DOI: 10.5281/zenodo.18302851
title: Data for: ATR-FTIR Spectral Fingerprinting of Serum with Machine Learning Enables Single-Measurement Diagnosis and Prognostic Risk Assessment for Sepsis

description: README: ML-Assisted Serum Fingerprinting for Sepsis

Title: ATR-FTIR Spectral Fingerprinting of Serum with Machine Learning Enables Single-Measurement Diagnosis and Prognostic Risk Assessment for SepsisAuthors: Xuejie Wang, Wei Xin, Tangdong Chen, et al.

--------------------------------------------------1. OVERVIEW--------------------------------------------------This dataset provides a complete evidence chain for the diagnosis and prognosis of sepsis using ATR-FTIR spectroscopy and machine learning. It includes raw spectra, preprocessed data, final feature matrix, and the computational codebase required for reproducibility.--------------------------------------------------2. FILE DESCRIPTIONS--------------------------------------------------

FILE 1: Raw_Data_Full (Folder/Zip)- Description: Original, unprocessed raw absorbance spectra.- Spectral Range: 4000–400 cm⁻¹- Status: No mathematical treatment applied. 

FILE 2: Processed_Dataset.CSV- Description: The preprocessed spectral matrix used for computational analysis and visualization (e.g., Figure 1).- Applied Preprocessing:     1. Rubber band baseline correction.    2. Savitzky–Golay smoothing (2nd order polynomial, 9-point window).    3. Min-Max normalization based on the Amide I band (approx. 1636 cm⁻¹).    4. -Data Range: Truncated to 4000–850 cm⁻¹- Structure:    * Column 1: Sample_ID    * Column 2: Group (Sepsis or Control)    * Remaining Columns: Processed absorbance values (a.u.) per wavenumber.- Sample Size: n = 146 (including 78 sepsis patients and 68 controls).

FILE 3: Feature_Vector_Table.CSV- Description: The final input table for Machine Learning (ML) algorithms (Extra Trees, Random Forest, etc.).- Content: Integrated area values of the six characteristic regions:    Region 1: 2997–2887 cm⁻¹    Region 2: 1721–1589 cm⁻¹    Region 3: 1589–1478 cm⁻¹    Region 4: 1275–1203 cm⁻¹    Region 5: 1184–1140 cm⁻¹    Region 6: 1140–956 cm⁻¹- Purpose: Supports results in Figures 2-4.

FILE 4: Clinical_Metadata.CSV- Description: Clinical characteristics and 28-day survival outcomes for the sepsis patients.- Sample Size: n = 83 (Patient sepsis-66 is excluded due to missing records).- Purpose: Supports the baseline characteristics (Table 4) and the prognostic scoring system (Figure 3).- Structure:    * Column 1: Sample_ID    * Column 2: 28-day survival outcomes     * Remaining Columns: Clinical markers (e.g., PLT, PCT, CRP, etc.) used for correlation and ROC analysis.

--------------------------------------------------3. COMPUTATIONAL SCRIPTS & RESULTS (CODE)--------------------------------------------------This section provides the Python codebase and detailed source data results.

FILE 5: Code_and_Results.zip│├── script_diagnosis.py         # Python script for Sepsis vs. Control classification.├── script_prognosis.py        # Python script for survival outcome prediction.│├── /Data/                     # Input datasets for machine learning scripts.│   ├── control.csv            # Preprocessed spectral data for healthy controls.│   ├── sepsis.csv             # Preprocessed spectral data for sepsis patients.│   └── prognosis_data.csv     # Combined clinical and spectral data for prognosis.│└── /Results/                  # Exhaustive model output for reproducibility.    ├── source_data_diagnosis_results.xlsx    │   # Metrics: Performance comparison, Feature Importance, Confusion Matrix,     │   # and ROC curve data for all 16 tested algorithms.    └── source_data_prognosis_results.xlsx        # Metrics: Comparison of 10 prognostic models, Naive Bayes details,         # and Precision-Recall (PR) data.

--------------------------------------------------4. SOFTWARE & ENVIRONMENT--------------------------------------------------- Spectral Acquisition: Nicolet iS50 FTIR spectrometer (Thermo Fisher Scientific).- Preprocessing Software: OMNIC 9.2 and Origin 2021.- ML Implementation: Python 3.10.0 (Scikit-learn, Pandas, Numpy, Matplotlib).Note on Environment: A requirements.txt file is provided within the Code_and_Results.zip. To install all necessary dependencies, run pip install -r requirements.txt.

--------------------------------------------------5. CONTACT--------------------------------------------------For questions regarding data or code, please contact:Guoqiang Bao (guoqiang@fmmu.edu.cn) or Lijuan Yuan (lijuanyuan@fmmu.edu.cn).