You can choose to look at all types, just diagnoses or just drugs. Highlight in the canvas below and drag around. The points that youve selected will show up in the table below along with a description in plain text. Please play around with this data and let me know what you find! Type name description Highlight some points above for this summary to be filled. 04 December 2015 Cleveland, oh). Parsing is the process of constructing a tree from a string of characters; unparsing is the reverse: constructing a string of characters movie from a tree. A so-called "pretty-printer" is an example of a processor that incorporates an unparser: It reads arbitrarily-formatted text, builds a tree representing the text's structure, and then unparses that tree using appropriate formatting rules to lay out the text in a standard way. An unparser is also used to produce a textual representation of a tree-structured data object.
Although arthritis is typically associated with advancing age, in ibd it often strikes the youngest patients. Dental Abscesses While not much medical literature exists with a specific link to dental abscesses and Crohns (there are general oral issues noticed here you do see lengthy discussions on the Crohns forums gpa about abscesses being a common occurance with Crohns. Yeast Infections Candidiasis of skin and nails is a form of yeast infection on the skin. From the journal Critical review of Microbiology here. It is widely accepted that Candidia could result from an inappropriate inflammatory response to intestinal microorganisms in a genetically susceptible host. Most studies to date have concerned the involvement of bacteria in disease progression. In addition to bacteria, there appears to be a possible link between the commensal yeast Candida albicans and disease development. Visualization For further investigation, i have used t-distributed stochastic neighbor embedding to embed the 100-dimensional vector space into 2 dimensions. This embedding should retain the general connections within the data, so you can look at similar diagnoses, drugs and allergies.
This disease is the number one killer of Americans. Our model found the following similar diseases: icd9 CodeDescriptionScore v12.71 Personal history of peptic ulcer disease.930 533.40 Chronic or unspecified peptic ulcer of unspecified site with hemorrhage, without mention of obstruction.926 153.6 Malignant neoplasm of ascending colon.910 238.75 myelodysplastic syndrome, unspecified.910. Partiaully due to smokers having a higher than average incidence of peptic ulcers and atherosclerosis. You can see an editorial in the British Medical journal all the way back in the 1970s discussing this. Hearing Loss From an article from the journal of Atherosclerosis in 2012: Sensorineural hearing loss seemed to be associated with vascular endothelial dysfunction and an increased cardiovascular risk Knee joint Replacements These procedures are common among those with osteoarthritis and there has been a solid. Crohns Disease Crohns disease is a type of inflammatory bowel disease that is caused by a combination of environmental, immune and bacterial factors. Lets see if we can recover some of these connections from the data. Icd9 CodeDescriptionScore 274.03 Chronic gouty arthropathy with tophus (tophi).870 522.5 Periapical abscess without sinus.869 579.3 Other and unspecified postsurgical nonabsorption.863 135 Sarcoidosis.859 112.3 Candidiasis of skin and nails.855 v16.42 Family history of malignant neoplasm of prostate.853 Arthritis From the. It may affect as many as 25 of people with Crohns disease or ulcerative colitis.
Sage reference - data, textual - sage knowledge
The set of ddls are here Transform this tabular data into a corpus of medical event sentences. The etl pig scripts are here The shell script oedipus executing the pig scripts are here build the word2vec model with Spark. You can see from the jupyter notebook detailing the model building portion and results here that model building is only a scant few lines: from pyspark import SparkContext from lib. Feature import Word2Vec sentences p(lambda row: row. Split word2vec word2Vec tSeed(0) tVectorsize(100) model t(sentences) Results One of the problems with unsupervised models is evaluating how well our model is describing reality. For the purpose of this entirely unscientific analysis, well restrict ourselves to just diagnoses and ask a couple of questions of the model: does the model correctly recover what we currently know based on medical research?
Does the model show us anything that is novel and likely, but unknown at present? One thing to note before we get started. This model uses cosine similarity as the score. This measure of similarity ranges from 0 to 1, with 1 being most similar and 0 being least similar. Atherosclerosis Also known as heart disease or hardening of the arteries.
For instance, is the vector representation of type 2 diabetes - obesity close to type 1 diabetes? When considering trying this technique out the problem, of course, is getting access to medical data. This data is extremely sensitive and is covered by hipaa here in the United States. What we need is a good, depersonalized set of medical encounter data. Thankfully, back in 2012 an electronic medical records system, Practice fusion released a set of 10,000 depersonalized medical records as part of a kaggle competition. This opened up the possibility of actually doing this analysis, albeit on a small subset of the population.
