At Google I / O, Sundar Pichai, CEO of the company, described how the company uses its artificial intelligence and machine learning infrastructure to better predict health outcomes. A number of cutting-edge directions in the field are emerging, but most medical data is unstructured and has a lot of trouble.
For Google, the interest in healthcare is more about proving its models and algorithms in the field. This is also a natural extension of the Google Cloud Platform. Google is also working with Fitbit to develop data and health APIs. In view of this, Google artificial intelligence is very interested in the field of healthcare.
In a paper published during the Google I / O conference, the company described how it uses electronic health records. The company noted that it is working with Stanford University, the University of California at San Francisco, and the University of Chicago to explore how deep learning models can be applied to patients in hospitals. The key development is that Google is looking to use data. Data preparation itself can have a lot of analytical work.
Google's article describes the data preparation challenges:
We assume that these technologies will translate well into health care; specifically, deep learning methods can include the entire EHR, including free-text notes, to provide predictions for a wide range of clinical problems, and that it is more accurate than the highest level of traditional predictions. model. Our core point is that instead of directly aligning EHR data, mapping it into a set of highly organized, structured predictors, and then feeding those variables into a statistical model, we might as well learn to synchronize the input through direct feature learning. Predict medical incidents.
Google said the company's model can scale and accurately predict fatality in predicting accidental readmissions, discharges, and hospitalizations. As previously reported by ZDNet, artificial intelligence is being used to better improve care while reducing costs. See also:
Google pointed out that it is of statistical importance and is only a beginning. The predictive and deep learning models are also only using retrospective data.
In fact, there are many other places in healthcare that can use artificial intelligence and machine learning. E.g:
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