Predicting Health Timelines with Transformer

Our lives can be seen as a series of events unfolding over time, and this can be modeled using Transformers, an AI architecture that has been highly successful in large language models. In our npj Digital Medicine article we explore how they can be applied to personalized healthcare journeys.
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The written word, composed of sequences of letters or tokens, has demonstrated immense potential through the rise of large language models (LLMs) like ChatGPT. These models, grounded in transformer architectures, have brought AI into the mainstream, shaping how society views intelligent systems. The conversational abilities of such AI systems have garnered widespread appreciation, as they offer a clear, understandable interface for the general public.

But let’s shift focus to something more relatable: our daily lives. Every moment of our existence can be seen as a sequence of events, much like the letters forming words. Whether it's waking up, preparing breakfast, commuting to work, or simply spending time on mundane activities—each action represents a discrete event in the timeline of our lives. Even periods of inactivity, such as an hour spent watching pigeons in the park, constitute an event within this temporal sequence.

Viewing life as a timeline of events reveals a parallel to how we understand text: both are sequences that can be modeled. This means that the same advanced technology we use for language models can be applied to model life events—transformers. These AI models, when applied to life’s timelines, can offer profound applications, such as predicting future events based on past sequences, much like how LLMs predict the next word in a sentence.

In healthcare, this concept is even more transformative. Patient interactions with the healthcare system, from doctor visits to lab results, form a detailed timeline of events. By applying transformer models to these patient health timelines (PHTs), we can anticipate patient outcomes, predict future health events, and recommend interventions. This idea has been explored in recent research, such as in the ETHOS system (Enhanced Transformer for Health Outcome Simulation), which leverages tokenized patient histories to predict future health trajectories using zero-shot learning.

Just as LLMs allow us to ask questions and receive coherent answers, transformer-based models in healthcare could enable clinicians to input a patient’s current health data and receive forecasts of their future conditions. The predictive power of these models could revolutionize care by providing proactive interventions, improving outcomes, and optimizing healthcare resources. The potential is immense, not just for individuals, but for population health management, as these models can scale to analyze large datasets and identify trends across patient populations.

This seamless transition from modeling text to modeling life and health timelines opens up exciting new possibilities for AI’s role in everyday decision-making and patient care. The boundaries between language and life events blur, showing us that the same technological breakthroughs driving conversational AI can also unlock the future of predictive, personalized healthcare.

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Artificial Intelligence
Mathematics and Computing > Computer Science > Artificial Intelligence
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  • npj Digital Medicine npj Digital Medicine

    An online open-access journal dedicated to publishing research in all aspects of digital medicine, including the clinical application and implementation of digital and mobile technologies, virtual healthcare, and novel applications of artificial intelligence and informatics.

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