| --- |
| language: |
| - en |
| - es |
| --- |
| |
| Biomedic Text Classifier |
|
|
| This repository contains a biomedical text classification model trained using a scikit-learn pipeline and stored in .pkl format. |
| The model is designed to process titles and abstracts from biomedical research and assign them to predefined categories. |
| import pickle |
|
|
| How to use? |
|
|
| # Cargar modelo entrenado |
| with open("classification_model.pkl", "rb") as f: |
| model = pickle.load(f) |
| |
| # Ejemplo de texto biomédico |
| sample_text = { |
| "title": "Adaptive Mechanisms in Cognitive Function Following Structural Intervention", |
| "abstract": ( |
| "The capacity of the human system to reorganize after targeted intervention " |
| "remains a subject of ongoing investigation. In this longitudinal study, " |
| "standardized assessments were conducted to evaluate how functional performance " |
| "evolves over time. A subset of individuals exhibited rapid stabilization of " |
| "higher-order processes, while others demonstrated gradual adaptation requiring " |
| "extended support." |
| ) |
| } |
| |
| # Realizar predicción |
| prediction = model.predict([sample_text["title"] + " " + sample_text["abstract"]]) |
| print("Predicción:", prediction[0]) |
