Using System Identification and Central Pattern Generators to Create Synthetic Gait Data

BackgroundPrivacy restrictions limit access to protected patient-derived health information for research purposes. Consequently, data anonymization is required to allow researchers data access for initial analysis before granting institutional review board approval. A system installed and activated at our institution enables synthetic data generation that mimics data from real electronic medical records, wherein only fictitious patients are listed.ObjectiveThis paper aimed to validate the results obtained when analyzing synthetic structured data for medical research. A comprehensive validation process concerning meaningful clinical questions and various types of data was conducted to assess the accuracy and precision of statistical estimates derived from synthetic patient data.MethodsA cross-hospital project was conducted to validate results obtained from synthetic data produced for five contemporary studies on various topics. For each study, results derived from synthetic data were compared with those based on real data. In addition, repeatedly generated synthetic datasets were used to estimate the bias and stability of results obtained from synthetic data.ResultsThis study demonstrated that results derived from synthetic data were predictive of results from real data. When the number of patients was large relative to the number of variables used, highly accurate and strongly consistent results were observed between synthetic and real data. For studies based on smaller populations that accounted for confounders and modifiers by multivariate models, predictions were of moderate accuracy, yet clear trends were correctly observed.ConclusionsThe use of synthetic structured data provides a close estimate to real data results and is thus a powerful tool in shaping research hypotheses and accessing estimated analyses, without risking patient privacy. Synthetic data enable broad access to data (eg, for out-of-organization researchers), and rapid, safe, and repeatable analysis of data in hospitals or other health organizations where patient privacy is a primary value.

e19554 Background: Adding daratumumab to standard multiple myeloma (MM) regimens improves response rates and clinical outcomes. Systematic reviews (meta-analyses) allow further evaluation of its effectiveness in MM patients with high-risk cytogenetic factors but are limited by model assumptions. his study addresses these limitations using a novel synthetic individual patient data (SynIPD) generation algorithm to enable a more robust analysis. Methods: Advances in AI have enabled the creation of synthetic data that mimics the statistical properties of real patient data, empowering aggregated analyses of clinical trials. This study focuses on newly diagnosed MM (NDMM) and relapsed/refractory MM (RRMM) patients with high-risk cytogenetic profiles. Six studies were included: ALCYONE, CASSIOPEIA, and MAIA for NDMM, and CANDOR, CASTOR, and POLLUX for RRMM. Large language models extracted clinical evidence, and SynIPD was generated, incorporating cytogenetic profiles. Random-effects meta-analyses were conducted, and results were compared with pooled analyses using synthetic data. Results: Synthetic data were generated for three studies for NDMM (ALCYONE, CASSIOPEIA, MAIA) and three studies for RRMM (CANDOR, CASTOR, POLLUX). The generated hazard ratios (HRs) for each study closely aligned with reported HRs for high-risk patients, with a maximum relative error of less than 2%. By pooling these datasets, we obtained synthetic datasets for NDMM and RRMM patients. Stratified Cox regression analyses yielded HRs (95% confidence interval) of 0.764 (0.529-1.104) for NDMM and 0.431 (0.294-0.613) for RRMM. Random-effects meta-analyses showed HRs (95% confidence interval) of 0.764 (0.529-1.104) for NDMM and 0.425 (0.287-0.621) for RRMM. Compared with traditional meta-analysis techniques, the synthetic data approach provides additional clinical insights that were not available in the original publications. For instance, the clinical results for the three NDMM studies are summarized as KM curves in Figure 1, which were not reported in the original studies. Conclusions: Our results suggest that the addition of daratumumab to standard regimens improves PFS for RRMM patients with high-risk cytogenetic factors, although its benefit for NDMM patients is not statistically significant. Pooling synthetic data enhances the robustness of conventional meta-analysis outcomes. The identification of HRs and corresponding confidence intervals through SynIPD improves the credibility of our proposed method, offering a reliable approach to evaluating the efficacy of daratumumab in high-risk patients. Since SynIPD includes individual covariate information, it provides deep clinical insights beyond merely enhancing meta-analysis. In other words, any statistical results derived from the original IPD can be effectively approximated using SynIPD.

