In the book Predictive Machines, Prof. Ajay Agarwal, explains the economics of AI as centered around bringing down the cost of ‘predictions’. In healthcare, there are several critical patient-centric questions that can be best formulated as predictions – predict which patient is at higher risk for a certain disease, predict which patient would benefit from a novel treatment, predict which patient would be a non-responder, predict which patient will not be adherent to treatment. These patient prediction problems offer a rich testing ground to evaluate a range of AI paradigms – from foundational to emerging and the value they can provide to address these high impact patient-centric questions.
In our ZS AI lab, we focused on a particularly complex version of these patient prediction problems – identifying patients who are suffering from rare conditions (affecting <1 in 2000 people) but are currently either undiagnosed or misdiagnosed. During the talk, we will motivate why this is both a hard and an important problem to address. We will highlight a series of systematic experiments that our ZS AI lab scientists conducted to tackle the key challenges underpinning this problem including noisy labels, high data cardinality, and the temporal patient journey dynamics. We will share the meta-learnings from our research - in particular, our findings on the relevance of semi-supervised and generative models. Given the complexity of both the central prediction problem and the overall healthcare domain, we are continuing to enhance our approach along a few key dimensions – the core prediction algorithms, technologies to handle data complexities, and improving model trustworthiness to drive broader adoption with healthcare stakeholders. We will share more on these forward looking aspects. Our broader goal at ZS is to improve health outcomes for all. Realizing this goal requires the best minds to come together, share, discuss, and collaborate. During the session we would, therefore, love to hear your thoughts and experiences with analogous problems and in adjacent fields. Please join us at the session – a small step towards the broader goal of moving from the current state of ‘sick-care’ to truly healthcare.