Artificial intelligence and machine learning in clinical trial design and application

In 2020, COVID-19 changed how health care was approached both in the United States and globally. In the early phases, the vast majority of energy and attention was devoted to containing the pandemic and treating the infected. Toward the end of 2020, that attention expanded to vaccinating people across the globe. What was not being considered at the time were challenges to future health and clinical trials that power new treatments for COVID-19 and non-COVID-19 treatments. © 2023 Elsevier Inc. All rights reserved.

Some examples of privacy-preserving sharing of COVID-19 pandemic data with statistical utility evaluation.

BACKGROUND: A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. METHODS: We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. RESULTS: The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ([Formula: see text]). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When [Formula: see text] and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints. CONCLUSIONS: Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

Investigation of COVID-19 symptoms using deep learning based image enhancement scheme for x-ray medical images

Image enhancement is the inevitable technique for investigating various biological features. The biological image data can be obtained from computer tomography (CT), magnetic resonance imaging (MRI), and X-ray imaging. X-ray imaging is useful for getting the information from lungs and respiratory system. COVID-19 is a life-threatening contiguous disease for the past two years in the world. Patient's chest images playing an important role in the diagnosis of early detection of disease intensity. We propose a generative adversarial network (GAN) method that identifies COVID-19 from medical images and improves diagnostic sensitivity. Here we used virtual colouring methods and a platform for training the images by using a deep parental training method. Similarly, it gives optimal classification results with the help of well-defined image enhancement techniques and image extraction approaches. In our method, the accuracy level lies between 87.8% and 89.6% correspondingly for the dataset and synthetic dataset.

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets.

Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors

Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation methods, including Synthetic Minority Oversampling Technique Nominal (SMOTEN), Tabular Variational Autoencoder (TVAE) and Conditional Tabular Generative Adversarial Networks (CTGANs). Then, we assessed the impact of combining oversampling strategies and linear and nonlinear supervised ML methods. Lastly, we conducted a post-hoc model interpretability based on the importance of the risk factors. Experimental results show the potential of GAN-based models for generating high-quality categorical synthetic data, yielding probability mass functions that are very close to those provided by real data, maintaining relevant insights, and contributing to increasing the predictive performance. The GAN-based model and a linear classifier outperform other oversampling techniques, improving the area under the curve by 2%. These results demonstrate the capability of synthetic data to help with both determining risk factors and building models for CVD prediction.

CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images.

We present a novel algorithm that is able to generate deep synthetic COVID-19 pneumonia CT scan slices using a very small sample of positive training images in tandem with a larger number of normal images. This generative algorithm produces images of sufficient accuracy to enable a DNN classifier to achieve high classification accuracy using as few as 10 positive training slices (from 10 positive cases), which to the best of our knowledge is one order of magnitude fewer than the next closest published work at the time of writing. Deep learning with extremely small positive training volumes is a very difficult problem and has been an important topic during the COVID-19 pandemic, because for quite some time it was difficult to obtain large volumes of COVID-19-positive images for training. Algorithms that can learn to screen for diseases using few examples are an important area of research. Furthermore, algorithms to produce deep synthetic images with smaller data volumes have the added benefit of reducing the barriers of data sharing between healthcare institutions. We present the cycle-consistent segmentation-generative adversarial network (CCS-GAN). CCS-GAN combines style transfer with pulmonary segmentation and relevant transfer learning from negative images in order to create a larger volume of synthetic positive images for the purposes of improving diagnostic classification performance. The performance of a VGG-19 classifier plus CCS-GAN was trained using a small sample of positive image slices ranging from at most 50 down to as few as 10 COVID-19-positive CT scan images. CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19.

Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis

Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis remains largely untapped. This is because the post hoc analysis of data collected during live procedures has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity and a lack of ground truth. Here we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection. We show that training AI image analysis models on realistically synthesized data, combined with contemporary domain generalization techniques, results in machine learning models that on real data perform comparably to models trained on a precisely matched real data training set. We find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real-data-trained models due to the effectiveness of training on a larger dataset. SyntheX provides an opportunity to markedly accelerate the conception, design and evaluation of X-ray-based intelligent systems. In addition, SyntheX provides the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time or mitigate human error, free from the ethical and practical considerations of live human data collection.Simulated data is an alternative to real data for medical applications where interventional data are needed to train AI-based systems. Gao and colleagues develop a model transfer paradigm to train deep networks on synthetic X-ray data and corresponding labels generated using simulation techniques from CT scans. The approach establishes synthetic data as a viable resource for developing machine learning models that apply to real clinical data.

