Digital Twins in Medicine

Medical systems are inherently complex, exhibiting multi-scale, dynamic, adaptive, and context-dependent behavior.
Understanding and modeling these systems requires specialized domain expertise together with diverse mathematical approaches to capture and represent processes across different scales.

Our research investigates how principles from complex systems theory, statistical physics, and information theory can provide a unifying foundation for AI-driven health research. We focus on bridging patient-level (microscopic) and population-level (macroscopic) modeling.

By treating patients and populations as interconnected levels of the same dynamic health system, we seek to move beyond isolated AI applications toward a coherent systems-level understanding of health and disease. This perspective supports the development of informed, interpretable, and scalable computational models for personalized medicine and precision public health.

Digital Twins for Personalised Medicine

Leading Question: How to guide and evaluate decision-making in clinical practice?

A Digital Twin is a virtual representation of a patient that can be used to predict and analyze various medical scenarios and treatments.

The origin of the Digital Twin concept is in the engineering disciplines, but recently gained a lot of attention in medical areas as tool to enable personalized healthcare. The Digital Twin incorporates high-level Machine Learning and integrates individual-level data, such as proteome and clinical characteristics, with other factors like clinical trials and population studies to create a multiscale and multimodal data set for model training.

For medical applications, black-box Machine Learning must be avoided. For the doctor to make informed decisions, it is important to enable

  1. An intuitively interpretable decision support and
  2. Inclusion of current evidence-based knowledge and clinical guidelines

Our unique Digital Twin platform enables a versatile and agnostic decision support system and intrinsic plus state-of-the-art interpretation techniques.

Cooperations

We work closely with medical doctors from the University Hospital and are focusing on the diagnosis of Prostate Cancer. The Urology department of the University Hospital offers a unique database of prostate cancer patients. Therefore, we are able to develop state-of-the-art machine learning pipelines and have a direct impact on the clinical patient care.

Machine Learning in Precision Public Health & Global Health

Leading Question: How to guide and evaluate design-choices in healthcare? How should population information be represented so that it remains interpretable, uncertainty-aware, and useful for decision-making?

We investigate the application of machine learning methods to public health, with a particular focus on global health in low-resource settings. Our research aims to improve the estimation of multimorbidity burden at the population level by developing advanced mathematical approaches for health data representation and population stratification. These methods support more robust epidemiological analyses and contribute to evidence-based global health research and policy-making.

Cooperations

We collaborate closely with epidemiologists at the Heidelberg Institute of Global Health and local partners at Health and Demographic Surveillance Sites (HDSS) across several African countries. These collaborations enable us to develop scientifically informed and context-aware machine learning methods with the potential to support evidence-based public health research, policy, and health system strengthening.

  • Cedric Becker – Master Thesis on brain tumors

    How can AI and machine learning improve the treatment and prognosis of brain tumors?
    My Master’s thesis addresses this question by focusing on the data-driven analysis of meningiomas (brain tumors) using modern machine learning techniques. In cooperation with the University Medical Center Mannheim, clinical patient data are evaluated computationally to identify patterns in the course of the disease and its treatment. The primary focus is on developing predictive models that provide forecasts regarding surgical outcomes, postoperative consequences, and tumor dynamics. Ultimately, this work aims to demonstrate how machine learning and data-driven methods can support translational medicine and personalized patient care

  • Anna-Katharina Nitschke starts her PhD in the Digital Twins team

    Anna stays with the Digital Twin group after successfully finishing her Master Thesis about an Architecture of Digital Twins of Patients in Urology and will continue her research on Digital Twins in Medicine for applications in Global Health together with partners from the Heidelberg Institute of Global Health and STRUCTURES. We wish good luck!

  • Digital Twin project starts

    A new project starts in the group. The Digital Twin group consists of two members, Anna Nitscke, who wants to start her master thesis in this project. And Carlos Brandl, who starts his PhD after being a Master student at the Rydberg team. Together with Prof. Ommer from the IWR the team tries to find new ways of creating digital twins for medicine. The project is part of the collaborative CLINIC5.1 project, lead by the University Hospital and funded by the BMWK.

Recent publications

2023

Geier S

Shaping the Hamiltonian of many-body spin systems on a Rydberg-atom quantum simulator PhD Thesis

2023.

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Wadenpfuhl K

Emergence of synchronisation in a driven-dissipative hot Rydberg vapour Masters Thesis

2023.

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Welz K, Gerken M, Zhu B, Lippi E, Rautenberg M, Chomaz L, Weidemüller M

Anomalous loss behavior in a single-component Fermi gas close to a p-wave Feshbach resonance Journal Article

In: Phys. Rev. A, vol. 107, iss. 5, pp. 053310, 2023.

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Nitschke A K

Digital Twins of Patients In Urology – A Proposed Architecture Masters Thesis

2023.

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2022

Franz T

Studies of out-of-equilibrium dynamics of disordered heisenberg spin models on a Rydberg quantum simulator PhD Thesis

2022.

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Tran B

From Efimov Physics to Polarons in an Ultracold Mixture of Li and Cs Atoms PhD Thesis

2022.

