As a part of HL7 parser development, need to know whether the placer order and filler order number of multiple OBR segments in a HL7 message will be same.
Read theory about placer and filler order number in the internet. But was not able to find good examples.
Looking forward for answer with good examples.
As a part of HL7 parser development, I personally, would always expect that the placer order and filler order number fields will have different values for each OBR segment in a message. However, I believe it is common for these order numbers to be the same in all OBR segments within a message.
That being said if an ORU (result) message contains observations for a test that is a single specimen with multiple tests performed then you will have multiple OBR and OBX segments for each of the tests performed. In a case such as this the multiple tests could be performed by different laboratories causing different filler order number fields.
Sources:
Corepoint Health's Website OBR definition
Check out the sample message at this HL7 viewer
My own 8 years of experience in Healthcare IT.
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I have a data set: article titles and number of views. In it I want to conduct such research, the dependence of the words applied in the title to the number of views. I have cleaned the data to the type of words without endings and using python want to calculate the average value of views for each word. this will allow me to judge the topics that people are more and less interested. Wanted to ask how such methods are called in statistics so i can read about them. For there are many different correlations, and i would like to approach the task as correctly as possible. P.S. Then I want to count the same correlation for a bundle of two and three words.
I'm struggling with a task of optimization of resources, and I wonder if someone knows any efficient way to find the optimal solution for it, using only Excel. I explain what I'm trying to achieve:
Suppose you are managing an assembly factory for metal tubes. Your raw material is standard size tubes, and then in your factory you need to cut these tubes according to a list of requests from clients, with very specific sizes. All tubes are of the same type, so we can reuse leftovers from each cut, if the length of that leftover is sufficient to satisfy any tube request.
We can also group small length requests to be made from one single tube, for example, on the attached list, we could use one 8 metre tube to deliver the last four entries (1,615+1,62+1,625+1,67), with 1,47 leftover wasted.
Assuming a long list of requests, and that the tubes supplied are 8 metres each, do you know of any way of calculating how many tubes I have to order to satisfy the list of requests, minimising the losses per each cut?
Example of request list, each entry is in metres
I am trying to extract numeric information from set of SMSs. The regexes fail in extracting balance and credit amounts as the patterns of the SMS is not consistent throughout the industry.
We are currently making assumptions to make it work like First Amount = Credit amount
Second Amount=Balance.
This has lot of limitations and error rate is gradually increasing.
Anyone has any alternatives to regexes?
There is no standard for this kind of message as every operator creates its own messages. this is marketing communication... Yet, for a given operator and a given plan all replies to balance inquiry messages should be the sames (as long as they are not changed by operators marketing teams...).
Regexes are a good tool but you need to know the message forehand and create the appropriate regex patterns
My friend has a small business where customers order services using email. He receives several emails a day and sorting thru it is becoming cumbersome.
There are about 10 different kind of tasks the customer can request, and for each there are one or two words that specify it. The other info present in the emails is the place where the service is to be delivered, the time, and the involved people's names. The email also contains an ID, a long number with a fairly standard format.
The emails are very unstructured, but all contain the key info above. My question is: what is the best method to sweep thru these emails and extract the key info (such as type of service, place, people's names, the ID etc)?
I thought about some kind of pre-processing, then pass it thru AlchemyAPI and then test the Alchemy output using Neural Networks for each feature (key info). This can be supervised learning as I can do a feedback loop all the time, as once the info is inputted, I can have someone to validate.
Any ideas? Thanks
I guess some parts (ID, task, time) can be captured by a regular expression and dictionary matching. Have a look at GATE's JAPE tool.
It should be fairly easy to assemble a dictionary and then use the lookups for the "task", also you can reuse the available jape rules for date/time and write a new one for the ID (also, a simple regex could be fine).
For matching the location and people's names you should be careful, openCalais and alchemyAPI can give you good results if names and places are used in well defined sentences and will probably make more mistakes with some tabular or weird format. Also you can never be sure you captured the place and person correctly so don't rely on that for processing orders directly.
If you have more information about mails' structure or expected names and places (i.e. you have a "clients" table with all possible names), you would probably want to do your own tagging, otherwise I'd stick to openCalais or alchemyAPI + some regular expressions.
P.S. I assume all mails are in English.
I have an app that extracts information from incoming messages. The messages all contain the same information, but they have different forms depending on the source that sent them.
Example:
Message from source A :
A: You spent $50.00 at Macy's on 2/20/12
Message from source B :
Purchase, $50.00, Macy's, 2Feb2012, Balance $5000.00
Every message from a single source has the same form though. So at the moment, I'm doing it by writing a set of regular expressions to first identify which message I'm trying to decode (i.e. what source it came from so I know what the form of the message is), and then extracting the necessary information from the message (in the above example, I want to know the transaction amount, the store where the transaction happened, and the date). If I discover a new source for a message, or a source changes the format of their message (doesn't happen very often, but could happen), I need to manually write the regular expressions for that message. I'm sure however that I could automate this using some kind of machine learning technique. I just don't know much about machine learning, and I don't know where to even start looking for a technique that would apply to my problem. I would like someone to just point me in the right direction on where to start reading.
In order to detect and label amounts, dates, person names and similar information you can use a technique called Named Entity Recognition. The Stanford Named Entity Recognizer comes with pretrained, ready to use models.
You also use whatever labeled data you have generated so far to learn a custom model for your application. The standard techniques used for this purpose are Conditional Random Fields or Sequence Perceptron. There are many toolkits implementing these models, including:
Wapiti - A simple and fast discriminative sequence labelling toolkit.
Sequor - sequence labeler based on Collins's (2002) perceptron.