I'll be short to save your time :)
I'm new at StackOverflow and also new with IBM Watson.
We are building an EMR (electronic medical records) system and would be glad to enhance it with Watson cognitive capabilities for healthcare.
Where do I start from?
Is here anyone who has ever used cognitive approach for assisted medical decision making? Can anyone give me an orientation?
I thought to start with Q&A for doctors but Q&A has been depreciated by IBM. Predictive analytics would also be exciting for physicians, however, what is the starting point?
Thank you beforehand!
I think you refer to a deprecated Bluemix API for health. One thing you can do is use Retrieve and Rank API on a trusted set of documents.
yes I have used the following for health with IBM Watson
Reading chest X-rays - https://www.ibm.com/watson/developercloud/doc/visual-recognition/
Reading EKG's - https://www.ibm.com/watson/developercloud/doc/visual-recognition/
Patient diagnosis for chest pain - https://www.ibm.com/watson/developercloud/dialog.html
Physical exam - we started to use retrieve and rank for machine learning of a patient's physical exam over the years. - https://www.ibm.com/watson/developercloud/retrieve-rank.html
Speech to text (patient telling watson where it hurts) - https://www.ibm.com/watson/developercloud/speech-to-text.html
As you can see there are many different watson api's .
Related
I'm new in machine learning I see some services on Google Cloud platform related to A.I I think these are easy to use.
Here is what I need I have around 20K paragraphs (3 or 4 line) I need to find the most matching paragraph according to user question. User ask any question or type any sentence I need to find the most similar paragraph related to this user sentence how can I do that. What Services I need to achieve this I want to use Google Cloud platform. is it possible in gcloud if yes then how.
I think you can start your journey looking through the GCP AI & ML product list. Narrowing down the initial request and seeking for a best match affording your custom use scenario, I would advice to get more details about GCP AutoML products, offering a variety of complete solution for a generic machine learning models such as AutoML Natural Language model specifically designed for a document and text analysis tasks.
I would encourage you to start with AutoML Natural Language beginner's guide to get more context, having look at features and capabilities like of Classification, Entity extraction, Sentiment analysis training approaches.
As from the developers perspective, Cloud AutoML Natural Language supports a client libraries for most known programming languages and offers good REST API documentation though.
I'm using IBM Watson Assistant and I have built a very good and functional chatbot and I want to make even better.
I'm trying for a while to find a dataset to import entities and intents for IBM Watson Assistant. I have already found some data from datasearch google and other sites but I'm searching for booking system and customer support data. Is there any site that I can find the right format for Watson or any data from the above.
Usually the trained intents and entities are customized within each company for its use-cases, and it will be hard to find a ready plug and play intents that fits your usecase 100%. But you can get the data to start training Watson assistant yourself using examples from datasets available on Kaggle.
Does the IBM Watson question answering system that won the Jeopardy game in 2011, support dialogue with users in its new versions?
I think you are referring to Watson Assistant, which is the 4th generation Q&A / Dialog / Conversation / Assistant service.
You can find a demo at the bottom of the page at
https://www.ibm.com/cloud/watson-assistant/features/
and getting started instructions at
https://cloud.ibm.com/docs/services/assistant?topic=assistant-getting-started#getting-started
If you opt for the plus or premium price plan then you can make use of search skill, which cribbed from the documentation -
When Watson Assistant doesn't have an explicit solution to a problem,
it routes the user question to a search skill to find an answer from
across your disparate sources of self-service content. The search
skill interacts with the IBM Watson™ Discovery service to extract this
information from a configured data collection.
If you already use the Discovery service, you can mine your existing
data collections for source material that you can share with customers
to address their questions.
However, you do not need to have a Discovery service instance. If you
choose to create a search skill, a free instance of Discovery is
provisioned for you. You can then create a collection from a data
source and configure your search skill to search this collection to
find answers to customer queries.
If you don't have a plus or premium plan then its still possible to craft in the Discovery search into your application using either your own orchestration or a cloud function.
Can anyone tell me what algorithm is used to classify intents and understand entities in Watson assistant? Have they published any papers or articles regarding this?
Yes, they published this paper explaining in a manner how the Watson Work, and for more information you should learn about Cognitive Systems, but in advance it's not just one algorithm used, but many approaches that combined are capable of getting the desired result.
Another material you should learn if this is your interest is the computer science area "Information Retrieval", in which many subjects are used to comprehend what the user wants and give the needed information. The book Modern Information Retrieval is a good start point.
According to IBM Developer Answers:
"Intents are classified using an SVM, with some pre training by IBM. entities use a fuzzy matching algorithm."
https://developer.ibm.com/answers/questions/387916/watson-conversation-algorithm/
Support Vector Machine (SVM) is a supervised machine learning algorithm.
I've been trying to use the IBM Watson Document Conversion service with the demo PDF, but it's not converting the document into little bits. All it's doing, is creating 1 answer unit, that's really long:
"text": "Watson is an artificially intelligent computer system capable of answering questions posed in natural language,[2] developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson.[3][4] The computer system was specifically developed to answer questions on the quiz show Jeopardy![5] In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.[3][6] Watson received the first place prize of $1 million.[7] Watson had access to 200 million pages of structured and unstructured content consuming four terabytes of disk storage[8] including the full text of Wikipedia,[9] but was not connected to the Internet during the game.[10][11] For each clue, Watson's three most probable responses were displayed on the television screen. Watson consistently outperformed its human opponents on the game's signaling device, but had trouble responding to a few categories, notably those having short clues containing only a few words. In February 2013, IBM announced that Watson software system's first commercial application would be for utilization management decisions in lung cancer treatment at Memorial Sloan- Kettering Cancer Center in conjunction with health insurance company WellPoint.[12] IBM Watson's former business chief Manoj Saxena says that 90% of nurses in the field who use Watson now follow its guidance.[13]"
Thanks in advance!
Unfortunately, that demo PDF is not the best document to use: Currently, Answer Units are split based on heading tags (h1 - h6), and that PDF doesn't contain any headers. =(
If you set the conversion_target to NORMALIZED_HTML, you'll be able to see the converted PDF before it is split up into Answer Units. It will contain paragraphs but no headings.
In the future, we expect to also allow splitting Answer Units by paragraph, but that hasn't been released yet.
UPDATE:
We updated the PDF on the demo site with one that's a much better example.