BackgroundThe exponential growth in patient data collection by healthcare providers, governments, and private industries is yielding large and diverse datasets that offer new insights into critical medical questions. Leveraging extensive computational resources, Machine Learning and Artificial Intelligence are increasingly utilized to address health-related issues, such as predicting outcomes from Electronic Health Records and detecting patterns in multi-omics data. Despite the proliferation of medical devices based on Artificial Intelligence, data accessibility for research is limited due to privacy concerns. Efforts to de-identify data have met challenges in maintaining effectiveness, particularly with large datasets. As an alternative, synthetic data, that replicate main statistical properties of real patient data, are proposed. However, the lack of standardized evaluation metrics complicates the selection of appropriate synthetic data generation methods. Effective evaluation of synthetic data must consider resemblance, utility and privacy, tailored to specific applications. Despite available metrics, benchmarking efforts remain limited, necessitating further research in this area.ResultsWe present SynthRO (Synthetic data Rank and Order), a user-friendly tool for benchmarking health synthetic tabular data across various contexts. SynthRO offers accessible quality evaluation metrics and automated benchmarking, helping users determine the most suitable synthetic data models for specific use cases by prioritizing metrics and providing consistent quantitative scores. Our dashboard is divided into three main sections: (1) Loading Data section, where users can locally upload real and synthetic datasets; (2) Evaluation section, in which several quality assessments are performed by computing different metrics and measures; (3) Benchmarking section, where users can globally compare synthetic datasets based on quality evaluation.ConclusionsSynthetic data mitigate concerns about privacy and data accessibility, yet lacks standardized evaluation metrics. SynthRO provides an accessible dashboard helping users select suitable synthetic data models, and it also supports various use cases in healthcare, enhancing prognostic scores and enabling federated learning. SynthRO’s accessible GUI and modular structure facilitate effective data evaluation, promoting reliability and fairness. Future developments will include temporal data evaluation, further broadening its applicability.

Genome-wide association studies (GWAS) have served as primary methods for the past decade for identifying associations between genetic variants and traits or diseases. Many software packages have been developed for GWAS analysis based on different statistical models. One key factor influencing the statistical reliability of GWAS is the amount of input data used. Few studies have been conducted to investigate this effect by comparing the performance of GWAS programs using varied amounts of experimental data, especially in the context of plants and plant genomes. In this paper, we investigate how input data quantity influences output of four widely used GWAS programs, PLINK, TASSEL, GAPIT, and FaST-LMM. Both synthetic and real data are used. Standard GWAS output includes single nucleotide polymorphisms (SNPs) and their p-values. To evaluate the programs, p-values and q-values of SNPs, and Kendall rank correlation between output SNP lists, are used. Results show that with the same GWAS program, different Arabidopsis thaliana datasets demonstrate similar trends of rank correlation with varied input quantity, but differentiate on the numbers of SNPs passing a given p- or q-value threshold. In practice, experimental datasets may have samples containing varied numbers of biological replicates. We show that this variation in replicates influences the p-values of SNPs, but does not strongly affect the rank correlation. When comparing synthetic and real data, the output SNPs from synthetic data have similar rank correlation trends across all four GWAS programs, but the same measure from real data is diverse across the programs. In addition, the real data results in a linear-like increase in the numbers of significant SNPs with more input data, but the synthetic data does not follow this trend. This study provides guidance on selecting GWAS programs when varied experimental data is present and on selecting significant SNPs for subsequent study. It contributes to understanding how much input data is necessary to yield satisfying GWAS results.

Named entity recognition (NER) plays a vital role in extracting critical medical entities from health care records, facilitating applications such as clinical decision support and data mining. Developing robust NER models for low-resource languages, such as Estonian, remains a challenge due to the scarcity of annotated data and domain-specific pretrained models. Large language models (LLMs) have proven to be promising in understanding text from any language or domain. This study addresses the development of medical NER models for low-resource languages, specifically Estonian. We propose a novel approach by generating synthetic health care data and using LLMs to annotate them. These synthetic data are then used to train a high-performing NER model, which is applied to real-world medical texts, preserving patient data privacy. Our approach to overcoming the shortage of annotated Estonian health care texts involves a three-step pipeline: (1) synthetic health care data are generated using a locally trained GPT-2 model on Estonian medical records, (2) the synthetic data are annotated with LLMs, specifically GPT-3.5-Turbo and GPT-4, and (3) the annotated synthetic data are then used to fine-tune an NER model, which is later tested on real-world medical data. This paper compares the performance of different prompts; assesses the impact of GPT-3.5-Turbo, GPT-4, and a local LLM; and explores the relationship between the amount of annotated synthetic data and model performance. The proposed methodology demonstrates significant potential in extracting named entities from real-world medical texts. Our top-performing setup achieved an F1-score of 0.69 for drug extraction and 0.38 for procedure extraction. These results indicate a strong performance in recognizing certain entity types while highlighting the complexity of extracting procedures. This paper demonstrates a successful approach to leveraging LLMs for training NER models using synthetic data, effectively preserving patient privacy. By avoiding reliance on human-annotated data, our method shows promise in developing models for low-resource languages, such as Estonian. Future work will focus on refining the synthetic data generation and expanding the method's applicability to other domains and languages.

