, and searching relevant matter information can also be performed by legal chatbots. Internal legal chatbots empower attorneys to reduce the risk of human errors by automating the monotonous administrative chores and allow them to focus more on higher value and complicated tasks that need attorney’s intervention.

External Chatbots

External chatbots, on the other side, are the client-facing legal chatbots. These chatbots can draft the legal documents, including UCC filings, divorce forms, and other non-disclosure agreements based on the client inputs.

External legal chatbots empower law firms to handle the client intake process efficiently and generate leads, which further reduces an attorney’s time spent on these activities.

In the current scenario of receiving information instantly at a fingertip, legal chatbots serve as the best solution to handle client queries and provide legal advice.

External, as well as internal legal chatbots with their 24/7 supporting abilities, facilitate law firms to manage operational costs and meet the evolving client expectations.

Although chatbots are taking time to augment legal services but are worth the effort.

KLoBot is an incredibly intelligent AI chatbot builder platform that allows legal firms to create text and voice-based chatbots within minutes. KLoBot’s easy drag and drop skill interface helps law firms to design no-code chatbots that can be deployed across an organization’s favorite channels. The chatbots built on the KLoBot platform help law firms perform simple and complex routine tasks, including QnA and knowledge repository search. Few other jobs, including scheduling meetings, setting up reminders, completing actions on behalf of attorneys, finding colleagues, assisting attorneys, and much more, are also being performed by KLoBot enabled chatbots.

KLoBot enabled chatbots to act as a personal assistant and enhance attorneys as well as client experiences. These chatbots empower law firms to simplify internal as well as external communications and streamline business processes.

KLoBot AI chatbots with its feature-rich admin console, provide law firms robust security controls. To know more about KLoBot click here.

References

[1] The Law Society Capturing Technological Innovation in Legal Services Report

[2] https://www.marketsandmarkets.com/

Source: https://chatbotslife.com/are-legal-chatbots-worth-the-time-and-effort-5f44936f7e89?source=rss—-a49517e4c30b—4

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Things to Know about Free Form Templates

A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to […]

The post Things to Know about Free Form Templates appeared first on 1redDrop.

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A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to view and also work with the form files. 

It will create a new free form template by copying and storing those files within a folder. A form template (.XSN) file designing or creation of a single file will include various supporting files. Users may fill out the online form by accessing the .XML form file, which is a form template.

Designing Free Form Templates

There are numerous processes that define free form template design, and are as follows:

  • Designing the form’s appearance – the instructional text, labels, and controls
  • Controls will assist with user interaction behavior on the form template. You can design a specific section to appear or disappear when the user chooses a particular option
  • Whether the form template may include some additional views. For a permit application form design, for example, you have to provide different views for each person. One view especially for the electrical contractor, next for the receiving agent, and finally, the investigator. He or she will deny or approve the permit application
  • Next, you need to know how & where to store the form data. Designing free from templates will allow users to submit their data within the database either online or direct access. If not, they can also store the same in any specific shared folder
  • It is essential to design the other elements, colors, and fonts within the form template
  • Users must be able to personalize the form. Allowing users to include various rows within the optional section, repeating section, or a repeating table
  • Users should receive a notification when they forget to input a mandatory field or make mistakes within the form
  • After completing the free form templates design, you can publish the same online using a .XSN file format

Club Signup Form

A simple registration form can help your Club Signup Form creation process go smoother. This signup form could be an ideal solution for a new club membership registration for any organization or club.

Application Form

Application form templates are much easier to use & set-up to streamline your application process. You can customize this online form and utilize the same for numerous applications. Make use of this application form as a job application form, volunteer applications, contest entries, or high school scholarship applications. It is an ideal solution for scholarship programs, nonprofit organizations, business owners, and many such users and use cases.

Scheduling Form

Scheduling form templates are handy and can be used for numerous appointment booking requirements. A scheduling form is also utilized for various appointment scheduling or online reservations and booking purposes. Regardless of your business requirement, it is easy to customize the form template.

Concept Testing Survey

While testing a new design or concept, it is essential to gather the responses quickly. Freeform templates for a concept testing survey make it much easier to gather product feedback and reach the target audience. It is essential to conduct market research while planning to release a new product. A mobile-friendly form will allow you to utilize the survey questions for collecting the product’s consumer input quickly.

Credit Card Order Form

It is not always a complex process to provide an online credit card payment form for the customers. This form template will allow you to access numerous services or products for collecting card payment information. You can utilize this yet-another endless and simple payment form.

Employment Application Form

The employment application form for recruitment will assist the HR team to gather the required information from candidates. During the interview or application process, you can easily remove any expensive follow-ups. Some of the fields are contact information, employment history, useful information, etc. as well as an outline of the job description, consent for background checks, military service record, anticipated start date, any special skills, and many more. It is optional to enable notifications for the form owners to receive an alert or email when a new employment application is submitted.

Source: https://1reddrop.com/2020/10/24/things-to-know-about-free-form-templates/?utm_source=rss&utm_medium=rss&utm_campaign=things-to-know-about-free-form-templates

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