The Danish researchers behind FindZebra would not be happy about my title as they published a warning on the top of their search engine with the message: “This is a research project to be used only by medical professionals.” This search engine only uses medical databases similarly to SciencerollSearch and named the rare disease correctly after entering the symptoms 67% of the time, compared to 32% using Google.
I think educating both patients and medical professionals about the proper use of search engines and operators would provide even better results.
Here is their description.
There are close to 7,000 rare diseases recognized by rare disease organizations. We index over 31,000 documents covering rare and genetic diseases from 10 reputable sources. Given the number of rare diseases and rate of publication, we think FindZebra is a good companion for medical professionals.
Everyone has heard about the new Graph Search function on Facebook. It says “Want to start a book club or find a gym buddy? Connect with friends who like the same activities—and meet new people, too.” It will let Facebook users do searches by choosing different parameters (e.g. who goes to the same gym as me and is single).
Well, many bloggers are optimistic about this launch and think it will be used in medicine too. I don’t think so and everyone should hope I’ll be right. It’s fun to identify friends in my community who I share the same multiple interests with (e.g. sci-fi and reading books), but the same concept in medicine just should not work. Here are examples what Michael Spitz came up with:
- “What do my friends think about HIV?”
- “Do any of my friends have erectile dysfunction?”
- “Have any of my friends had a bad reaction to taking Drug X?”
- “What do you think about Dr. Y?”
- “How was your stay at hospital Z?”
Only a minority of Facebook users would add the medical conditions they have to their profile; or publish a post about a side effect of a new drug they are taking. As such data would not be added to Facebook, it will not be used for search. Moreover, if Facebook makes it clear to my friends which gym I go to, that’s OK as far as this is within the privacy borders I set; but making clear which conditions I have or which drugs I take is just not the function I expect from a social networking site.
WolframAlpha works fine because data are added in a professional, anonymous and structured way. See all the medical examples they have.
There are other platforms such as Yandex Wonder doing the same as Facebook Graph but in a much better quality and with much more data (its access to Facebook was blocked when Facebook Graph was released, what a “coincidence”). But it still might not be used for medical purposes.
So expect to see this kind of search engines in the near future, but hopefully this new feature will not breach the privacy of patients and doctors on Facebook.
Joshua Schwimmer, the most famous kidney doctor and blogger, had an interview with the Google team about using Google Book Search in medical education:
You know how much I admire WolframAlpha and how often I use it for medical search queries. Now they have an amazing, interactive main page with a lot of medical examples including tooth #31, check it out.
Social media is changing how medicine is practiced and healthcare is delivered. Patients, doctors, communication or even time management, everything is changing, except one thing: medical education. We need a revolution!
When a UK physician wanted to visit Hungary every week just to attend my university course focusing on social media and medicine, I decided it’s time to make this course global.
Today, The Social MEDia Course goes live with 16 flash Prezis, exciting tests, badges and achievements. Enjoy and have fun while learning! Medical students, physicians and even patients, everyone is welcome to take the course which is, of course, for free.
Here is a video about the course (and also a Prezi).
A medical professional (just like an e-patient) has to be proficient at searching online. I’ve been telling my students that they have to keep practicing. One of the ways to do so is a Chrome extension, A Google a Day.
- They provide a task every day.
- You try to find the answer. That’s it.
Let’s see one example:
If you key in international dialing code 40, how would you say “good morning” in the language of the country you’re calling?
Google Correlate is a tool on Google Trends which enables you to find queries with a similar pattern to a target data series. The target can either be a real-world trend that you provide (e.g., a data set of event counts over time) or a query that you enter. I found a slightly good correlation between weight loss and wedding checklist. Is it surprising?
Try other medical conditions as well.
The developer team that designed the semantic-like clustering search engine of Webicina.com created another engine that was featured in a competition initiated by the National Library of Medicine.
NLMplus ( http://nlmplus.com) is an innovative semantic search and discovery application, developed by WebLib LLC, a small business in Maryland, in response to a challenge contest by the National Library of Medicine (NLM) to make use of NLM’s vast collection of biomedical data and services for the benefit of the Library’s diverse worldwide user communities.
Now that Webicina.com has a new design and a brand new search engine, I thought it would be useful to show the basic features and I also publish an interview with Endre Jóföldi, CEO at WebLib LLC, the company behind the search engine of Webicina.com.
If you do a search for diabetes, you will see
- whether Webicina has a diabetes resource (a blog, podcast, Facebook group, Twitter user, etc), collection (Diabetes and Web 2.0) or sub-section (Diabetes Mobile Apps).
- You can narrow the search by “social media collection” (e.g. a diabetes resource in the asthma collection), “curated dynamic news category” (e.g. news categories featuring diabetes resources), “resource type” (RSS, resource, subsection or collection) or “languages”.
- Please tell us more about WebLib! What kind of projects are you famous for?
Generally we are building intelligent search solutions for our customers like the NIH or Vanderbilt University Biomedical Library. Behind the scenes we are also working on a semantic web knowledge base to improve our core search engine.
- What is the engine behind the Webicina search function like?
Our PolySearch enterprise search engine is a best-of-class domain independent semantic indexing and search solution built on top of the open source Solr/Lucene enterprise search platform. We have built several different search solutions based on this engine, which are all different in some sense based on the different needs of our customers.
PolySearch uses grammatical tools to find different forms of words and also uses our SearchComplete to offer health related search queries when the user typing his/her queries. It also utilize our medical spell checker to help correcting typos.
- How does clustering search help us find exactly what we need?
We face the problem of information overload after doing searches. For example if you search for cancer on Webicina you will get more then 40 thousand results. Our relevance algorithm floats up the most important Webicina resources, however if you want to look through the tons of RSS results, it definitely helps to filter it down to your area of interest. Like you will see only 479 results in the Nutrition category. This is a much more friendly number.
- How difficult was it to develop a search engine for Webicina.com?
We tried to make a very fast engine, what helps Webicina visitors to find all the information Webicina treasures. It is also handling the different resource types and languages so we think it can really improve the user experience. More than that we have still plans how to make it even better, so it is going to be a longer process, where we want to use the data how the visitors are going to use this search service.