AI Case Study
Pharmaceutical company identifies warnings for non-Hodgkin’s lymphoma patients requiring change of treatment using machine learning
An undisclosed Pharmaceutical company has leveraged artificial intelligence to better understand non-Hodgkin’s lymphoma's clinical progression and identify the best personalised treatment for each stage. The company applied machine learning to electronic health records (EHR) and other data to map out the warnings that indicate that patients need to switch to a later line of therapy. The team used an automated-feature-discovery (AFD) machine learning engine to test million hypothesis based on internal and external data. The system tried to find a statistically significant correlation between variables in patient data and transition to a later line of therapy. The technology enabled them to identify and isolate the variable combinations that predict transitions.
Pharmaceuticals And Biotech
"Let’s look at the case of a pharmaceutical company we worked with. It applied machine learning to EHR and other data to study the characteristics or triggers that presage the need for patients with a type of non-Hodgkin’s lymphoma to transition to a later line of therapy. The company wanted to better understand the clinical progression of the disease and what treatment best suits patients at each stage of it. The company’s story highlights three guiding principles other pharma companies can use to successfully deploy advanced analytics in their own organizations.
The pharma company brought in teams from its brand, medical, and business intelligence groups to generate hypotheses on the likely predictors that patients would have to move from one therapy to another and the triggers of those transitions. For example, in trying to hypothesize what drives fast or slow disease progression, the medical group contributed its clinical understanding of the disease, the brand team offered its detailed understanding of the company’s treatment offerings and how physicians use them, and the business intelligence team presented the analytical methods and datasets it had already used to shape the current understanding of treatment and disease courses.
The pharma company’s analytics group realized that its internal data didn’t capture the variables likely to predict patient transitions in sufficient depth. The group therefore pursued a strategy in which it used internal and external data, combining an oncology-specific, integrated, structured EHR data set with some analysis replicated and validated on claims data.
All the data were stitched together and fed into an automated-feature-discovery (AFD) machine learning engine that allowed the company to test millions of hypotheses within hours. The engine explored every possible variation of the patient data to see if any variables had a statistically significant correlation with the transition to a later line of therapy. The insights gleaned from subject-matter experts helped ensure that the AFD results were clinically relevant. For example, when results indicated that an elevated liver function marker correlated with disease progression, medical officers confirmed that, although it wasn’t a factor they’d previously considered, it was clinically possible.
The pharma company’s analytics group tested more than 200 lab values, major chronic comorbidities, and elements of medical history. Machine learning helped identify and isolate the critical variable combinations that predict transitions. Models were validated and refined to avoid noise and reduce the number of variables.
After weeks of iteratively learning and validating, a model was successfully developed to predict progression from initial diagnosis to later lines of therapy. Specifically, machine learning was used to extract features and triggers from the patient’s treatment, lab, and medication history, and the validated features were used to score and rank patients by expected likelihood of transition."
"The models uncovered many critical insights, including:
* Abnormalities in select lab results, such as the elevated liver function marker, increased the likelihood of a patient transitioning to the next line of therapy by in as much as 140% in some cases.
* Patients on maintenance therapy were 20% less likely to transition to the next line of therapy."
Combined data were "fed into an automated-feature-discovery (AFD) machine learning engine" in order to automatically test a large number of hypotheses. Machine learning was also used to "extract features and triggers from the patient’s treatment, lab, and medication history, and the validated features were used to score and rank patients by expected likelihood of transition."
Digital Data Management
"The growing availability of real-world data has generated tremendous excitement in health care. By some estimates, health data volumes are increasing by 48% annually, and the last decade has seen a boom in the collection and aggregation of this information. Among these data, electronic health records (EHRs) offer one of the biggest opportunities to produce novel insights and disrupt the current understanding of patient care.
But analyzing the EHR data requires tools that can process vast amounts of data in short order. Enter artificial intelligence and, more specifically, machine learning, which is already disrupting fields such as drug discovery and medical imaging but only just beginning to scratch the surface of the possible in health care."
"The pharma company’s analytics group realized that its internal data didn’t capture the variables likely to predict patient transitions in sufficient depth. The group therefore pursued a strategy in which it used internal and external data, combining an oncology-specific, integrated, structured EHR data set with some analysis replicated and validated on claims data."