ratschlab/circews

circEWS public code

circews's Issues

"Code is required from following article for SHAP PDP with histogram and regression line"

Respected sir,
I am Junaid, I want the code to draw the figure 4b,c from following picture in this article "https://www.nature.com/articles/s41591-020-0789-4#MOESM2".

I am looking for it since several weeks but I was not able to draw it, please provide me whole code of SHAp library for Partial dependence plot with nonparamateric regression line and SD, + histograms right and above the main scatter plot.

Thanks.

https://www.nature.com/articles/s41591-020-0789-4#MOESM2

Sir, can I get whole code junk for SHAP partial dependence plot (scatterplot)+nonparametric regression line with SD with right and top histogram. I am very confused about it and i like it. Please provide me Thanks.

Additional csv files

First of all gratulations to your paper!

I am currently trying to get the model you made available on Github (https://github.com/ratschlab/circEWS) to work for the above-mentioned paper. In the course of this, I noticed that you also used other CSV files in addition to the HiRID and MIMIC III data.
In the script "mimic_paths.py", for example, other CSV files were used in addition to the MIMIC III, e.g. in the folder "ref_lists", "external_validation" or "misc_derived/stephanie". It would be very helpful for me if you can provide all CSV files used outside HiRID and MIMIC III. My understanding is that these are essential to make the model work with validation.

Thanks a lot in advance!

How to screen patients admitted due to trauma and query blood transfusion information?

Hi,
I am using the HIRID database to screen patients admitted for trauma, but the database does not seem to contain information about the diagnosis of admission or the type of treatment the patient receives. Can I screen patients admitted to the hospital for trauma through other channels?

In addition, by searching keywords, I retrieved the following IDs related to blood transfusion in the table:
1000050, 1000744 (Transfusion of plasma (FFP))
1000100, 1000743 (Intravenous blood transfusion of packed cells)
1000245, 1000201 (Platelet transfusion)

But` I am not sure if I have missed other fluids related to blood transfusion.

regarding circulatory status

Hi there,

It's great work you guys published on Nature Medicine. And I especially appreciate it that you shared both HiRID and codes with the community.

I'm working with HiRID using the merged and imputed data as a first step, and hopefully you can provide some suggestions.
Currently, I am trying to generate circulatory status using the merged data provided in HiRID, but I found it is tethered to "Chunked" data generated during the preprocessing stage, and drugs associated with vasopressor or inotropes are not available in these merged data either. While trying to rerun the preprocessing stage, I found "preproc_utils" is missing. Could you direct me to a via approach to move this forward? Appreciate that!

Best,
Ran

Missing files and errors in `external_validation` with MIMIC-III

Hi! We are trying to run your code in the external_validation folder. So far come up against two issues, we do not have any of the data in mimic_paths.py in the leomed section. Is this data possible to access or private? Without it we cannot run run_mimic_prep.py. Also, on the current version of MIMIC-III, we get this error on the age calculation:

Traceback (most recent call last):
  File "np_datetime.pyx", line 736, in pandas._libs.tslibs.np_datetime.add_overflowsafe
OverflowError: value too large

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/harryw/eva/circEWS/external_validation/run_mimic_prep.py", line 10, in <module>
    em.build_static_table(version=version)
  File "/home/harryw/eva/circEWS/external_validation/extract_data_from_mimic.py", line 725, in build_static_table
    table["Age"] = (pd.to_datetime(table["ADMITTIME"]) - pd.to_datetime(table["DOB"])) / np.timedelta64(24 * 365, "h")
                    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
    return method(self, other)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/arraylike.py", line 194, in __sub__
    return self._arith_method(other, operator.sub)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/series.py", line 6126, in _arith_method
    return base.IndexOpsMixin._arith_method(self, other, op)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/base.py", line 1382, in _arith_method
    result = ops.arithmetic_op(lvalues, rvalues, op)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 273, in arithmetic_op
    res_values = op(left, right)
                 ^^^^^^^^^^^^^^^
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
    return method(self, other)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/arrays/datetimelike.py", line 1457, in __sub__
    result = self._sub_datetime_arraylike(other)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/arrays/datetimelike.py", line 1154, in _sub_datetime_arraylike
    return self._sub_datetimelike(other)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/harryw/.conda/envs/eva/lib/python3.12/site-packages/pandas/core/arrays/datetimelike.py", line 1169, in _sub_datetimelike
    res_values = add_overflowsafe(self.asi8, np.asarray(-other_i8, dtype="i8"))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "np_datetime.pyx", line 709, in pandas._libs.tslibs.np_datetime.add_overflowsafe
  File "np_datetime.pyx", line 743, in pandas._libs.tslibs.np_datetime.add_overflowsafe
OverflowError: Overflow in int64 addition

This can be fixed by adding the following line to get rid of the dates that are censored down into the 1800s in MIMIC-III:

table = table[pd.to_datetime(table["DOB"]) > pd.Timestamp("1950-01-01")]

But already this means we must have a different cohort which makes reproducing difficult.

Thanks!