Tag: AWS

The increased interest in NLP

Although some users do not share the massive enthusiasm for the ChatGPT, calling it a creativity killer, the ability to accelerate and optimize many processes has made numerous entrepreneurs and businesses increasingly interested in NLP tools involving Artificial Intelligence and Machine Learning. 

 

So, what is NLP? 

NLP stands for Natural Language Processing, a field of study within computer science and artificial intelligence (AI) that focuses on the interaction between computers and human language. NLP involves the development of algorithms and techniques that allow computers to analyze, understand, and generate human language in a way that is very similar to the way humans communicate with each other.

NLP uses various techniques, such as machine learning, deep learning, and neural networks, to help computers understand the nuances of human language. NLP algorithms can be trained on large data sets of human language to learn patterns and structures such as grammar, syntax, and meanings.

For example, ChatGPT is an NLP tool designed to generate human-like responses to input text. When a user enters text, the model generates a response based on the patterns it has learned from the training data. 

Actually, NLP deals with a wide range of tasks, including language translation, speech recognition, sentiment analysis, text classification and information extraction. These tasks are usually performed on unstructured data, such as text documents, audio recordings, and social media messages.

 

How can it benefit businesses?

• Improving customer service: NLP can be used to automate customer service tasks, such as answering customer requests or distributing queries to the appropriate departments. This can help businesses improve customer satisfaction and reduce the workload of support staff. NLP-powered chatbots and virtual assistants can be used to provide immediate responses to customer queries and provide personalized recommendations. They can also help reduce waiting time and provide 24/7 support.
• Increasing operational efficiency: NLP-based text analytics automatically extracts information from large amounts of unstructured data, such as customer reviews, social media posts and customer service calls. This can help companies identify trends, track brand sentiment, and gain valuable insights into customer needs and preferences. NLP can automate various business processes, such as content moderation, data extraction and categorization, reducing manual work and streamlining operations.
• Improving marketing: enables businesses to understand their customers better and deliver more personalized, targeted messaging. Keyword extraction helps identify keywords and topics relevant to an enterprise's target audience, which helps create content and marketing messages. Methods such as sentiment analysis can be used to understand customer feedback and reviews, helping companies learn how their customers perceive their brand and products. This can help justify marketing messages and improve customer interactions.
• Improving compliance: NLP can automatically detect and flag inappropriate or offensive content in text data, which can help businesses comply with regulations and avoid legal risks. It can automatically monitor regulatory compliance by analyzing documents for relevant keywords, phrases, and patterns, which can be extremely useful for contract management, Anti-Money Laundering (AML), fraud detection, and data privacy compliance.

 

How difficult and expensive is the implementation of NLP?

The complexity and cost of NLP implementation into business processes can vary depending on several factors, including the size of the company and the specific NLP use cases. In general, it can be challenging, time-consuming, and expensive, especially if you want to develop everything from scratch. NLP requires a large amount of data to train and test models and significant computing resources.

But NLP tools can be much more accessible and cost-effective due to cloud services provided by companies such as Amazon Web Services (AWS). These services deliver easy-to-use APIs and pre-built models that can be integrated with other software applications.

 

What are NLP services offered by AWS?

One of the most famous NLP solutions from AWS is probably Alexa. This virtual assistant can understand natural language commands and perform various tasks like playing music, controlling smart home devices, providing weather updates, and answering questions. Alexa uses advanced Natural Language Processing (NLP) techniques to interpret and understand user input.

Alexa was created on top of the Amazon Lex service - a fully managed service providing a platform to build, test, and deploy chatbots and other conversational interfaces that can understand natural language input and respond with appropriate actions. Developers can create custom skills for Alexa using the Lex console, which allows them to define intents and slots, build dialogue flows, and integrate with external services.

Amazon Polly is used to converting generated responses into speech. This text-to-speech service can generate lifelike speech in multiple languages and voices, using deep learning technologies and advanced prosody models, resulting in high-quality, natural-sounding speech output.

A neural machine translation technology is used by Amazon Translate Service to provide high-quality translations. It supports 75 different languages. 

For working with large volumes of data and documents:

• Amazon Comprehend - NLP service that can extract insights and relationships from text data. It can perform tasks like sentiment analysis, entity recognition, language detection, and keyphrase extraction.
• Amazon Textract: a service that can extract text and data from scanned documents, forms, and tables.
• Amazon Kendra: An enterprise search service that uses machine learning to provide natural language search capabilities across multiple data sources, like documents, FAQs, and wikis.

