ALAMEDA Predictor Variable Timeseries Classification Toolkit (1.0.0)

API description for the ALAMEDA Predictor Variable Timeseries Classification Toolkit. This description presents the RESTful interface by which the suite of algorithms developed for the classification of the patient disease status based on month-long data collected from wearables and PROs.

Each algorithm receives as input a CSV file containing timestamped results of PROs collected from a patient, as well as information aggregated at day level determined from wearables (e.g. sum of steps, percentages of time per day in each type of activity intensity level, detected activties or exercises, objectively measured stiffness, brady / dyskenisia, detected fall).

Algorithms are selectable by a model and are applicable per disease (PD, MS, Stroke). The algorithms can be run in evaluation or prediction mode. In evaluation mode, the input CSV must contain both predictor and target variables, while the result CSV will report on the performance metrics for each target variable. In prediction mode, the input CSV contains only predictor variables, while the result CSV contains one or more target variables together with the probability distribution for each target variable value.


VarCls

Prepare execution of an ALAMEDA algorithm

Prepare the execution of an ALAMEDA variable timeseries classification algorithm by uploading the required CSV input file. The call will return an ID of the file for use in the algorithm invocation. The algorithm to be executed is identified through *model* and *disease* parameters. The validity of the CSV input file is checked against these.

post https://varcls.alamedaproject.eu/v1 /prepare_algorithm Try out

formData

body required

Launch a classification algorithm into execution

This is the main call which launches a classification algorithm into execution. The type of the algorithm is determined by the _model_, _disease_ and *fileID* parameters. The algorithm can be launched in *classification* or *evaluation* mode. The former returns probability distributions over the possible values of each target variable known to the algorithm. The latter returns performance metrics for all the target variables included in the uploaded CSV file.

post https://varcls.alamedaproject.eu/v1 /apply_algorithm Try out

body

body required
Config of the call specifying a structure containing model name, disease name and uploaded input file ID
{
    "model": "",
    "disease": "",
    "mode": "",
    "fileID": ""
}

Retrieve status and result of a launched algorithm instance identified by algID

get https://varcls.alamedaproject.eu/v1 /apply_algorithm/{algID} Try out

path

algID required
integer
ID of algorithm instance to return

Stop a running execution or inform the server that the results of the algorithm invocation identified by algID can be safely discarded

delete https://varcls.alamedaproject.eu/v1 /apply_algorithm/{algID} Try out

path

algID required
integer
ID of algorithm instance to return