drug_couse 0.0.1
Analyze drug couse
Requires Python: >=3.11
Keywords: Associated rule mining, Pharma analytics, Patient analytics, Drug couse

# Drug couse

Method(s) to analyze patient drug couse.

- [Drug couse](#drug-couse)
  - [Installation](#installation)
    - [Dependencies](#dependencies)
  - [Associated rule mining](#associated-rule-mining)
    - [1. Association Rule Mining (ARM)](#1-association-rule-mining-arm)
      - [Metrics](#metrics)
      - [example\\arm.py\\ Output crosssection](#examplearmpy-output-crosssection)
      - [example\\arm.py\\ Log](#examplearmpy-log)

## Installation

### Dependencies

```
uv pip install --system fastparquet matplotlib mlxtend openpyxl pandas
or
uv pip install --system --upgrade matplotlib pandas

https://test.pypi.org/project/drug-couse/

run examples\arm.py
```



## Associated rule mining

`Question`: Likelihood of a patient taking drug A also taking drug B?


### 1. Association Rule Mining (ARM)

ARM is an `Apriori Algorithm` similar to market basket analysis where you can identify associations between different drugs. You can look for rules like "if a patient takes Drug A, they are likely to take Drug B".

#### Metrics

1. **Support**

    Relative fequency of a particular combination of drugs occuring.

    Support(A,B) = Count(A and B) / Total patients

    Support = 0.3 means 30% of patients take both drugs


2. **Confidence**

    Probability that Drug B is taken given Drug A is taken.

    Confidence(A→B) = Count(A and B) / Count(A)

    Confidence = 0.8 means 80% of patients who take Drug A also take Drug B

    Confidence is a measure of the reliability of the rule.


3. **Lift**

    Measures the deviation from independence. How much more likely Drug B is when Drug A is present vs. baseline (random chance).

    Lift(A→B) = Confidence(A→B) / Support(B)

    Lift = 1: Completely independent or no association (random)<br>
    Lift > 1: Positive association (Drug A increases likelihood of Drug B)<br>
    Lift < 1: Negative association (Drug A decreases likelihood of Drug B)<br>
    Lift = 2.0: Patients taking Drug A are 2x more likely to take Drug B than average


4. **Conviction**

    How much more often the rule would be incorrect if Drug A and Drug B were independent.

    Higher values: Stronger rules


**What to Look For**

- High Confidence Rules (>0.7): Strong predictive relationships
- High Lift Rules (>2.0): Interesting associations beyond random chance
- Reasonable Support (>0.1): Patterns that affect a meaningful number of patients



#### example\arm.py\ Output crosssection

**Confidence Matrix**

In real data the confidence matrix will show relationships most likely in clusters.

![](./img/confidence_matrix.png)


The lift relationship shall follow similar pattern.

![](./img/lift_matrix.png)


#### example\arm.py\ Log

```py
🚀 Creating Complete Drug Relationships Matrix:
============================================================
🔄 Preprocessing data...
✅ Data preprocessed: 1000 patients, 12 unique drugs

📋 Transaction Matrix Shape: {transactions.shape}
Sample of transaction matrix:
   Drug 1  Drug 2  Drug 3  Drug 4  Drug A  Drug B  Drug C  Drug D  Drug W  Drug X  Drug Y  Drug Z
0   False   False   False   False   False   False   False   False   False    True    True   False
1   False   False   False   False   False   False   False   False   False    True    True   False
2    True    True    True   False    True   False   False   False   False   False   False   False
3   False   False   False   False    True    True   False    True   False   False   False   False
4   False   False   False   False   False   False   False   False   False    True    True   False

📈 Drug Usage Statistics:
==================================================
Total patients: 1000
Total unique drugs: 12

Top 10 most prescribed drugs:
  Drug X: 366 patients (36.6%)
  Drug Y: 363 patients (36.3%)
  Drug 1: 362 patients (36.2%)
  Drug 2: 355 patients (35.5%)
  Drug A: 351 patients (35.1%)
  Drug B: 351 patients (35.1%)
  Drug 3: 52 patients (5.2%)
  Drug 4: 49 patients (4.9%)
  Drug D: 48 patients (4.8%)
  Drug C: 47 patients (4.7%)
🔍 Finding frequent itemsets with min_support=0.2...
✅ Found 9 frequent itemsets

📊 Top 10 Frequent Itemsets:
   support          itemsets
4    0.366          (Drug X)
5    0.363          (Drug Y)
0    0.362          (Drug 1)
1    0.355          (Drug 2)
2    0.351          (Drug A)
3    0.351          (Drug B)
8    0.342  (Drug X, Drug Y)
6    0.329  (Drug 2, Drug 1)
7    0.329  (Drug B, Drug A)
📊 Generating association rules with min_confidence=0.5...
✅ Generated 6 association rules
🔄 Calculating relationships for ALL drug pairs...
   Analyzing 12 drugs = 132 possible relationships
✅ Calculated 92 drug-to-drug relationships
✅ Saved parquet file: complete_drug_relationships_matrix.parquet
❌ Error exporting to Excel: [Errno 13] Permission denied: 'complete_drug_relationships_matrix.xlsx'
✅ Saved as CSV and Parquet: complete_drug_relationships_matrix.csv/.parquet

📈 Complete Analysis Summary:
   Total drug pairs analyzed: 92
   High confidence relationships (60%+): 18
   Strong associations (Lift 2.0+): 30
   High priority relationships: 18

📊 File Size Comparison:
========================================
   Excel: 0.03 MB
 Parquet: 0.01 MB
     CSV: 0.01 MB

📁 Output Files Generated:
   📊 Excel File: complete_drug_relationships_matrix.xlsx
      • Multiple sheets with filtered views
      • Pivot tables for easy analysis
      • Summary statistics
   📦 Parquet Files: Multiple .parquet files for different views
      • Faster loading for large datasets
      • Better compression than CSV
      • Preserves data types

🔄 Demonstration: Loading Parquet Data
✅ Loaded parquet file: complete_drug_relationships_matrix.parquet
   Shape: (92, 14)
   Columns: ['Drug_A', 'Drug_B', 'Patients_A', 'Patients_B', 'Patients_Both', 'Support', 'Confidence', 'Confidence_%', 'Lift', 'Leverage', 'Conviction', 'Rule_Strength', 'Clinical_Priority', 'Relationship_Type']
   Sample of loaded data:
   Drug_A Drug_B  Confidence_%  Lift Rule_Strength
Drug D Drug A         100.0 2.849   Very Strong
Drug D Drug B         100.0 2.849   Very Strong
Drug C Drug A         100.0 2.849   Very Strong

============================================================
🎉 Enhanced Analysis Complete with Dual Export!
📋 Key Features:
   ✅ Excel export with multiple sheets
   ✅ Parquet export for large datasets
   ✅ Automatic handling of Excel row limits
   ✅ File size comparisons
   ✅ Easy data loading utilities
============================================================
```