pharmdout

Classifying and predicting active pharmaceutical ingredient shortages

Bernhard Konrad

Insight Data Science

Distributor

Manufacturer

Pharmacies, Hospitals

  • Partner is big pharmaceutical distributor

The classification problem

Distributor

Manufacturer

Pharmacies, Hospitals

The classification problem

  • Manufacturer-specific: Find alternative supplier or pay fee.
  • Industry-wide: Convince client that it's not your fault.

?

/

$

$

Predicting upcoming shortages

Time-series analysis looking for leading indicators

Past

Future

?

Today

Will      be short in the near future?

Time-series analysis looking for leading indicators

\text{Prob}(\ \ \ \ (t+\tau)\,|\,\ \ \ \ (t))
Prob(    (t+τ)∣    (t))
  1. Use Pearson's correlation to find best indicators.
  2.  
  3. Rank drugs by likelihood of upcoming shortage.

Past

Future

2 weeks

2 weeks

2 weeks

Today

Predicting upcoming shortages

Impact:

Potential in savings and increase in revenue:

+$4.5M

 

3 correctly predicted new shortages in January 2015 watch list

(0.8 expected matches if watch list is chosen randomly)

Predicting upcoming shortages

Verification:

Action:

Check internal supply, demand and price

Bernhard Konrad

Mathematical Biology

Details Classification Problem

Data from Distributor

(historic)

Data from ASHP

(current)

classes balanced

classes unbalanced

#51

#409

#594

Classifier Performance

85% of current shortages correctly classified

Unbalanced classes

Before balancing

After balancing

<

precision recall
MS 1.00 0.14
IW 0.77 1.00
precision recall
MS 0.46 0.81
IW 0.91 0.67

1:3 ratio of manufacturer:industry-wide on ASHP

Choice of classifier: Distributor

Logistic Regression

Random Forest

  • Precision: 0.69
  • Recall: 0.69
  • Precision: 0.66
  • Recall: 0.66

>

Feature importance

Important features

Less important

  • Average # manufacturers who can not deliver
  • "raw material" in reason
  • # page updates
  • Total # shortages
  • Time since last shortage
  • # affected drugs

How Indicators are used

P({\color{blue}A}) = P({\color{blue}A}\,|\,{\color{red}B})P({\color{red}B}) + P({\color{blue}A}\,|\,\overline {\color{red}B})P(\overline {\color{red}B})
P(A)=P(A∣B)P(B)+P(A∣​B​​​)P(​B​​​)

B today

A in future

From historical time series data

Examples of Indicators

Understanding Indicators

Technical Details on Indicators

  • Use Pearson's correlation coefficient to rank lagged time series for different lag values.
  • For each drug and lag, use n best predictors in forecast.
  • For each drug, average n predictions by strength of correlation.
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