- The fusion model derives its inputs from the base models below, taking its results and feeding it into a - classifier to determine most likely results. Across all of our available data, the fusion model performed - the best, returning the most predicted findings matching the real findings. To read more about how it - works and how we developed it, you can reach the{' '} - fusion model documentation. -
{!fusionInference &&Try running the demo!
} {fusionLoading &&+ The fusion model derives its inputs from the base models below, taking its results and feeding it into a + classifier to determine most likely results. Across all of our available data, the fusion model performed + the best, returning the most predicted findings matching the real findings. To read more about how it + works and how we developed it, you can reach the{' '} + fusion model documentation. +
- The tabular model takes the tabular data that has been inputed (temperature, heartrate, etc.) and - attempts to classify into one of five possible options. In a proper setting, this would be tens to - hundreds of inputs that could be taken it, improving the models ability to predict a class, however - due to a limitation of data, we only have these inputs. To read more about the tabular model, see the{' '} - tabular model documentation. -
{!tabularInference &&Try running the demo!
} {tabularLoading &&+ The tabular model takes the tabular data that has been inputed (temperature, heartrate, etc.) and + attempts to classify into one of five possible options. In a proper setting, this would be tens to + hundreds of inputs that could be taken it, improving the models ability to predict a class, however + due to a limitation of data, we only have these inputs. To read more about the tabular model, see the{' '} + tabular model documentation. +
- The text model takes in the input from the notes, processed via a modified BioClinical Bert model. The - model is trained on clinician notes describing the patient's present history of illness, with the - expectation that it will learn the symptoms of the four possible options, and no findings if it cannot - be determined. To read more about how it works and how we developed it, you can reach the{' '} - text model documentation. -
{!textInference &&Try running the demo!
} {textLoading &&+ The text model takes in the input from the notes, processed via a modified BioClinical Bert model. The + model is trained on clinician notes describing the patient's present history of illness, with the + expectation that it will learn the symptoms of the four possible options, and no findings if it cannot + be determined. To read more about how it works and how we developed it, you can reach the{' '} + text model documentation. +
- The image model utilizes chest xrays, preprocessed into a lower resolution. It utilizes EfficientNet - image classification, resulting in an extremely powerful classification model. To read more about how - it works and how we developed it, you can reach the{' '} - image model documentation. -
{!imageInference &&Try running the demo!
} {imageLoading &&+ The image model utilizes chest xrays, preprocessed into a lower resolution. It utilizes EfficientNet + image classification, resulting in an extremely powerful classification model. To read more about how + it works and how we developed it, you can reach the{' '} + image model documentation. +
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