使用Sckikit-Learn和Pandas处理 Time Series
数据。
Purpose
In this project you will extract several performance metrics of an Artificial
Pancreas system from sensor data.
Objectives
Students will be able to:
- Extract feature data from a data set.
- Synchronize data from two sensors.
- Compute and report overall statistical measures from data.
Technology Requirements
Python 3.6 to 3.8 (do not use 3.9).
- scikit-learn==0.21.2
- pandas==0.25.1
- Python pickle
Project Description
In this project we are considering the Artificial Pancreas medical control
system, specifically the Medtronic 670G system. The Medtronic system consists
of a continuous glucose monitor (CGM) and the Guardian Sensor (12), which is
used to collect blood glucose measurements every 5 minutes. The sensor is
single use and can be used continuously for 7 days after which it has to be
replaced. The replacement procedures include a recalibration process that
requires the user to obtain blood glucose measurements using a Contour
NextLink 2.4 glucosemeter .
Note that this process also requires manual intervention. The Guardian Link
Transmitter, powers the CGM sensor and sends the data to the MiniMed 670G
insulin pump. The insulin pump utilizes the Smart Guard Technology that
modulates the insulin delivery based on the CGM data. The SmartGuard
Technology uses a Proportional, Integrative, and Derivative controller to
derive small bursts of insulin also called Micro bolus to be delivered to the
user. During meals, the user uses a BolusWizard to compute the amount of food
bolus required to maintain blood glucose levels. The user manually estimates
the amount of carbohydrate intake and enters it to the Bolus Wizard.
The Bolus Wizard is pre-configured with the correction factor, body weight,
and average insulin sensitivity of the subject, and it calculates the bolus
insulin to be delivered. The user can then program the MiniMed 670G infusion
pump to deliver that amount. In addition to the bolus, the MiniMed 670G
insulin pump can also provide a correction bolus. The correction bolus amount
is provided only if the CGM reading is above a threshold (typically 120 mg/dL)
and is a proportional amount with respect to the difference of the CGM reading
and the threshold.
The SmartGuard technology has two methods of suspending insulin delivery: a)
Suspend on low, where the insulin delivery is stopped when the CGM reading is
less than a certain threshold, or b) suspend on predicted low, where the
insulin delivery is stopped when the CGM reading is predicted to be less than
a certain threshold. Apart from these options, insulin delivery can also be
suspended manually by the user or can be suspended when the insulin reservoir
is running low.
Directions
Dataset
You will be given two datasets:
- a) from the Continuous Glucose Sensor (CGMData.csv) and
- b) from the insulin pump (InsulinData.csv)
The output of the CGM sensor consists of three columns: - a) data time stamp (Columns B and C combined),
- b) the 5 minute filtered CGM reading in mg/dL, (Column AE)and
- c) the ISIG value which is the raw sensor output every 5 mins.
The output of the pump has the following information: - a) data time stamp,
- b) Basal setting,
- c) Micro bolus every 5 mins,
- d) Meal intake amount in terms of grams of carbohydrate,
- e) Meal bolus,
- f) correction bolus,
- g) correction factor,
- h) CGM calibration or insulin reservoir related alarms, and
- i) auto mode exit events and unique codes representing reasons (Column Q).
The bold items are the columns that you will be using in this assignment.
Metrics to be extracted
- a) Percentage time in hyperglycemia (CGM > 180 mg/dL),
- b) percentage of time in hyperglycemia critical (CGM > 250 mg/dL),
- c) percentage time in range (CGM >= 70 mg/dL and CGM <= 180 mg/dL),
- d) percentage time in range secondary (CGM >= 70 mg/dL and CGM <= 150 mg/dL),
- e) percentage time in hypoglycemia level 1 (CGM < 70 mg/dL), and
- f) percentage time in hypoglycemia level 2 (CGM < 54 mg/dL).
Each of the above mentioned metrics are extracted in three different time
intervals: daytime (6 am to midnight), overnight (midnight to 6 am) and whole
day (12 am to 12 am).
Percentage is with respect to the total number of CGM data that should be
available each day. Assume that the total number of CGM data that should be
available is 288. There will be days such that the number of data available is
less than 288, but still consider the percentage to be with respect to 288.
You have to extract these metrics for each day and then report the mean value
of each metric over all days.
Hence there are 18 metrics to be extracted.
The metrics will be computed for two cases:
Case A: Manual mode
Case B: Auto mode
Analysis Procedure
The data is in reverse order of time. This means that the first row is the end
of the data collection whereas the last row is the beginning of the data
collection. The data starts with manual mode. Manual mode continues until you
get a message AUTO MODE ACTIVE PLGM OFF in the column “Q” of the
InsulinData.csv. From then onwards Auto mode starts. You may get multiple AUTO
MODE ACTIVE PLGM OFF in column Q but only use the earliest one to determine
when you switch to auto mode. There is no switching back to manual mode. So
the first task is to determine the time stamp when Auto mode starts. Remember
that the time stamp of the CGM data is not the same as the time stamp of the
insulin pump data because these are two different devices which operate
asynchronously.
Once you determine the start of Auto Mode from InsulinData.csv, you have to
figure out the time stamp in CGMData.csv where Auto mode starts. This can be
done simply by searching for the time stamp that is nearest to (and later
than) the Auto mode start time stamp obtained from InsulinData.csv.
For each user, CGM data is first parsed and divided into segments, where each
segment corresponds to a day worth of data. One day is considered to start at
12 am and end at 11:59 pm. If there is no CGM data loss, then there should be
288 samples in each segment. The segment as a whole is used to compute the
metrics for the whole day time period. Each segment is then divided into two
sub-segments: daytime sub-segment and overnight subsegment. For each
subsegment, the CGM series is investigated to count the number of samples that
belong to the ranges specified in the metrics. To compute the percentage with
respect to 24 hours, the total number of samples in the specified range is
divided by 288.
Note that here you have to tackle the “missing data problem”. So a particular
may not have all 288 data points. In the data files those are represented as
NaN. You need to devise a strategy to tackle the missing data problem. Popular
strategies include deletion of the entire day of data, or interpolation.
Write a Python script that accepts two csv files: CGMData.csv and
InsulinData.csv and runs the analysis procedure and outputs the metrics
discussed in the metrics section in another csv file using the format
described in Results.csv.
Submission Directions for Project Deliverables
Submit your Python script, and Results.csv file in a zip file in Coursera.
Deliverables:
- Code (‘main.py.’ code file)
- Results.csv
- Do NOT include headers
- Your Results.csv should only contain numbers and it should be a 2 X 18 matrix
- Requirements file (detailing how the requirements were fulfilled). See attached Requirements file template for an example. Save the Requirements file as a “Requirements.txt” file.
Submission Guidelines: - Please submit a zipped file containing 1, 2 and 3 deliverables as “yourfirstname_lastname_Assignment1.zip”. Do not create an additional folder; just zip the files directly.
Evaluation
We will run your Python code on a new CGMData.csv and InsulinData.csv from
another subject and generate a Results.csv file. We will have a baseline
Results.csv file generated from code on our end. We will match the results of
your code and our code. A mean square error limit of 10% will be set as the
baseline threshold for getting a full grade.
Successful execution of your code will result in 70 points
The remaining 30 points will be based on the total number of metrics you got
within the 10% error limit.
