Data for: ATR-FTIR Spectral Fingerprinting of Serum with Machine Learning Enables Single-Measurement Diagnosis and Prognostic Risk Assessment for Sepsis
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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).