Abstract | Links | BibTeX

Hassan S Z

Dynamics of anions and ultracold atoms in a hybrid atom-ion trap PhD Thesis

2022.

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Gerken M

Exploring p-wave Feshbach Resonances in Ultracold Lithium and Lithium-Cesium Mixtures PhD Thesis

2022.

Abstract | Links | BibTeX

Alves R F

Realization of a Heisenberg XXZ spin system using Rydberg atoms PhD Thesis

2022.

Abstract | Links | BibTeX

Tan C, Lin X, Zhou Y, Jiang Y H, Weidemüller M, Zhu B

Dynamics of position disordered Ising spins with a soft-core potential Journal Article

In: Phys. Rev. B, vol. 105, pp. 104204, 2022.

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Hu F, Tan C, Jiang Y, Weidemüller M, Zhu B

Observation of photon recoil effects in single-beam absorption spectroscopy with an ultracold strontium gas Journal Article

In: Chin. Phys. B, vol. 31, pp. 016702, 2022.

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Scholl P, Williams H J, Bornet G, Wallner F, Barredo D, Lahaye T, Henriet L, Signoles A, Hainaut C, Franz T, Geier S, Tebben A, Zürn G, Salzinger A, Weidemüller M, Browaeys A

Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms Journal Article

In: Phys. Rev. X Quantum, vol. 3, pp. 020303, 2022.

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Schultzen P, Franz T, Hainaut C, Geier S, Salzinger A, Tebben A, Zürn G, Gärttner M, Weidemüller M

Semiclassical simulations predict glassy dynamics for disordered Heisenberg models Journal Article

In: Phys. Rev. B, 2022.

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Schultzen P, Franz T, Geier S, Salzinger A, Tebben A, Hainaut C, Zürn G, Weidemüller M, Gärttner M

Glassy quantum dynamics of disordered Ising spins Journal Article

In: Phys. Rev. B, vol. 105, pp. L020201, 2022.

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Hassan S Z, Tauch J, Kas M, Nötzold M, Carrera H L, Endres E S, Wester R, Weidemüller M

Associative detachment in anion-atom reactions involving a dipole-bound electron Journal Article

In: Nat. Comm., vol. 13, pp. 818, 2022.

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Hassan S Z, Tauch J, Kas M, Nötzold M, Wester R, Weidemüller M

Quantum state-dependent anion-neutral detachment processes Journal Article

In: J. Chem. Phys., vol. 156, pp. 094304, 2022.

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Bharti V, Sugawa S, Mizoguchi M, Kunimi M, Zhang Y, de Léséleuc S, Tomita T, Franz T, Weidemüller M, Ohmori K

Ultrafast Many-Body Dynamics in an Ultracold Rydberg-Excited Atomic Mott Insulator Working paper

arXiv e-prints: 2201.09590, 2022.

Abstract | Links | BibTeX

2021

Tebben A

Rydberg Electromagnetically Induced Transparency – A vanishing linear response, resonances, and a stationary Rydberg polariton PhD Thesis

2021.

Abstract | Links | BibTeX

Tauch J

New approaches for cooling molecular anions to the Kelvin range PhD Thesis

2021.

Abstract | Links | BibTeX

Wintermantel T M

Complex systems dynamics in laser excited ensembles of Rydberg atoms PhD Thesis

2021.

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Tran B, Rautenberg M, Gerken M, Lippi E, Zhu B, Ulmanis J, Drescher M, Salmhofer M, Enss T, Weidemüller M

Fermions meet two bosons — the heteronuclear Efimov effect revisited Journal Article

In: Braz. J. Phys., vol. 51, pp. 316, 2021.

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Signoles A, Franz T, Alves R F, Gärttner M, Whitlock S, Zürn G, Weidemüller M

Observation of glassy dynamics in a disordered quantum spin system Journal Article

In: Phys. Rev. X, vol. 11, pp. 011011, 2021.

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Geier S, Thaicharoen N, Hainaut C, Franz T, Salzinger A, Tebben A, Grimshandl D, Zürn G, Weidemüller M

Floquet Hamiltonian Engineering of an Isolated Many-Body Spin System Journal Article

In: Science, vol. 374, pp. 1149, 2021.

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Stutterheim C, Weidemüller M

Was die Welt zusammenhält: Babel und Bubbles Online

2021, visited: 01.01.2021.

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Tebben A, Hainaut C, Salzinger A, Geier S, Franz T, Pohl T, Gärttner M, Zürn G, Weidemüller M

Nonlinear absorption in interacting Rydberg electromagnetically-induced-transparency spectra on two-photon resonance Journal Article

In: Phys. Rev. A, vol. 103, pp. 063710, 2021.

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Qiao C, Tan C, Siegl J, Hu F, Niu Z, Jiang Y, Weidemüller M, Zhu B

Rydberg blockade in an ultracold strontium gas revealed by two-photon excitation dynamics Journal Article

In: Phys. Rev. A, vol. 103, pp. 063313, 2021.

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