 

These AWS NLP services can be used individually or in combination to build applications that can analyze, understand, and generate natural language.

By integrating these services with other AWS offerings like, for example, a unified communications service - Amazon Chime, companies can create robust and cost-efficient solutions that improve customer engagement and satisfaction and provide assertive communication and collaboration tools for businesses of all sizes, from virtual meetings to contact centres to virtual assistants.

Inmost team has expertise in building solutions that combine AWS services with NLP capabilities, and we can help you create high-performance and cost-efficient solutions that drive business success. 

Don't hesitate to contact: https://inmost.pro/contact-us/

 

Digitization of the healthcare sector

In recent years, the healthcare sector has begun to actively embrace modern digital solutions - from telemedicine applications, connecting residents of the most remote, “hard-to-reach” regions to world-class medical services, to the use of sensors and devices that help remotely monitor and record patient physical data such as: heartbeat, blood pressure, movement and even behavioral patterns. The unique challenges of Covid-19 have played a decisive role in accelerating the digitization of healthcare, when it became clear that many processes in the healthcare sector require a fundamental transformation.

Currently, medicine has a variety of digital tools to improve communication, administrative and operational processes, data storage and transition.

One such tool that facilitates the work of medical professionals is Transcribe Medical Service from Amazon.

 

What is Amazon Transcribe Medical?

In the past, writing paper reports took doctors a lot of time. And after the beginning of digital transition there is a standard requirement for healthcare providers to enter medical records into Electronic Health Record (EHR) systems on a daily basis. According to a study held by the University of Wisconsin and the American Medical Association in 2017, primary care physicians in the United States spent up to 6 hours a day entering this data.

In 2019, Amazon launched a service built on top of the Amazon Transcribe. It was designed specially for healthcare professionals to transcribe medical-related speech, such as physician-dictated notes, drug safety monitoring, telemedicine appointments and consultations, or conversations of doctors with patients. 

The Amazon Transcribe Medical service uses machine learning and natural language processing (NLP) to accurately convert audio speech or conversation to a text. It is trained to understand complex medical language and special terms and measurements used by doctors. Developers can use Amazon Transcribe Medical for medical voice applications, by integrating with the service’s easy-to-use APIs. Pharmaceutical companies and healthcare providers can use Amazon Transcribe Medical to create services that enable fast and accurate documentation. 

The service can transcribe speech as either an audio file or a real-time stream, the input audio can be in FLAC, MP3, MP4, Ogg, WebM, AMR, or WAV file format. Streaming transcription is available in US English, it can produce transcriptions of accented speech, spoken by non-native speakers.

This service provides transcription expertise for primary care and specialty areas such as cardiology, neurology, obstetrics-gynecology, pediatrics, oncology, radiology and urology. Transcription accuracy can be improved by using medical custom vocabularies.

 

Transcribe Medical use cases

Medical dictation: medical specialists can record their notes by speaking into the microphone of a mobile device during or after interacting with a patient, being able to reduce the administrative workload and focus on providing quality patient care.

Drug safety monitoring: transcribing of phone calls regarding drug appointment and side effects enables more safe service provisioning by pharmaceutical companies and clinics. 

Transcribing of conversations: recording conversations between a doctor and a patient in real time without disrupting the interaction, allows healthcare providers to accurately capture details such as mentioned symptoms, medicine dosage and frequency, side effects. This information can be processed through subsequent text analytics and then entered into Electronic Health Record (EHR) systems.

In case of online video or phone consultations Channel Identification feature can be used. This is a powerful tool allowing to independently transcribe the patient and clinician audio channels and provide real-time conversational subtitles.

 

Benefits of Amazon Transcribe Service

Amazon Transcribe Medical benefits a wide range of healthcare specialists: nurses, physicians, researchers, insurers, and pharmaceutical companies - as well as their patients. The following features make it highly attractive to clinicians and healthcare professionals:

HIPAA (Health Insurance Portability and Accountability Act) eligible: providing support for the automatic identification of protected health information (PHI) in medical transcriptions Amazon Transcribe Medical reduces the cost, time, and effort expended on identifying PHI content through manual processes. PHI entities are labeled clearly with each output transcript, making it convenient to build additional downstream processing for a variety of purposes, such as redaction prior to text analytics.

Highly accurate transcription: the narrow specialization of the service, exclusively aimed  at the needs of the healthcare sector, ensures that even the most complex medical terms, such as the technical names of diseases and medicines, are recorded correctly. 

