Software

• • Physics-informed ML

  Physics-informed neural networks for hidden insulin dynamics

  Physics-informed neural networks that estimate insulin dynamics, which are not measured directly, from glucose data.

  GitLabPaper

  Used in PRAESIIDIUM

• • Physics-informed ML

  Reduced model of type 2 diabetes progression

  Simulation code for a two-scale model of the long-term effects of physical activity on type 2 diabetes and for its reduced (homogenised) version, which allows fast long-term predictions.

  GitLabPaper

  Used in PRAESIIDIUM

• • Physics-informed ML

  Surrogate model of prediabetes progression

  Deep learning surrogate of a mechanistic model of prediabetes progression, for fast patient-specific simulations. Data and trained model are not included.

  GitLabPaper

  Used in PRAESIIDIUM

• • Physics-informed ML

  Hierarchical Bayesian inference for ODE systems

  Hierarchical Bayesian estimation of the parameters of ordinary differential equations from multiple sparse data sets, which share information across individuals or experiments.

  GitLabPreprint

• • Physics-informed ML

  Spatial regression with PDE penalisation

  Code for spatial regression with partial differential equation penalisation, used to estimate blood flow velocity fields.

  Code

• • Causal AI and graphical models
  • Physics-informed ML

  Counterfactual inference with differential equation models

  Counterfactual inference for ordinary differential equation models, applied to a model of glucose regulation to assess personalised physical activity plans for the prevention of type 2 diabetes.

  GitLabPaper

  Used in PRAESIIDIUM

• • Causal AI and graphical models

  Causal discovery for type 2 diabetes risk

  Bootstrap-based causal discovery of a global causal model of type 2 diabetes risk factors from primary care data.

  GitLabPaper

  Used in PRAESIIDIUM

• • Causal AI and graphical models

  Causal AI Clinician

  Structural causal models with expert clinical knowledge that estimate the effect of intravenous fluids and vasopressors in the first hours of septic shock, trained on MIMIC and externally validated on eICU, with an interactive dashboard.

  GitHubPreprint

  Used in Emulation of clinical trials with real-world data

• • Causal AI and graphical models

  Hierarchical Bayesian network parameters

  Code for the hierarchical estimation of conditional probability tables in Bayesian networks.

  Code

• • Federated learning

  FLOTTA

  Secure and flexible federated learning framework, inspired by Apache Spark, for training models across institutions without sharing data.

  GitHubDocumentationPaper

  Used in SPEARHEAD

• • Federated learning

  FPLIER

  Federated version of PLIER, which extracts pathway-level representations of transcriptomic data distributed across institutions, with a simulation based on the Flower framework.

  GitHubPaper

  Used in K-CLIER

• K-CLIER protocol

  Step-by-step protocol, with R code and test data, for interpretable, single-cell-informed deconvolution of bulk RNA-seq data.

  GitHubSTAR Protocols

  Used in K-CLIER

IDSIA on GitHub