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Multi-dimensional MRI representation and privileged learning approaches to functional outcome prediction for ischemic stroke patients.

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  1. [1] § Methods › External validation dataset ↔ mrs_predict.py, lines 94–143 · score 0.64 · hot encoded, age, etiology, filling, BMI, NIHSS

Paper

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The authors' code

Python · 150 lines · 5.6 KB · no license · 1 match

  1. import pandas as pd
  2. import numpy as np
  3. import argparse
  4. import os
  5. from caller import call_mrs_prediction
  8. def main():
  9. df = load_clinical_data(args.data_path)
  10. df = df[df['gs_rankin_6isdeath'].notna() & (df['gs_rankin_6isdeath'] != 'n/a')]
  11. y = df.gs_rankin_6isdeath if args.mdl_type == 'regression' else df.gs_rankin_6isdeath > 2
  12. y = validate_y(y, args.mdl_type)
  13. assert len(df) == len(y), "Provided dataframe and output vector do not have the same length."
  15. x = preprocess_clinical_df(df)
  16. os.makedirs("output", exist_ok=True)
  17. x.to_csv(os.path.join('output', 'df.csv'), index=False)
  18. call_mrs_prediction(os.path.join('output', 'df.csv'), args.mdl_type)
  20. return
  23. def load_clinical_data(file_path):
  24. if not os.path.exists(file_path):
  25. raise FileNotFoundError(f"File does not exist: {file_path}")
  27. ext = os.path.splitext(file_path)[-1].lower()
  29. try:
  30. # CSV
  31. if ext == '.csv':
  32. df = pd.read_csv(file_path)
  33. # TSV or TXT (tab-separated)
  34. elif ext in ['.tsv', '.txt']:
  35. # Tries to infer delimiter if not standard
  36. with open(file_path, 'r') as f:
  37. line = f.readline()
  38. if '\t' in line:
  39. df = pd.read_csv(file_path, sep='\t')
  40. else:
  41. df = pd.read_csv(file_path)
  42. # Excel
  43. elif ext in ['.xls', '.xlsx']:
  44. df = pd.read_excel(file_path)
  45. # Parquet
  46. elif ext == '.parquet':
  47. df = pd.read_parquet(file_path)
  48. # Add more types here as needed, e.g. .json, .dta
  49. else:
  50. raise ValueError(f"Unsupported file extension '{ext}'. "
  51. "Supported types: .csv, .tsv, .txt, .xls, .xlsx, .parquet.")
  52. except Exception as e:
  53. raise IOError(f"Failed to read {file_path}: {e}")
  55. return df
  57. def validate_y(y, mdl_type):
  58. # Convert y to pandas Series for consistency
  59. y_ser = pd.Series(y)
  61. # 1. Check for missing values
  62. if y_ser.isnull().any():
  63. raise ValueError("y contains missing values. Please remove.")
  65. # 2. Classification
  66. if mdl_type == 'classification':
  67. unique_vals = y_ser.unique()
  68. if len(unique_vals) != 2:
  69. raise ValueError(f"For classification, y must be binary (found values: {list(unique_vals)}).")
  70. # Accept bools, [0, 1], or [True, False];
  71. # Try to convert; raise error if can't.
  72. try:
  73. y_bool = y_ser.astype(bool)
  74. except Exception as e:
  75. raise TypeError("For classification, y could not be converted to boolean. Error: {}".format(e))
  76. return y_bool
  78. # 3. Regression: integers 0–6, ordered categorical
  79. elif mdl_type == 'regression':
  80. try:
  81. y_int = y_ser.astype(int)
  82. except Exception as e:
  83. raise TypeError("For regression, y must be convertible to integer values from 0 to 6. Error: {}".format(e))
  84. # Ensure exact match
  85. if not (np.all(np.isin(y_int, np.arange(0, 7))) and np.all(y_ser == y_ser.astype(int))):
  86. raise ValueError("For regression, all y values must be integers between 0 and 6 (inclusive) without any fractions.")
  87. y_cat = pd.Categorical(y_int, categories=list(range(7)), ordered=True)
  88. return pd.Series(y_cat, index=y_ser.index)
  90. else:
  91. raise ValueError("mdl_type must be either 'classification' or 'regression'.")
  94. def preprocess_clinical_df(df):
  95. # Step 1: Drop specified columns
  96. cols_to_drop = ['participant_id', 'gs_rankin_6isdeath', 'etiology']
  97. df = df.drop(columns=cols_to_drop, errors='ignore')
  99. # Step 2: Remove any row with "n/a" in sex
  100. df = df[df['sex'] != 'n/a']
  102. # Step 3: Filter for acuteischaemicstroke == 1 and drop the column
  103. df = df[df['acuteischaemicstroke'] == 1]
  104. df = df.drop(columns=['acuteischaemicstroke'])
  106. # Step 4: Replace "n/a" with np.nan for all columns
  107. df = df.replace('n/a', np.nan)
  109. # Step 5: Standardize column names
  110. df = df.rename(columns={
  111. 'sex': 'Male_YN', # will handle this below
  112. 'age': 'Age',
  113. 'priorstroke': 'Stroke_Hx_YN',
  114. 'bmi': 'BMI_kgm2',
  115. 'nihss': 'NIHSS_Total_Initial'
  116. }, errors='ignore')
  118. # Step 6: Convert Male_YN column
  119. # Assuming M = male, F = female:
  120. if 'Male_YN' in df.columns:
  121. df['Male_YN'] = df['Male_YN'].map({'M': True, 'F': False})
  123. # Step 7: Convert Stroke_Hx_YN column to boolean
  124. if 'Stroke_Hx_YN' in df.columns:
  125. df['Stroke_Hx_YN'] = df['Stroke_Hx_YN'].map({1: True, 0: False, np.nan: np.nan})
  127. # Step 8: Race one-hot encoding
  128. races = {
  129. 'b': 'Race_BAA',
  130. 'w': 'Race_W'
  131. }
  132. # Create columns and fill with False
  133. for race_code, col_name in races.items():
  134. df[col_name] = df['race'].apply(lambda x: True if x == race_code else False if x in ['b', 'w'] else np.nan)
  135. # Drop the original race column
  136. df = df.drop(columns=['race'])
  138. # Step 9: Set correct dtypes (float for Age, BMI, NIHSS)
  139. for col in ['Age', 'BMI_kgm2', 'NIHSS_Total_Initial']:
  140. if col in df.columns:
  141. df[col] = pd.to_numeric(df[col], errors='coerce')
  143. return df
  145. if __name__ == '__main__':
  146. parser = argparse.ArgumentParser(description="3D Image Reconstruction from Embeddings")
  147. parser.add_argument('--data_path', required=True, help="Path to file containing clinical data.")
  148. parser.add_argument('--mdl_type', required=True, choices=['classification', 'regression'], help="\'classification\' or \'regression\'")
  149. args = parser.parse_args()
  150. main()