Improving the patient and practitioner experience: so that the doctor does not have to waste time taking notes and writing reports, but can focus on the patient, accurately transcribing all the details of the consultation or conversation without disrupting the interaction.

Easy to use: no prior knowledge or experience with machine learning is required. Developers can focus on building their medical speech applications by simply integrating with the service's APIs. Transcribe Medical handles the development of state-of-the-art speech recognition models.

Thus, Amazon keeps investing into the medical sector, empowering healthcare and life sciences, and enhancing the number of digital services to deliver patient-centered care, accelerate the pace of innovation and unlock the potential of data, while maintaining the security and privacy of health information.

 

Our experience

With extensive experience in building healthcare applications based on Amazon services and developing long-term partnership with global leaders in telemedicine technologies & services, we, Inmost Company, took the opportunity to ease the burden of reporting and documentation for our clients by integrating Transcribe Medical into the application for remote medical consultations. This has significantly optimized medical staff workload, streamlined processes, and increased positive feedback from patients.

Based on these experiences, we consider Amazon Transcribe Medical Service to be a really important and useful tool for transforming medical services. 

And, of course, we are ready to support healthcare organizations on their digital transformation path by providing consulting services, renovating and improving existing platforms or developing efficient and reliable solutions from scratch.

 

Transcribe Service was launched by Amazon in 2017 enabling developers to implement a speech-to-text feature to their applications.

Analyzing and data extraction from audio files is almost impossible for computers. To use such data in an application, speech must first be converted to text. Services performing speech recognition technologies have certainly existed before, but they were generally expensive and poorly adapted to various scenarios, such as low-quality phone audio in some contact centers.

Powered by deep learning technologies, Amazon Transcribe is a fully managed and continuously trained automatic speech recognition service that automatically generates time-stamped text transcripts from audio files. The service parses audio and video files stored in many common formats (WAV, MP3, MP4, AMR, Flac, etc.) and returns a detailed and accurate transcription with timestamps for each word, as well as appropriate capitalized words and punctuation. For most languages, numbers are transcribed into a word form, however for English and German languages Transcribe treats numbers differently depending on the context in which they're used.

Now Transcribe supports 37 languages.

Transcription methods can be divided into two main categories:

• Batch transcription: transcribing media files that have been uploaded into an Amazon S3 bucket;
• Streaming transcriptions: Transcribe media streams in real time.

 

Here are some of the features it provides:

• Single and multi language identification: identifying the dominant language spoken in your media file and creating a transcript. If speakers change language during a conversation, or if each participant speaks a different language, your transcription output correctly detects and transcribes each language;
• Transcribing multi-channel audio: combines transcriptions from multi channel audio into a single output file. It is possible to enable channel identification for both batch processing and real-time streaming;
• Speaker diarization: the partition of the text from different speakers, detecting each speaker in the provided audio file;
• Custom language models: designed to improve transcription accuracy for domain-specific speech. This includes any content that goes beyond the everyday type of conversations. For example, an audio recording of a report from a scientific conference will obviously contain special scientific terms that standard transcription is unlikely to be able to recognize. In this case, you can train a custom language model to recognize the specialized terms used in your discipline;
• Custom vocabularies: are used to improve transcription accuracy for a list of specific words. These are generally domain-specific terms, such as brand names and acronyms, proper nouns, and words that Amazon Transcribe isn't rendering correctly;
• Tagging: adding custom metadata to a resource in order to make it easier to identify, organize, and find in a search;
• Subtitles: can be used to create closed captions for your video and filter inappropriate content from your subtitles.

 

Transcribe offers indispensable features for call centers and support services. It helps to capture useful insights by transcribing customer calls in real time. Analyzing and categorizing calls by keywords, phrases and sentiment can help track negative situations, identify trends in customer issues or allocate calls to specific departments.

It is possible to measure the volume of speech. This metric helps to understand if the customer or employee is talking loudly, which is often an indication of being angry or upset. The quality of communication with the client can also be determined by setting the following metrics: interruptions, non-talk time, talk speed, talk time.

Besides call-centers, Transcribe Service can be useful in almost any field: education, law, e-commerce, and many others. For example, Amazon Comprehend Medical is a machine-learning-powered HIPAA-eligible service pre-trained to identify and extract health data from medical texts, such as prescriptions, procedures, or diagnoses. 

It is difficult to imagine modern technologies without a service that can transform speech into text. And of course, Transcribe has analogues from other digital giants. However, it is worth noting that a large number of developers who have leveraged Amazon service, admit a much higher quality and accuracy compared to similar solutions provided by the current market.