Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Bayesian inference for infectious disease transmission models based on ordinary differential equations

Predicting the epidemiological effects of new vaccination programmes through mathematical-statistical transmission modelling is of increasing importance for the German Standing Committee on Vaccination. Such models commonly capture large populations utilizing a compartmental structure with its dynamics being governed by a system of ordinary differential equations (ODEs). Unfortunately, these ODE-based models are generally computationally expensive to solve, which poses a challenge for any statistical procedure inferring corresponding model parameters from disease surveillance data. Thus, in practice parameters are often fixed based on epidemiological knowledge hence ignoring uncertainty. A Bayesian inference framework incorporating this prior knowledge promises to be a more suitable approach allowing for additional parameter flexibility. This thesis is concerned with statistical methods for performing Bayesian inference of ODE-based models. A posterior approximation approach based on a Gaussian distribution around the posterior mode through its respective observed Fisher information is presented. By employing a newly proposed method for adjusting the likelihood impact in terms of using a power posterior, the approximation procedure is able to account for the residual autocorrelation in the data given the model. As an alternative to this approximation approach, an adaptive Metropolis-Hastings algorithm is described which is geared towards an efficient posterior sampling in the case of a high-dimensional parameter space and considerable parameter collinearities. In order to identify relevant model components, Bayesian model selection criteria based on the marginal likelihood of the data are applied. The estimation of the marginal likelihood for each considered model is performed via a newly proposed approach which utilizes the available posterior sample obtained from the preceding Metropolis-Hastings algorithm. Furthermore, the thesis contains an application of the presented methods by predicting the epidemiological effects of introducing rotavirus childhood vaccination in Germany. Again, an ODE-based compartmental model accounting for the most relevant transmission aspects of rotavirus is presented. After extending the model with vaccination mechanisms, it becomes possible to estimate the rotavirus vaccine effectiveness through routinely collected surveillance data. By employing the Bayesian framework, model predictions on the future epidemiological development assuming a high vaccination coverage rate incorporate uncertainty regarding both model structure and parameters. The forecast suggests that routine vaccination may cause a rotavirus incidence increase among older children and elderly, but drastically reduces the disease burden among the target group of young children, even beyond the expected direct vaccination effect by means of herd protection. Altogether, this thesis provides a statistical perspective on the modelling of routine vaccination effects in order to assist decision making under uncertainty. The presented methodology is thereby easily applicable to other infectious diseases such as influenza

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

Essays on Representation Learning for Political Science Research

This dissertation consists of three papers about leveraging representation learning for political science research. Representation learning refers to techniques that learn a mapping between input data and a feature vector or tensor with respect to a task, such as classification or regression. These vectors or tensors capture abstract and relevant concepts in the data, making it easier to extract information. In the three papers, I show how representation learning allows political scientists to work with complex data such as text and images effectively. In the first paper, I propose using word embeddings to calculate partisan associations from Twitter users' bios. It only requires that some users in the corpus of tweets use partisan words in their bios. Intuitively, the word embeddings learn associations between non-partisan and partisan words from bios and extend those associations to all users. I apply the method to a collection of users who tweeted about election incidents during the 2016 United States general election. Which partisan accounts get retweeted, favorited, and followed, and which partisan hashtags are used closely correlate with the partisan association scores. I also apply the method to users who tweeted about masks during the COVID-19 pandemic. I find that users with more Democratic-leaning partisan association scores are more likely to use health advocacy hashtags, such as #MaskUp. In the second paper, I look at the automated classification of observations with both images and text. Most state-of-the-art vision-and-language models are unusable for most political science research, as they require all observations to have both image and text and require computationally expensive pretraining. This paper proposes a novel vision-and-language framework called multimodal representations using modality translation, or MARMOT. MARMOT presents two methodological contributions: it constructs representations for observations missing image or text, and it replaces computationally expensive pretraining with modality translation. Modality translation learns the patterns between images and their captions. MARMOT outperforms an ensemble text-only classifier in 19 of 20 categories in multilabel classifications of tweets reporting election incidents during the 2016 U.S. general election. MARMOT also shows significant improvements over the results of benchmark multimodal models on the Hateful Memes dataset, improving the best accuracy and area under the receiver operating characteristic curve (AUC) set by VisualBERT from 0.6473 to 0.6760 and 0.7141 to 0.7530, respectively. In the third paper, I turn to the issue of computationally studying language usage evolution over time. The corpora that political scientists typically work with are much smaller than the extensive corpora used in natural language processing research. Training a word embedding space over each period, the usual approach to studying language usage evolution, worsens the problem by splitting up the corpus into even smaller corpora. This paper proposes a framework that uses pretrained and non-pretrained embeddings to learn time-specific word embeddings, called the pretrained-augmented embeddings (PAE) framework. In the first application, I apply the PAE framework to a corpus of New York Times text data spanning several decades. The PAE framework matches human judgments of how specific words evolve in their usage much more closely than existing methods. In the second application, I apply the PAE framework to a corpus of tweets written during the COVID-19 pandemic about masking. The PAE framework automatically detects shifts in discussions about specific events during the COVID-19 pandemic vis-a-vis the keyword of interest.PHDPolitical ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169642/1/pywu_1.pd

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field

Bayesian inference for infectious disease transmission models based on ordinary differential equations

Predicting the epidemiological effects of new vaccination programmes through mathematical-statistical transmission modelling is of increasing importance for the German Standing Committee on Vaccination. Such models commonly capture large populations utilizing a compartmental structure with its dynamics being governed by a system of ordinary differential equations (ODEs). Unfortunately, these ODE-based models are generally computationally expensive to solve, which poses a challenge for any statistical procedure inferring corresponding model parameters from disease surveillance data. Thus, in practice parameters are often fixed based on epidemiological knowledge hence ignoring uncertainty. A Bayesian inference framework incorporating this prior knowledge promises to be a more suitable approach allowing for additional parameter flexibility. This thesis is concerned with statistical methods for performing Bayesian inference of ODE-based models. A posterior approximation approach based on a Gaussian distribution around the posterior mode through its respective observed Fisher information is presented. By employing a newly proposed method for adjusting the likelihood impact in terms of using a power posterior, the approximation procedure is able to account for the residual autocorrelation in the data given the model. As an alternative to this approximation approach, an adaptive Metropolis-Hastings algorithm is described which is geared towards an efficient posterior sampling in the case of a high-dimensional parameter space and considerable parameter collinearities. In order to identify relevant model components, Bayesian model selection criteria based on the marginal likelihood of the data are applied. The estimation of the marginal likelihood for each considered model is performed via a newly proposed approach which utilizes the available posterior sample obtained from the preceding Metropolis-Hastings algorithm. Furthermore, the thesis contains an application of the presented methods by predicting the epidemiological effects of introducing rotavirus childhood vaccination in Germany. Again, an ODE-based compartmental model accounting for the most relevant transmission aspects of rotavirus is presented. After extending the model with vaccination mechanisms, it becomes possible to estimate the rotavirus vaccine effectiveness through routinely collected surveillance data. By employing the Bayesian framework, model predictions on the future epidemiological development assuming a high vaccination coverage rate incorporate uncertainty regarding both model structure and parameters. The forecast suggests that routine vaccination may cause a rotavirus incidence increase among older children and elderly, but drastically reduces the disease burden among the target group of young children, even beyond the expected direct vaccination effect by means of herd protection. Altogether, this thesis provides a statistical perspective on the modelling of routine vaccination effects in order to assist decision making under uncertainty. The presented methodology is thereby easily applicable to other infectious diseases such as influenza