PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (12)

[1]
Clinical prediction models and the multiverse of madness
2023Richard D Riley, Alexander Pate et al.
[2]
Stability of clinical prediction models developed using statistical or machine learning methods
2022R. Riley, G. Collins
[3]
Interpretability of machine learning‐based prediction models in healthcare
2020Gregor Stiglic, Primož Kocbek et al.
[4]
Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study
2017J. Hippisley-Cox, C. Coupland et al.
[5]
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
2016Balaji Lakshminarayanan, A. Pritzel et al.
[6]
Deep Learning
2016Xingbang Hao, Guigang Zhang et al.
[7]
The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective
2014Syed S Mahmood, D. Levy et al.
[8]
Dropout: a simple way to prevent neural networks from overfitting
2014Nitish Srivastava, Geoffrey E. Hinton et al.
[9]
EuroSCORE II.
2012Samer A M Nashef, François Roques et al.
[10]
Bayesian approach for neural networks--review and case studies
2001J. Lampinen, Aki Vehtari
[11]
Bagging Predictors
1996L. Breiman
[12]
An international randomized trial comparing four thrombolytic strategies for acute myocardial infarction.
1993M. Simoons, E. Topol et al.

Founder's Pitch

"Develop a regularisation framework for stabilizing clinical prediction models' outputs, enhancing reliability and interpretability in healthcare."

Medical AIScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

4/4 signals

10

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/11/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.