Archive for February, 2013
February 26, 2013
IBM Watson is going to medical school at Cleveland Clinic. What Watson has to bring to medicine is the potential for advanced clinical decision support. Specifically algorithm-based, Bayesian decision analysis, rule based and expert systems. Several hurdles exist to accomplishing this: acquiring and validating of patient data, modeling of medical knowledge, keeping the data up-to-date, validate and integrate with the workflow. This process fits well with the Learning Healthcare System concept from the Institute of Medicine of taking research on evidence-based medicine into clinical decision support.
IBM Watson’s process in medical school will be to improve the inference graphs based on current data through human intervention. Providing clinical decision support is based on EMR data and the medical literature using DeepQA.
“The DeepQA project at IBM shapes a grand challenge in Computer Science that aims to illustrate how the wide and growing accessibility of natural language content and the integration and advancement of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation and Reasoning, and massively parallel computation can drive open-domain automatic Question Answering technology to a point where it clearly and consistently rivals the best human performance.”
Welcome Artificial intelligence to medicine and specifically clinical decision support.
February 21, 2013
My recent Perspective on iHealthbeat focused on the uses of data mining of EMR data which are yet to be fully exploited. My thoughts were provoked by a New York Times article titled, Mining Electronic Records for Revealing Health Data. Although data mining in healthcare has gotten a bad reputation, an approach which respects privacy and a focus on research discovery can yield important results. The potential uses of EMRs in research is another opportunity yet to be realized.
A new article in The Atlantic, The Robot Will See You Now, discusses IBM Watson and other initiatives moving medicine toward what I call Algorithm Medicine and Artificial Intelligence. The potential of mining EMRs to generate real-time clinical decision support has exciting possibilities. However, there are skeptics, especially when the predictions expand to entertain the idea of replacing physicians. Realizing the limitations of technology must be acknowledge. For instance, the concerning problem of copy-and-paste in EMRs would have a negative affect on data mining those records. Also, data mining has presents real challenges both in defining research questions and finding the correct data to answer those questions.
So data mining shows promise but a realistic approach without wild predictions can lead to real discovery and impact on practice.