This study presents a parameter optimization strategy for generating synthetic seismic data that closely match the characteristics of target field data, aiming to improve deep learning-based fault detection. An analysis in the latent space is conducted to assess the similarities between synthetic data and target field data. Based on the results from this analysis, we optimize the parameters for generating synthetic data. Further refinement of the data generation process is achieved through the application of Explainable Artificial Intelligence (XAI). The fault interpretation results using the U-Net model trained on optimized synthetic data show significant improvements compared to those from the model trained on unoptimized data. The optimization strategy employed allows for the visualization of feature distributions in the latent space, offering a direct understanding of how the distribution of features shifts depending on the desired parameters. This approach not only circumvents the limitations associated with using field data for training, such as the challenge of acquiring accurate fault structure labels and the scarcity of sufficient training data, but also overcomes the potential discrepancies in interpretation results due to significant deviations in the characteristics of synthetic data from the target field data. The proposed optimization framework improves the performance of deep learning models in fault interpretation and establishes an advanced approach for using synthetic data in deep learning–based seismic interpretation.

Preserving logical and functional dependencies in synthetic tabular data

Teaching Aids for Modeling and Control of Hybrid Systems (CAMCHS)

Deep learning (DL) applications of seismic reservoir characterization often require the generation of synthetic data to augment available sparse labeled data. An approach for generating synthetic training data consists of specifying probability distributions modeling prior geologic uncertainty on reservoir properties and forward modeling the seismic data. A prior falsification approach is critical to establish the consistency of the synthetic training data distribution with real seismic data. With the help of a real case study of facies classification with convolutional neural networks (CNNs) from an offshore deltaic reservoir, we have highlighted several practical nuances associated with training DL models on synthetic seismic data. We highlight the issue of overfitting of CNNs to the synthetic training data distribution and propose regularization strategies to address it. We demonstrate the efficacy of our proposed strategies by training the CNN on synthetic data and making robust predictions with real 3D partial stack seismic data.

Generation of synthetic full-scale burst test data for corroded pipelines using the tabular generative adversarial network

High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent such hurdles. Although many healthcare institutions still use the classical de-identification and anonymization techniques for generating synthetic data, deep learning-based generative models such as generative adversarial networks (GANs) have shown a remarkable performance in generating tabular datasets with complex structures. This paper examines the GANs’ potential and applicability within the healthcare industry, which often faces serious challenges with insufficient training data and patient records sensitivity. We investigate several state-of-the-art GAN-based models proposed for tabular synthetic data generation. Healthcare datasets with different sizes, numbers of variables, column data types, feature distributions, and inter-variable correlations are examined. Moreover, a comprehensive evaluation framework is defined to evaluate the quality of the synthetic records and the viability of each model in preserving the patients’ privacy. The results indicate that the proposed models can generate synthetic datasets that maintain the statistical characteristics, model compatibility and privacy of the original data. Moreover, synthetic tabular healthcare datasets can be a viable option in many data-driven applications. However, there is still room for further improvements in designing a perfect architecture for generating synthetic tabular data.

The efficacy of the multimodel framework (MMF) in modeling and identification of complex, nonlinear, and uncertain systems has been widely recognized in the literature owing to its simplicity, transparency, and mathematical tractability, allowing the use of well-known modeling analysis and control design techniques. The approach proved to be effective in addressing some of the shortcomings of other modeling techniques such as those based on a single nonlinear autoregressive network with exogenous inputs model or neural networks. A great number of researchers have contributed to this active field. Due to the significant amount of contributions and the lack of a recent survey, the review of recent developments in this field is vital. In this two-part paper, we attempt to provide a comprehensive coverage of the multimodel approach for modeling and identification of complex systems. The study contains a classification of different methods, the challenges encountered, as well as recent applications of MMF in various fields. Part 1 of this paper presents an overview of MMF for modeling and identification of nonlinear systems as well as the review of recent developments in the partitioning strategies employed.

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two interlaced parts of modeling that are computer-aided. The first performs computer-aided identification of a model structure composed of elements selected from user-specified domain-specific modeling knowledge, while the second part performs parameter estimation. In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated in two continuous-time case studies. The first case study illustrates the use of the process-based modeling on synthetic data while the second case-study evaluates process-based modeling on measured data for a standard system-identification benchmark. The experimental results clearly demonstrate the ability of process-based modeling to reconstruct both model structure and parameters from measured data.

Generation of synthetic data with low-dimensional features for condition monitoring utilizing Generative Adversarial Networks

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that will satisfy similar statistical properties as the original data. This learned model can generate an arbitrary amount of synthetic data, which can then be freely shared due to the post-processing guarantee of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both binary data (MIMIC-III) and mixed-type data (ADULT), and compare its performance with existing private algorithms on metrics in unsupervised settings. We also introduce a new quantitative metric able to detect diversity, or lack thereof, of synthetic data.