mrs_predict.py at commit 2525de5, no license · at the source

Overview

Authors: Emily Wittrup1, John Reavey-Cantwell2, Aditya S Pandey3, Dennis J Rivet II2, Badih J Daou3, Kayvan Najarian1,4,5,6
  1. Gilbert S. Omenn Computational Medicine and Bioinformatics, University of MIchigan, Ann Arbor, MI USA
  2. Department of Neurosurgery, Virginia Commonwealth University, Richmond, VA USA
  3. Department of Neurological Surgery, MIchigan Medicine, Ann Arbor, MI USA
  4. Department of Emergency Medicine, MIchigan Medicine, Ann Arbor, MI USA
  5. MIchigan Institute for Data and AI in Society (MIDAS), University of MIchigan, Ann Arbor, MI USA
  6. Center for Data-Driven Drug Development and Treatment Assessment (DATA), University of MIchigan, Ann Arbor, MI USA
Institutions: University of Michigan (United States); Virginia Commonwealth University (United States); Virginia Commonwealth University Medical Center (United States); Michigan Medicine (United States)
Journal: NPJ digital medicine, volume 9, issue 1, article 557
Dates: received 2 January 2026; accepted 24 April 2026; published online 10 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41746-026-02708-0 · PMID 42106496 · PMCID PMC13381960 · OpenAlex W7160705729
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), stroke (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity
Keywords: Computational biology and bioinformatics, Medical research, Neurology, Neuroscience
Topic: Acute Ischemic Stroke Management (Epidemiology, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

kayvanlabs/ischemic_stroke_mrs_prediction

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2525de52cbb2717ba74051bbc84d1c3b3398c140, 19 December 2025
Languages: Python (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), pandas (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/kayvanlabs/ischemic_stroke_mrs_prediction

The paper's code and data availability statement is in the Data section.

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41746-026-02708-0.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 31 references.

Cite

This paper

Wittrup, E., Reavey-Cantwell, J., Pandey, A. S., Rivet II, D. J., Daou, B. J., & Najarian, K. (2026). Multi-dimensional MRI representation and privileged learning approaches to functional outcome prediction for ischemic stroke patients. NPJ digital medicine, 9(1), 557. https://doi.org/10.1038/s41746-026-02708-0

BibTeX

@article{wittrup2026multi,
author = {Wittrup, Emily and Reavey-Cantwell, John and Pandey, Aditya S and Rivet II, Dennis J and Daou, Badih J and Najarian, Kayvan},
title = {{Multi-dimensional MRI representation and privileged learning approaches to functional outcome prediction for ischemic stroke patients}},
journal = {NPJ digital medicine},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {557},
publisher = {Nature Publishing Group},
issn = {2398-6352},
doi = {10.1038/s41746-026-02708-0},
url = {https://doi.org/10.1038/s41746-026-02708-0},
pmid = {42106496},
pmcid = {PMC13381960}
}

RIS

TY - JOUR
AU - Wittrup, Emily
AU - Reavey-Cantwell, John
AU - Pandey, Aditya S
AU - Rivet II, Dennis J
AU - Daou, Badih J
AU - Najarian, Kayvan
TI - Multi-dimensional MRI representation and privileged learning approaches to functional outcome prediction for ischemic stroke patients
T2 - NPJ digital medicine
J2 - NPJ Digit Med
PY - 2026
DA - 2026/05/10
VL - 9
IS - 1
SP - 557
SN - 2398-6352
PB - Nature Publishing Group
DO - 10.1038/s41746-026-02708-0
UR - https://doi.org/10.1038/s41746-026-02708-0
LA - en
ER -

CSL-JSON

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"family": "Wittrup",
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