inTrigue Case Study
This case study derives from a Volv engagement, which started in July 2017. A pharmaceutical company approached Volv Global to develop a prediction model to identify additional patients suffering from the rare disease treatable by its specialist medicine, using its rare event finding technology.
From this original engagement, the inTrigue methodology was born.
This pharmaceutical company faced four difficulties:
To compound these four difficulties, the pharmaceutical company was unable to provide any single medical record for an already diagnosed patient. In the absence of any examples, Volv undertook to build a methodology that would learn about the disease automatically from publicly available data.
We made a number of statements of intention as follows: to deliver patient cohort selection performance above current, manual best practice,; to derive new insights into diagnosing the disease; to become better at diagnosis than experienced specialist clinicians.
Deriving diagnostic models for rare diseases manually, based on input from experts, is problematic for several reasons, including the evolving nature of experts’ understanding, the complexity of disease processes, and cognitive biases associated with human decision-making [Elstein, Schwarz 2002].
The Volv approach lifts clinical accuracy for the detection of at-risk patients to world-class levels of precision.
It outperformed the existing, state-of-the-art prediction models with a two thirds reduction in error rate. The sensitivity of prediction models to false positives is measured using Area Under the ROC Curve (‘AUC’). In terms of AUC, 1.00 is perfect accuracy (no false positives), and Volv’s prediction model achieved a rating of 0.935, which is well above the human performance level of 0.76 and the best alternative models (Kopcke and Miotto).
When the system and methodology were used to augment the physician the accuracy rose to 0.975.