Towards MOOCs for Lipreading: Using Synthetic Talking Heads to Train Humans in Lipreading at Scale

Many people with some form of hearing loss consider lipreading as their primary mode of day-to-day communication. However, finding resources to learn or improve one's lipreading skills can be challenging. This is further exacerbated in the COVID19 pandemic due to restrictions on direct interactions with peers and speech therapists. Today, online MOOCs platforms like Coursera and Udemy have become the most effective form of training for many types of skill development. However, online lipreading resources are scarce as creating such resources is an extensive process needing months of manual effort to record hired ac-tors. Because of the manual pipeline, such platforms are also limited in vocabulary, supported languages, accents, and speakers and have a high usage cost. In this work, we investigate the possibility of replacing real human talking videos with synthetically generated videos. Synthetic data can easily incorporate larger vocabularies, variations in accent, and even local languages and many speakers. We propose an end-to-end automated pipeline to develop such a platform using state-of-the-art talking head video generator networks, text-to-speech models, and computer vision techniques. We then perform an extensive human evaluation using carefully thought out lipreading exercises to validate the quality of our designed platform against the existing lipreading platforms. Our studies concretely point toward the potential of our approach in developing a large-scale lipreading MOOC platform that can impact millions of people with hearing loss. © 2023 IEEE.

Generating Synthetic Images & Data to Improve Object Detection in CCTV Footage from Public Transport

Passenger behaviour on public transport has become a source of great interest in the wake of the COVID-19 pandemic. Operators are interested in employing new methods to monitor vehicle utilisation and passenger behaviour. One way to do this is through the use of Machine Learning, using the CCTV footage that is already being captured from the vehicles. However, one of the limitations of Machine Learning is that it requires large amounts of annotated training data, which is not always available. In this poster, we present a technique that uses 3D models to generate synthetic training images/data and discuss the effect that training with the synthetic data had on the Machine Learning models when applied to real-world CCTV footage. © 2022 IEEE.

Data Analysis and Synthesis of COVID-19 Patients using Deep Generative Models: A Case Study of Jakarta, Indonesia

Two years have passed since COVID-19 broke out in Indonesia. In Indonesia, the central and regional governments have used vast amounts of data on COVID-19 patients for policymaking. However, it is clear that privacy problems can arise when people use their data. Thus, it is crucial to keep COVID-19 data private, using synthetic data publishing (SDP). One of the well-known SDP methods is by using deep generative models. This study explores the usage of deep generative models to synthesise COVID-19 individual data. The deep generative models used in this paper are Generative Adversarial Networks (GAN), Adversarial Autoencoders (AAE), and Adversarial Variational Bayes (AVB). This study found that AAE and AVB outperform GAN in loss, distribution, and privacy preservation, mainly when using the Wasserstein approach. Furthermore, the synthetic data produced predictions in the real dataset with sensitivity and an F1 score of more than 0.8. Unfortunately, the synthetic data produced still has drawbacks and biases, especially in conducting statistical models. Therefore, it is essential to improve the deep generative models, especially in maintaining the statistical guarantee of the dataset. © 2022 IEEE.

Validating a membership disclosure metric for synthetic health data.

Background: One of the increasingly accepted methods to evaluate the privacy of synthetic data is by measuring the risk of membership disclosure. This is a measure of the F1 accuracy that an adversary would correctly ascertain that a target individual from the same population as the real data is in the dataset used to train the generative model, and is commonly estimated using a data partitioning methodology with a 0.5 partitioning parameter. Objective: Validate the membership disclosure F1 score, evaluate and improve the parametrization of the partitioning method, and provide a benchmark for its interpretation. Materials and methods: We performed a simulated membership disclosure attack on 4 population datasets: an Ontario COVID-19 dataset, a state hospital discharge dataset, a national health survey, and an international COVID-19 behavioral survey. Two generative methods were evaluated: sequential synthesis and a generative adversarial network. A theoretical analysis and a simulation were used to determine the correct partitioning parameter that would give the same F1 score as a ground truth simulated membership disclosure attack. Results: The default 0.5 parameter can give quite inaccurate membership disclosure values. The proportion of records from the training dataset in the attack dataset must be equal to the sampling fraction of the real dataset from the population. The approach is demonstrated on 7 clinical trial datasets. Conclusions: Our proposed parameterization, as well as interpretation and generative model training guidance provide a theoretically and empirically grounded basis for evaluating and managing membership disclosure risk for synthetic data.

Individually Tuned Causal Models of Disease Progression

Situation Awareness in health care involves two dif-ferent sets of concerns that are only exacerbated in a pandemic. First is the societal: who is exposed, who is infected, when and how they interact with others, and how can we reduce the severity of impact (epidemiological). Second is the personal: what happens or will happen to each person, and how we can improve that result (individual). This paper is about the latter. It describes an approach to building and maintaining models of disease progression using advanced mathematical methods. These models will be tuned to individual patients, based on the existing and arriving measurement data, as the disease is progressing. The initial models will be generic representations, based on medical expertise, of the current understanding of the various ways the disease can progress. The models will be changed, adjusted separately for each individual patient, according to the newly arriving measurements. This paper describes the technical approach, the purpose and style of modeling we propose, and what we can expect to learn from the application of these methods. The target disease for these first experiments is COVID-19. © 2022 IEEE.

