Metadata-Version: 2.4
Name: kyle_out_zip
Version: 1.11.6
Summary: Multi-site clinical cohort preprocessing, predictive modeling, and model explainability pipeline
Author: Lewis Katz School of Medicine at Temple University Center for Biostatistics & Epidemiology
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: xgboost
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: shap
Requires-Dist: dalex
Requires-Dist: openpyxl

# Clinical Site Statistics Rollup & Predictive Modeling Pipeline
Lewis Katz School of Medicine at Temple University Center for
Biostatistics & Epidemiology
2026-08-04

# Project Overview

This repository contains the multi-center pipeline, statistical models,
and manuscript deliverables consolidating longitudinal Electronic Health
Record (EHR) inpatient data across five clinical health systems: -
**TEMPLE** (Lewis Katz School of Medicine at Temple University) -
**PSU** (Penn State University) - **Hopkins** (Johns Hopkins
University) - **PITT** (University of Pittsburgh Medical Center) -
**Geisinger** (Geisinger Health System)

The pipeline standardizes encounter types and procedure coding, executes
longitudinal lookback window aggregations (1-year and 2-year), maps lab
values via LOINC codes, generates Table 1 descriptive statistics
rollups, and trains patient-level traditional machine learning models
(Logistic Regression, Random Forest, HistGBM, and XGBoost) using
Stratified 5-Fold Cross-Validation on $N = 347,541$ qualifying patient
encounters.

------------------------------------------------------------------------

# Repository Structure & Extract Downloads Directory

To maintain a clean working repository, server extracts and download
archives are organized under the **`extracts/`** directory:

    kyle_out_zip/
    ├── extracts/
    │   ├── from_server/          # Drop newly downloaded server ZIP archives here (*.zip)
    │   └── archive/              # Archived historic server extracts and legacy datasets
    ├── old_logs/                 # Archived pipeline execution logs and raw run dumps
    ├── Dr_Rubin_Manuscript_Deliverables_20260729/   # Formatted manuscript tables & figures
    ├── Dr_Rubin_Manuscript_Deliverables_20260729.zip
    ├── eDERRI_Traditional_ML_Models_Manuscript.qmd  # Quarto manuscript draft (v1.11.6 with DALEX & Appendices A & B)
    ├── tasks/add/appendix_include/                   # Biostatistical & metric appendix modules
    ├── generate_manuscript_tables.py
    ├── generate_shap_4panel.py
    ├── generate_dalex_4panel.py
    ├── generate_shap_blowup_analysis.py
    ├── build_dr_rubin_deliverables.py
    ├── diagnose_lr_utilization.py
    ├── pyproject.toml
    ├── test_table1.py
    ├── test_pipeline_and_paths.py
    └── test_manuscript_data_fixes.py

All Python helper scripts (`generate_manuscript_tables.py`,
`generate_shap_4panel.py`, `generate_dalex_4panel.py`,
`build_dr_rubin_deliverables.py`, `diagnose_lr_utilization.py`)
automatically search `extracts/from_server/` and `extracts/` recursively
to detect and process the latest extract ZIP files.

------------------------------------------------------------------------

# Deliverables & Manuscript Table Specifications

### 1. Formatted Manuscript Tables (`Dr_Rubin_Manuscript_Deliverables_20260729/01_Manuscript_Tables/`)

- **`Manuscript_Tables_Combined.xlsx`**: Holds sheets matching
  Dr. Rubin’s exact shells:
  - `Table 1. Descriptive`: Baseline characteristics for all **83
    features** (Whole cohort, Readmission subset, Non-readmission
    subset).
  - `Table 2. LogReg`: Multivariable Logistic Regression risk factors
    for all **93 features** (Dual Unscaled Marginal ORs & Normalized
    ORs, 95% CIs, P-values).
  - `Table 3. Model performance`: Model discrimination and calibration
    measures (AUC, F1, Sensitivity, Specificity, Brier Score across
    model types; all cross-validation $\text{SD} < 0.01$).
- **`Manuscript_Tables_By_Site.xlsx`**: Per-site Table 1s, Table 2s, and
  master site performance metrics.

### 2. Publication Figures (`Dr_Rubin_Manuscript_Deliverables_20260729/02_Manuscript_Figures/`)

- **`Figure_Top10_DALEX_4Panel.png`**: High-resolution 300 DPI 4-panel
  summary plot comparing top 10 feature importances via DALEX variable
  dropout loss across models.
- **`Figure_Top10_SHAP_4Panel.png`**: High-resolution 300 DPI summary
  plot comparing top 10 features via SHAP Shapley values across models.
- **`Figure_Prior_Utilization_SHAP_Blowup.png`**: 4-panel architectural
  comparison for the top utilization feature (`prior_IP_OS_ED_count`)
  showing how LR (linear tail), RF (bimodal butterfly), HistGBM (smooth
  saturation), and XGBoost (regularized step) process risk differently.
- **`Figure_SHAP_4Panel.png`**: Combined 4-panel full SHAP summary
  figure.
- **`Figure_HistGBM_SHAP.png`**: Standalone 300 DPI HistGBM SHAP summary
  plot.

------------------------------------------------------------------------

# Empirical Diagnostic Findings

A 2-feature Logistic Regression diagnostic experiment
(`diagnose_lr_utilization.py`) was conducted on the full combined
dataset ($N = 347,541$ encounters): - **Full 93-Feature Model**:
$\text{AUC-ROC} = 0.787$, $\text{Brier Score} = 0.128$ - **2-Feature
Utilization Sub-Model** (`prior_IP_OS_ED_count` + 12-month
readmissions): $\text{AUC-ROC} = 0.761$, $\text{Brier Score} = 0.136$ -
**Finding**: Historical healthcare utilization alone accounts for
**96.6%** of the total discriminatory power of Logistic Regression.

------------------------------------------------------------------------

# Running Pipeline Scripts

### 1. Generating Manuscript Tables & Figures

``` bash
python generate_manuscript_tables.py
python generate_shap_4panel.py
python generate_shap_blowup_analysis.py
python build_dr_rubin_deliverables.py
```

### 2. Running Unit Tests

``` bash
pytest -v
```

------------------------------------------------------------------------

# License & Authorship

Developed by the **Lewis Katz School of Medicine at Temple University
Center for Biostatistics & Epidemiology** in collaboration with the
**eDERRI Research Group**.
