AI Case Study
Takeda and ConvergeHEALTH aim to better understand how treatment-resistant depression responds to medication using deep learning models
Data scientists from Takeda and institute ConvergeHEALTH by Deloitte are using insurance claims information such as diagnoses and prescriptions to analyse patient information. By looking at disease datasets like treatment-resistant depression they aim to identify factors that highly impact patient's outcomes predictions.
The analysis happens through deep learning models that go through patient histories to predict resistance to medication and benefits from switching.
Pharmaceuticals And Biotech
"Pharmaceutical company Takeda and research and development data science institute ConvergeHEALTH by Deloitte have partnered to study patient datasets to better understand the etiology, progression and most effective therapies for difficult diseases.
Using insurance claims information including diagnoses, medical procedures and prescriptions, they ran linear and non-linear models on disease datasets like treatment-resistant depression. The goal was to identify data factors with the highest impact on predicting patient outcomes.
By combining the right data and the right questions, the organizations improved the predictability of deep learning models, allowing for the analysis of wider and more complex data sets and a better understanding of patient trajectories.
They also identified potential for these machine-learning techniques for use on other difficult to diagnose diseases, to determine what patients are more prone to these illnesses and the best courses for personalized treatment.
The organizations are using claims data sets with machine learning to build predictive models to determine the patients who may be resistant and the medications or classes of depression medications for patients to switch between.
With effective predictive models, they can work to adjust guidelines or provide digital diagnostic tools that look at patient histories to identify who would likely benefit from switching to a product earlier or potentially using it as a first-line treatment.
Patient histories include related temporal events, comorbidities, diagnostic pathways and procedures. So the organizations worked through data science and machine learning, ultimately testing deep learning methods to determine the predictability of medication switches and determine if they could isolate patterns useful for practicing medicine."
R And D
""The benefit to the patient is a shorter journey to a drug that will keep them well and less time struggling with their depression," Housman explained. "The benefit to Takeda is to be able to build tools both with guidelines or decision support systems to help physicians find the patients who can benefit from our products.”
“Predicting who will likely fail or succeed with a drug is a very challenging problem to determine given the many nuances in medical records," he added."
"The reseachers said they were encouraged that the models using different techniques demonstrated increasing predictive power. In treatment resistant depression they found that AUC went at a low of 55.1 percent in traditional linear models, to 90.2 percent using RNN deep learning models."
According to Dan Housman, ConvergeHEALTH by Deloitte: ""We leveraged the Amazon Web Services computing systems including GPU on demand servers in order to build and train the models," Housman said. "To manage the creation of pipelines and execution of machine learning models we used Deloitte's Deep Miner tools and Amazon's underlying SageMaker tools for managing execution of the machine learning jobs.”"
Insurance claims information including diagnoses, medical procedures and prescriptions, disease datasets like treatment-resistant depression.