The National COVID Cohort Collaborative: Analyses of original and computationally derived electronic health record data

Background: Computationally derived ("synthetic") data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic. Objective: We aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes. Methods: We used the National COVID Cohort Collaborative's instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19-positive cohort;(2) training and testing predictive models for assessing the risk of admission among these patients;and (3) determining geospatial and temporal COVID-19-related measures and outcomes, and constructing their epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data. Results: For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. Although the synthetic and original data yielded overall nearly the same results, there were exceptions that included an odds ratio on either side of the null in multivariable analyses (0.97 vs 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts. Conclusions: This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Data Augmentation for Short-Term Time Series Prediction with Deep Learning

In this paper, a hybrid data augmentation technique for short-term time series prediction is proposed in order to overcome the underfitting problem in deep learning models based on recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The proposal hybrid technique consists of the combination of two basic data augmentation techniques that are generally used for time series classification, these are: time-warping and jittering. Time-warping allows the generation of synthetic data between each pair of values in the time series, extending its length, while jittering allows the synthetic data generated to be non-linear. To evaluate the proposal technique, it’s experimented with three non-seasonal short-term time series of Perú: CO2 emissions per capita, renewable energy consumption and Covid-19 positive cases, it is considered that predicting non-seasonal time series is more difficult than seasonal ones. The results show that the regression models based on recurrent neural networks using the selected time series with data augmentation improve results between 16.318% and 42.1426% . © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Simple evolutionary algorithm for quantifying how medical history factors predict disease outcomes

The medical history information contained in electronic health records (EHR) is a valuable and largely untapped data mining source for predicting patient outcomes and thereby improving treatment. This paper presents a simple but novel evolutionary algorithm (EA) for identifying how various medical history and demographic factors predict clinical outcomes. For this initial study, our EA was tested using synthetic data concerning COVID-19 hospitalization rates and we show that the EA results are more informative than logistic regression, neural network, or decision tree results. © 2022, EasyChair. All rights reserved.

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).

OBJECTIVE: This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip code-level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely tested zip codes (which contained most of the data) were similar between original and synthetically derived datasets. Analyses of sparsely tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression-an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

Verum Fitness: An AI Powered Mobile Fitness Safety and Improvement Application

At home fitness has rapidly risen recently due to the COVID-19 pandemic and stay-at-home-orders. This also produced a large set of first time users of gym equipment and structured exercise routines. Access to professional fitness trainers to assist beginners in proper exercise form has become increasingly difficult. According to the National Safety Council (NSC), approximately 468, 000 injuries occurred due to exercise in 2019 before the pandemic. Without proper guidance, this statistic is bound to increase. Therefore, there is a need for systems to monitor exercise performance for both short term and long term injury prevention. We present a novel mobile app called Verum Fitness which will use the camera from a smart phone to record a user performing an exercise. Then, the app will skeletonize the user, extract angles from specific joints, and feed this data into a Fuzzy Inference System (FIS), an inherently explainable model, to classify exercise performance. With the FIS, we can provide a description of each repetition performed to determine if it could cause injury and how to improve. From our synthetically generated data, we show a training and test Accuracy of 80.42% and 71.67%, respectively, as well as high Sensitivity and Specificity for the goblet squat. © 2021 IEEE.

Leveraging Simulation and Virtual Reality for a Long Term Care Facility Service Robot during COVID-19

Providing care to seniors and adults with Developmental Disabilities (DD) presents challenges associated with care, companionship, medication intake, and fall monitoring among others. Currently, measures to prevent the spread of COVID-19 have seen restricted access to those living in long-term care facilities (LTCFs). While technologies such as robotics and virtual reality (VR) have seen advances in overcoming the aforementioned challenges, the restrictions have impacted research and development relying on human participants. Recently, the use of synthetic data for training motion detection algorithms and virtual worlds has been gaining momentum as an alternative continue for simulating robot and human interactions instead of relying on public databases and physical locations. Here, we propose the development of VR robot simulator for Aether™, a socially assistive mobile robot created to help seniors and people living with DD to achieve a higher degree of independence. For example, Aether™can assist caregivers by alleviating the burden of care by monitoring the LTCF for tripping hazards, open doors and cabinets. Our simulator allows configuring the virtual Aether™robot to navigate a virtual environment and detect upper limb gestures performed by a virtual avatar. Our preliminary results indicate that the virtual sensor has detection equivalent to the real sensor, thus ensuring that the simulated data is transferable for real-world testing. © 2021 ACM.

Reconciling public health common good and individual privacy: new methods and issues in geoprivacy.

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic.

Validating social distancing through deep learning and VR-based digital twins

The Covid-19 pandemic resulted in a catastrophic loss to global economies, and social distancing was consistently found to be an effective means to curb the virus’s spread. However, it is only as effective when every individual partakes in it with equal alacrity. Past literature outlined scenarios where computer vision was used to detect people and to enforce social distancing automatically. We have created a Digital Twin (DT) of an existing laboratory space for remote monitoring of room occupancy and automatically detecting violation of social distancing. To evaluate the proposed solution, we have implemented a Convolutional Neural Network (CNN) model for detecting people, both in a limited-sized dataset of real humans, and a synthetic dataset of humanoid figures. Our proposed computer vision models are validated for both real and synthetic data in terms of accurately detecting persons, posture, and intermediate distances among people. © 2021 Copyright held by the owner/author(s).