Implementation Since ive been doing a lot with Spark lately at work, i wanted to see if I could use the word2Vec implementation built into Sparkml to accomplish this. Also, frankly, having worked with medical data at some big hospitals and insurance companies, i am aware that there is a real scale problem when doing something this complex for millions of medical encounters and I wanted to ensure that anything I did could scale. The implementation boiled down into a few steps, which are common to most projects that ive seen run on Hadoop. I have created a small github repo to capture the code collateral used to process the data here. Ingest the Practice fusion database dumps into hadoop. Shell script here pin up hive tables for each of the tables, roughly corresponding to a table per medical event.
Presentation of data - slideShare
We will call this set of events a medical encounter and they happen every day all over the world. This sequence of events has a similar tone to what were familiar with in natural language. The encounter can be essay thought of as a sort of medical sentence. Each medical event within the encounter can be thought of as a medical word. The type of event (lab, procedure, diagnoses, etc.) can be considered as a sort of part-of-speech. It remains to determine if this structure can be teased out and encoded into a vector space model like natural language can. If so, then we can ask questions like: How similar are two diseases based on how they are treated and comorbidities found in the same encounter? Can we compose diseases and make them similar to other diseases?
The vector representation of king - male female is near the vector representation of queen). This is a surprisingly rich organization of data and one that has proven very effective in enhancing the accuracy of machine learning models that deal with natural language. Perhaps the most surprising day part of this is that the vectorization model does not utilize any of the grammatical structure of the natural language directly. It simply analyzes the words within the sentences and through usage it fits the proper embedding. This led me to consider whether other, non-textual data which has some inherrent structure can also be organized this way with the same algorithm. Medical Data Whenever we go to the doctor, a set of events happen: measurements are made (e.g. Blood pressure, pulse, height, weight) Labs are drawn and ordered (e.g. Blood tests) Procedures are performed (e.g. An x-ray) diagnoses are made Drugs are prescribed These events happen in a certain overall order but the order varies based on the patient situation and according to the medical staffs best judgement.
complex ways to represent your data. Whole companies have been formed around providing a way to gain insight through more complex organizations of the data, taking some of the burden of interpretation from our brain and encoding it in an organization scheme. Today, id like to talk about another approach to data simplification for event data which provides not just an interesting representation, but also a way to ask the data certain kinds of useful questions of your data. One common way to impose order on data that is used by engineers and mathematicians everywhere is to embed your data in a vector space with a metric. This gives us a couple things : Data now has a distance which can be interpreted as the degree of difference between the data data can be combined via addition and subtraction operations which can be interpreted as combination and separation operations The issue now. Thankfully, the nice people at google developed a nice way of doing this in the domain of natural language text called Word2Vec. I wont go into extravagant detail into the implementation as Radim Řehůřek did a great job here. The major takeaways, however, is that using the inherrent structure of natural language, word2Vec is able to construct a vector space such that a word similarity can be interpreted as a distance calculation The notion of analogies can be interpreted using the addition and subtraction.
The second, perhaps less obvious, challenge is that subject matter experts knowledge is biased toward that which is already known. Often data scientists and small analysts are trying to understand the data not as an ends, but rather as a means to gaining insight. If you only take into account received knowledge, then making unexpected insights can be challenging. That being said, spending time with subject matter experts is a necessary yet insufficient part of data analysis. To complete the task of understanding your data, i have found that it is necessary to spend time looking at the data. One can think of the entire field of statistics as an exercise in building a mechanism to ask data pointed questions and get answers that we can trust, often with caveats. The goal is generally to get a sense of how the data is organized or arranged. With the unbelievable complexity of most real data, we are forced to simplify our representations. The question is just precisely how to simply that representation to find the proper balance between simplicity and complexity.
Textual Data representation Know the code
At least half of the battle of data analysis and data science is understanding your data. That sounds obvious, but ive seen whole data science projects fail because not nearly enough time was spent on the exercise of understanding your data. There are only two real ways to go about doing this: Ask an expert, ask the data, to have a shot at doing this you really have to do both. In the course of this blog post, Im going to describe some of the challenges with understanding data and Ill go into some technical detail of how to borrow some scalable unsupervised learning from natural language processing coupled with a very nice data visualization. I spend a lot of time with healthcare data and the obvious subject matter experts are nurses and doctors. These people are very gracious, very knowledgeable and extremely pressed for time. The problem with expert knowledge is that its essay surprisingly hard to communicate effectively sufficient nuance to help the working data scientist accomplish their goals. Furthermore, its extremely time consuming. This is made doubly hard when the expert is entirely unclear about the goal.