The Solution

Based on the precedent of over 2.5 million client progress records and an abundance of unstructured data Forest Grove developed a machine learning model that alerts LWB to clients that are most at risk of experiencing a behavioural or medical incident. 

Leveraging KNIME, the ML model carried out the following key processes:

1. Automated data Cleansing and missing value imputation
2. Event tagging and analysis
3. Supervised and unsupervised machine learning processes
4. Tiered client ‘at risk’ classification

Proven Results

The predictive model has enabled LWB to determine at-risk clients prior to an incident with a high success rate.  A previously unknown correlation between clients who experienced a move (house, facility etc) and a considerable jump in incidents has enabled LWB to focus resources on these at-risk clients.  Key solution Benefits:

• High degree of success in predicting potential at-risk clients prior to a medical or behavioural incident
• Ability to uncover previously unforeseen trends
• Streamlining of resource management & allocation processes