Machine learning can be used for tasks that are labor intensive, tedious, or cannot be done at scale by humans. Three use cases are described: counting parasites in petri dishes to detect 70-95% accurately, classifying clinical documents to label 90% accurately, and coding doctor's notes to map to ICD-10 codes with 75-95% accuracy using rules-based approaches. Overall, initial results were promising but further work is needed to integrate models into products and ensure consistent performance.

1. In Research and Development
October 15th
Machine Learning

2. Why Machine Learning?
We want the machine to do something:
1. we can’t do
2. we prefer not to do
3. we can’t do at scale
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3. Machine Learning Use Cases
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Counting
Parasites
Classifying
Documents
Coding Doctors
Notes

4. Use Case 1 - Counting Parasites
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The Problem
Counting the parasites for our animal
health products is labor-intensive,
tedious, cumbersome and error-prone
1. Evaluate the practical feasibility of endo-parasites (worms) counting from videos
2. Create a prototype of a model that will count worms from a video of a single
segment of a Petri dish with at least 90% precision
Our Goal
• Detected between 70% - 95% of parasites across different petri dish segments
• Additional work required to move and count the entire petri dish
• Did not detect dead worms
Initial Results

5. Use Case 2 – Classifying Clinical Documents
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The Problem
Building the Trial Master File (TMF) is a
laborious task that requires a lot of
time and manpower. Mislabeled
documents may take time to locate
during auditing
Evaluate the feasibility of machine learning for classifying and labeling documents,
especially when documents are scanned and partially handwritten
Our Goal
• Accurately classified 90% of documents to their labels after applying OCR
• Additional work required to integrate the machine learning model with the product
we use for TMF
Initial Results

6. Use Case 3 – Coding Doctor’s Notes
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The Problem
Creating population health metrics
from certain global EMR systems is
difficult since they are not coded and
only have unstructured doctors notes
1. Evaluate the feasibility of mapping unstructured doctors notes to ICD-10 codes
2. Achieve > 95% accuracy that can be verified by physicians and specialists
Our Goal
• Initial machine learning approach achieved very poor results
• 75% - 95% accuracy was achieved through a rules-based approach with regular
expressions.
Initial Results
Chief Complaint:
Agitation
The patient complains of
feeling agitated most of
time. Associated symptoms
include anxiety.
Patient is not currently
medication.

7. Lessons Learned
• Finding your Precision vs Recall needs
• Language Handling
• Durability of results
• Trust & Confidence in results
• Vendor software integration
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