The translation from the proprietary healthcare data to the JSON data we need is relatively straightforward since most proprietary healthcare systems also model their data based on the common healthcare concepts. For example, Patient has a name, a birthday Observation has a value, etc. The JSON data schema formalizes the fields of the respective healthcare concept. There is one JSON data schema per healthcare concept - Patient, Observation, Condition, MedicationRequest, and so on, - they are all healthcare concepts. For the purpose of this blog post, we can think of it as JSON data. It’ll take 10 other blog posts to fully explain FHIR. The Google Brain team first converted ICU data they received from partner hospitals into an open healthcare data standard, called FHIR. Now with the setting of the learning task clarified, let’s dive into the encoding procedures to see exactly how the medical records are transformed for the learning task.
#MEDICAL MEDIUM MANUAL#
#MEDICAL MEDIUM SERIES#
However, the Google Brain team hypothesized, which proved successful with data validation, that by taking into account all the available patient data, and performing a time series analysis, a more accurate prediction for all the above outcomes can be achieved. This blog post includes a summary of some popular ICU analysis models in the end. Traditional methods/models rely on handpicked feature variables, evaluated at the prediction time. We can predict quality of care, in this case, 30 days readmission after discharge. We can predict resource utilization, in this case, larger than 7 days length of stay.
We can predict clinical outcomes - in this case, mortality (death) events. Google Brain and many other researchers chose ICU settings because ICU’s data is usually the most complete and available for research compared to other healthcare data. The setting of the learning task is to make predictions for intensive care unit (ICU) patients. If you don’t want to read the original 30+ pages of abstruse content, this blog post is a good alternative for you. It illustrates with simplified examples and visual graphs the methods proposed in Google Brain’s “ scalable and accurate deep learning with electronic health records” and its supplementary materials.
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It examines how to encode medical records and make them suitable for deep learning, in particular, time series learning.
This blog post has a very specific focus. While the data analysis or machine learning model development typically draws more attention, the importance of the data engineering leading to that should not be overlooked. With that exciting development, now comes the million-dollar question: how do we actually go and harness the power of such data records? In some controlled settings, healthcare providers, payers, and researchers are starting to piece together more and more data from idiosyncratic upstream systems to construct comprehensive patient records. If you pay attention to the recent trends in the healthcare tech world, you’ll see that the hope is starting to come true. It’s widely held that once equipped with the richness and completeness of the patient’s healthcare data, we can greatly improve care quality, reduce operational cost, and advance medical/drug research. The hope is that one day, we’ll be able to harmonize data from disparate sources to form a “longitudinal view” of a patient, which includes everything about the patient’s health, ranging from clinic visit, hospital stay, and medication history to immunization records, family history, and lifestyle observations. The healthcare industry started systematically digitizing healthcare data more than a decade ago.
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