1
|
Ye Y, Pandey A, Bawden C, Sumsuzzman DM, Rajput R, Shoukat A, Singer BH, Moghadas SM, Galvani AP. Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges. Nat Commun 2025; 16:581. [PMID: 39794317 PMCID: PMC11724045 DOI: 10.1038/s41467-024-55461-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Accepted: 12/12/2024] [Indexed: 01/13/2025] Open
Abstract
Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our review highlights the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration. However, key research gaps remain. These include the need for better incorporation of realistic decision-making considerations, expanded exploration of diverse datasets, and further investigation into biological and socio-behavioral mechanisms. Addressing these gaps will unlock the synergistic potential of AI and mechanistic modeling to enhance understanding of disease dynamics and support more effective public health planning and response.
Collapse
Affiliation(s)
- Yang Ye
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA
| | - Abhishek Pandey
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA
| | - Carolyn Bawden
- Department of Microbiology and Immunology, McGill University, Montréal, QC, Canada
- Agent-Based Modelling Laboratory, York University, Toronto, ON, Canada
| | | | - Rimpi Rajput
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA
| | - Affan Shoukat
- Department of Mathematics and Statistics, University of Regina, Regina, SK, Canada
| | - Burton H Singer
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Seyed M Moghadas
- Agent-Based Modelling Laboratory, York University, Toronto, ON, Canada
| | - Alison P Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
| |
Collapse
|
2
|
Samadi ME, Mirzaieazar H, Mitsos A, Schuppert A. Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions. BMC Bioinformatics 2024; 25:155. [PMID: 38641616 PMCID: PMC11031902 DOI: 10.1186/s12859-024-05769-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 04/09/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model's ability to extrapolate. RESULTS We present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process. CONCLUSIONS NoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/ . The implementation detailed in this paper corresponds to the version 0.2.1 release of the software.
Collapse
Affiliation(s)
- Moein E Samadi
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Hedieh Mirzaieazar
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Alexander Mitsos
- Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany
| | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.
| |
Collapse
|
3
|
Führer F, Gruber A, Diedam H, Göller AH, Menz S, Schneckener S. A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat. J Comput Aided Mol Des 2024; 38:7. [PMID: 38294570 DOI: 10.1007/s10822-023-00547-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024]
Abstract
An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.
Collapse
Affiliation(s)
- Florian Führer
- Engineering & Technology, Applied Mathematics, Bayer AG, 51368, Leverkusen, Germany.
| | - Andrea Gruber
- Pharmaceuticals, R&D, Preclinical Modeling & Simulation, Bayer AG, 13353, Berlin, Germany
| | - Holger Diedam
- Crop Science, Product Supply, SC Simulation & Analysis, Bayer AG, 40789, Monheim, Germany
| | - Andreas H Göller
- Pharmaceuticals, R&D, Molecular Design, Bayer AG, 42096, Wuppertal, Germany
| | - Stephan Menz
- Pharmaceuticals, R&D, Preclinical Modeling & Simulation, Bayer AG, 13353, Berlin, Germany
| | | |
Collapse
|
4
|
Gruber A, Führer F, Menz S, Diedam H, Göller AH, Schneckener S. Prediction of human pharmacokinetics from chemical structure: combining mechanistic modeling with machine learning. J Pharm Sci 2023; 113:S0022-3549(23)00466-5. [PMID: 39492474 DOI: 10.1016/j.xphs.2023.10.035] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/25/2023] [Accepted: 10/25/2023] [Indexed: 11/05/2024]
Abstract
Pharmacokinetics (PK) is the result of a complex interplay between compound properties and physiology, and a detailed characterization of a molecule's PK during preclinical research is key to understanding the relationship between applied dose, exposure, and pharmacological effect. Predictions of human PK based on the chemical structure of a compound are highly desirable to avoid advancing compounds with unfavorable properties early on and to reduce animal testing, but data to train such models are scarce. To address this problem, we combine well-established physiologically based pharmacokinetic models with Deep Learning models for molecular property prediction into a hybrid model to predict PK parameters for small molecules directly from chemical structure. Our model predicts exposure after oral and intravenous administration with fold change errors of 1.87 and 1.86, respectively, in healthy subjects and 2.32 and 2.23, respectively, in patients with various diseases. Unlike pure Deep Learning models, the hybrid model can predict endpoints on which it was not trained. We validate this extrapolation capability by predicting full concentration-time profiles for compounds with published PK data. Our model enables early selection and prioritization of the most promising drug candidates, which can lead to a reduction in animal testing during drug discovery and development.
Collapse
Affiliation(s)
- Andrea Gruber
- Bayer AG, Pharmaceuticals, R&D, Preclinical Modeling & Simulation, 13353 Berlin, Germany.
| | - Florian Führer
- Bayer AG, Engineering & Technology, Applied Mathematics, 51368 Leverkusen, Germany
| | - Stephan Menz
- Bayer AG, Pharmaceuticals, R&D, Preclinical Modeling & Simulation, 13353 Berlin, Germany
| | - Holger Diedam
- Bayer AG, Crop Science, Product Supply, SC Simulation & Analysis, 40789 Monheim, Germany
| | - Andreas H Göller
- Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 42096 Wuppertal, Germany
| | | |
Collapse
|
5
|
Abdollahi H, Saboury B, Soltani M, Shi K, Uribe C, Rahmim A. Radiopharmaceutical therapy on-a-chip: a perspective on microfluidic-driven digital twins towards personalized cancer therapies. Sci Bull (Beijing) 2023; 68:1983-1988. [PMID: 37573246 DOI: 10.1016/j.scib.2023.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/14/2023]
Affiliation(s)
- Hamid Abdollahi
- Department of Radiology, University of British Columbia, Vancouver V5Z 1M9, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver V5Z 1L3, Canada
| | - Babak Saboury
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver V5Z 1L3, Canada; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda 20892, USA
| | - Madjid Soltani
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver V5Z 1L3, Canada; Department of Electrical & Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Canada
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland; Computer Aided Medical Procedures and Augmented Reality, Institute of Informatics, Technical University of Munich, Munich 80333, Germany
| | - Carlos Uribe
- Department of Radiology, University of British Columbia, Vancouver V5Z 1M9, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver V5Z 1L3, Canada; Functional Imaging, BC Cancer, Vancouver V5Z 4E6, Canada
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver V5Z 1M9, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver V5Z 1L3, Canada; Department of Physics & Astronomy, University of British Columbia, Vancouver V6T 1Z1, Canada.
| |
Collapse
|
6
|
Koksal ES, Aydin E. Physics Informed Piecewise Linear Neural Networks for Process Optimization. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
|
7
|
Physics-Informed Recurrent Neural Networks and Hyper-parameter Optimization for Dynamic Process Systems. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
|
8
|
A general deep hybrid model for bioreactor systems: Combining first principles with deep neural networks. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|