MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment

Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. To address this issue, we propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices. Our system leverages a decentralized learning mechanism that combines transfer and federated machine learning concepts using smart contracts, allowing data to remain on users' devices and enabling effective tracking of mental health conditions for psychiatric treatment and management in a privacy-aware and accountable manner. We evaluate our model using a popular mental health dataset that demonstrates promising results. By utilizing connected health systems and machine learning models, our approach offers a novel solution to the challenge of providing psychiatrists with further insight into their patients' mental health outside of traditional office visits.Comment: Accepted at AMIA 2023 Annual Symposiu

Incentivized Research Data Sharing, Reusing and Repurposing with Blockchain Technologies

Data is the lifeblood of many organizations. Compared to the centralized mechanisms of data sharing (and the subsequent reuse and repurposing), many, if not all, aspects of these processes can be decentralized by using blockchain and provenance semantics. By capturing metadata details at each step of the workflow, data will be easier to audit, verify, and merge with related datasets. It is common in settings where data is either sensitive or valuable (or both) to have formal data use agreements or sometimes less formal rules for reuse, which we have captured in smart contracts. A key innovative aspect of this work is the departure from the traditional natural language-based data use agreements with the aim of making these agreements more computable, resulting in enhanced usability by a broader community. We have also engineered an innovative incentive mechanism for sharing data using an ERC20 token, a popular technical standard for developing fungible tokens on the Ethereum blockchain. The system we developed can be used to track data reuse, thus providing metrics for use in measuring data producers’ impact for enterprise reward structures and research measures such as an h-index. As an example application, we discuss how this approach could radically improve the quality and the efficiency of scientific output in the setting of research data sharing. We address the challenge of the costly and time-consuming effort needed to bring an innovative idea from the bench (basic research) to the bedside (clinical level)

PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace

Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models. Although numerous publicly available machine learning models exist, they are often unhosted, necessitating end-users to establish their computational infrastructure. Alternatively, these models may only be accessible through paid cloud-based mechanisms, which can prove costly for general public utilization. Moreover, model and data providers require a more streamlined approach to track resource usage and capitalize on subsequent usage by others, both financially and otherwise. An effective mechanism is also lacking to contribute high-quality data for improving model performance. We propose a blockchain-based marketplace called "PredictChain" for predictive machine-learning models to address these issues. This marketplace enables users to upload datasets for training predictive machine learning models, request model training on previously uploaded datasets, or submit queries to trained models. Nodes within the blockchain network, equipped with available computing resources, will operate these models, offering a range of archetype machine learning models with varying characteristics, such as cost, speed, simplicity, power, and cost-effectiveness. This decentralized approach empowers users to develop improved models accessible to the public, promotes data sharing, and reduces reliance on centralized cloud providers

Data-driven Analysis of Remote Work in China during the COVID-19 Pandemic

This paper leverages online content to investigate teleworking forced due to the COVID-19 pandemic -- using China as a primary case study. Telecommuting has become popular since February 2020 primarily due to the pandemic, and people have been slowly returning to their office from May 2020. This study focuses on two time windows in the year 2020 to calculate the growth of different job sectors. Our results indicate the negative impact of teleworking in manufacturing industry, but shows that information technology-related industries are less affected by working from home. This paper also investigates the impact of COVID-19 on the stock market and discussed what plan of action the policy-makers should take to provide a good economic environment. In addition to the overall economic situation, the psychological situation of employees will affect the development of a given industry. Therefore, misinformation in certain Chinese social media channels is also a concern studied in this paper specifically examining the rumors and their latent topics. We hope that our work will initiate a dialogue and collaboration between scientists, policy makers and government officials to use these lessons and engage effectively for the betterment of society

Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives