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David Laprade
Standard Model Biomedicine
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Shaun Porwal
Standard Model Biomedicine
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Founder's Pitch
"Transform patient data dynamics into predictive models for healthcare systems to anticipate and guide treatment outcomes."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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2/4 signals
Series A Potential
4/4 signals
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arXiv Paper
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Why It Matters
This research shifts the paradigm from treating patient data as static text documents to dynamic systems, representing a significant shift in personalizing medicine by predicting how patient conditions evolve over time, which is crucial for enhancing treatment outcomes.
Product Angle
By integrating SMB-Structure into existing electronic health record (EHR) systems, it can provide real-time insights into patient trajectories, allowing healthcare providers to tailor interventions and monitor progress more effectively.
Disruption
This approach can significantly disrupt current electronic health records and clinical decision support systems by shifting from static data interpretation to dynamic prediction models, hence improving real-time decision-making in clinical settings.
Product Opportunity
The healthcare sector is increasingly emphasizing personalized patient care; technologies that enhance this capability by accurately predicting patient outcomes will be crucial. Hospitals and clinics would pay for technology that improves patient management and outcomes, especially for chronic and complex diseases.
Use Case Idea
Develop a clinical decision support tool that uses SMB-Structure's predictive capabilities to optimize personalized treatment plans for complex diseases like cancer by accurately predicting disease progression.
Science
The paper introduces SMB-Structure, a novel model that treats patient data as dynamic trajectories rather than static text. This involves using Joint-Embedding Predictive Architectures (JEPA) to predict disease trajectory dynamics in latent space, significantly outperforming traditional token prediction models.
Method & Eval
The model was tested on two large cohorts: oncology patients at Memorial Sloan Kettering and pulmonary embolism patients. It achieved competitive performance in capturing disease dynamics over traditional methods by focusing on trajectory-level reasoning.
Caveats
The model relies on significant computational resources for training and validation on large datasets, which may limit accessibility. There are also potential biases if the training data is not representative of the broader patient population.
Author Intelligence
Irsyad Adam
LEAD Standard Model Biomedicine
irsyad@standardmodel.bio
Zekai Chen
LEAD Standard Model Biomedicine
zach@standardmodel.bio
David Laprade
Standard Model Biomedicine
Shaun Porwal
Standard Model Biomedicine
David Laub
Standard Model Biomedicine
Erik Reinertsen
Standard Model Biomedicine
Arda Pekis
Standard Model Biomedicine
Kevin Brown
Standard Model Biomedicine