sarveshpratap6093-beep/14

How sarveshpratap6093-beep/14 works

This is a compact research prototype, not a deployed service: a command-line workflow reads a CSV, prepares patient-level training and test data, trains and evaluates an XGBoost risk classifier, then accepts one patient's vital signs and prints risk probabilities and an alarm decision. Training and inference share one script and the fitted imputer/model state. The README also mentions MATLAB-generated signals and dataset assets, but their runtime use is not shown; the sampled source does not establish how those assets relate to the CSV input.

Main entry points: code.py Patient prediction

Components 13

Prediction workflow

Modeling and evaluation

Other components

Operator
Patient CSV

Connections 15

Patient CSV → Load dataset: provides rows code.py
Load dataset → Select vital features: selects columns code.py
Select vital features → Patient-level split: splits features code.py
Patient-level split → Median imputation: passes partitions code.py
Median imputation → XGBoost classifier: trains on imputed data code.py
XGBoost classifier → Model evaluation: produces predictions code.py
XGBoost classifier → Saved model files: saves model code.py
Median imputation → Saved model files: saves imputer code.py
Operator → Vital-sign prompts: enters vitals code.py
Vital-sign prompts → Patient prediction: calls prediction code.py
Patient prediction → Median imputation: transforms vitals code.py
Patient prediction → XGBoost classifier: predicts risk code.py
Patient prediction → Alarm decision: checks class code.py
Patient prediction → Risk and alarm output: returns risk probabilities code.py
Alarm decision → Risk and alarm output: sets alarm status code.py

More Python diagrams: 09nat/netmasquerade · abdulrysrr/caie-9618-cs-p4-algorithms-compilation-2024 · aguiarandre/etl-pipelines · ait-aecid/aecid-alert-aggregation · aiven/aiven-db-migrate

Drawn by GitDiagram on September 30, 2026 from the repository's file tree, README and source files. Read as Markdown ·

How sarveshpratap6093-beep/14 works

Main entry points: code.py Patient prediction

Connections 15

Patient CSV → Load dataset: provides rows code.py
Load dataset → Select vital features: selects columns code.py
Select vital features → Patient-level split: splits features code.py
Patient-level split → Median imputation: passes partitions code.py
Median imputation → XGBoost classifier: trains on imputed data code.py
XGBoost classifier → Model evaluation: produces predictions code.py
XGBoost classifier → Saved model files: saves model code.py
Median imputation → Saved model files: saves imputer code.py
Operator → Vital-sign prompts: enters vitals code.py
Vital-sign prompts → Patient prediction: calls prediction code.py
Patient prediction → Median imputation: transforms vitals code.py
Patient prediction → XGBoost classifier: predicts risk code.py
Patient prediction → Alarm decision: checks class code.py
Patient prediction → Risk and alarm output: returns risk probabilities code.py
Alarm decision → Risk and alarm output: sets alarm status code.py

Drawn by GitDiagram on September 30, 2026 from the repository's file tree, README and source files. Read as Markdown ·