360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360 Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.Comment: QCRI Technical Repor

Dwarna : a blockchain solution for dynamic consent in biobanking

Dynamic consent aims to empower research partners and facilitate active participation in the research process. Used within the context of biobanking, it gives individuals access to information and control to determine how and where their biospecimens and data should be used. We present Dwarna—a web portal for ‘dynamic consent’ that acts as a hub connecting the different stakeholders of the Malta Biobank: biobank managers, researchers, research partners, and the general public. The portal stores research partners’ consent in a blockchain to create an immutable audit trail of research partners’ consent changes. Dwarna’s structure also presents a solution to the European Union’s General Data Protection Regulation’s right to erasure—a right that is seemingly incompatible with the blockchain model. Dwarna’s transparent structure increases trustworthiness in the biobanking process by giving research partners more control over which research studies they participate in, by facilitating the withdrawal of consent and by making it possible to request that the biospecimen and associated data are destroyed.peer-reviewe

Personal information privacy: what's next?

In recent events, user-privacy has been a main focus for all technological and data-holding companies, due to the global interest in protecting personal information. Regulations like the General Data Protection Regulation (GDPR) set firm laws and penalties around the handling and misuse of user data. These privacy rules apply regardless of the data structure, whether it being structured or unstructured. In this work, we perform a summary of the available algorithms for providing privacy in structured data, and analyze the popular tools that handle privacy in textual data; namely medical data. We found that although these tools provide adequate results in terms of de-identifying medical records by removing personal identifyers (HIPAA PHI), they fall short in terms of being generalizable to satisfy nonmedical fields. In addition, the metrics used to measure the performance of these privacy algorithms don't take into account the differences in significance that every identifier has. Finally, we propose the concept of a domain-independent adaptable system that learns the significance of terms in a given text, in terms of person identifiability and text utility, and is then able to provide metrics to help find a balance between user privacy and data usability

Advocating for Action Design Research on IT Value Creation in Healthcare

Today there is mixed evidence that health IT decreases costs and/or improves care quality in the US. Some of the same factors that have driven delays in realizing the benefits from IT investments in other industries (i.e., time consuming process changes) are apparent in the U.S. healthcare industry, which is only now digitizing its fundamental patient data, the electronic health record. The healthcare industry itself is in transition and new IT may not provide full benefit unless it is accompanied with a restructuring of healthcare delivery. Traditional ex post approaches to measuring IT value will limit the ability of healthcare IT value researchers to add value to practice now especially as government incentives in the US drive significant investment. But generalizing results from traditional IT value research to the healthcare setting is risky due to differences between healthcare and other industries. I advocate for action design research that uses existing theory as a foundation, but adapts it to the specific unique characteristics of this industry. By actively participating in the design and evaluation of new socio-technical systems, IT value researchers can generate grounded theory to explain value creation in healthcare while influencing practice now

Application Of Blockchain Technology And Integration Of Differential Privacy: Issues In E-Health Domains

A systematic and comprehensive review of critical applications of Blockchain Technology with Differential Privacy integration lies within privacy and security enhancement. This paper aims to highlight the research issues in the e-Health domain (e.g., EMR) and to review the current research directions in Differential Privacy integration with Blockchain Technology.Firstly, the current state of concerns in the e-Health domain are identified as follows: (a) healthcare information poses a high level of security and privacy concerns due to its sensitivity; (b) due to vulnerabilities surrounding the healthcare system, a data breach is common and poses a risk for attack by an adversary; and (c) the current privacy and security apparatus needs further fortification. Secondly, Blockchain Technology (BT) is one of the approaches to address these privacy and security issues. The alternative solution is the integration of Differential Privacy (DP) with Blockchain Technology. Thirdly, collections of scientific journals and research papers, published between 2015 and 2022, from IEEE, Science Direct, Google Scholar, ACM, and PubMed on the e-Health domain approach are summarized in terms of security and privacy. The methodology uses a systematic mapping study (SMS) to identify and select relevant research papers and academic journals regarding DP and BT. With this understanding of the current privacy issues in EMR, this paper focuses on three categories: (a) e-Health Record Privacy, (b) Real-Time Health Data, and (c) Health Survey Data Protection. In this study, evidence exists to identify inherent issues and technical challenges associated with the integration of Differential Privacy and Blockchain Technology

Healthcare 5.0 Security Framework: Applications, Issues and Future Research Directions

Healthcare 5.0 is a system that can be deployed to provide various healthcare services. It does these services by utilising a new generation of information technologies, such as Internet of Things (IoT), Artificial Intelligence (AI), Big data analytics, blockchain and cloud computing. Due to the introduction of healthcare 5.0, the paradigm has been now changed. It is disease-centered to patient-centered care where it provides healthcare services and supports to the people. However, there are several security issues and challenges in healthcare 5.0 which may cause the leakage or alteration of sensitive healthcare data. This demands that we need a robust framework in order to secure the data of healthcare 5.0, which can facilitate different security related procedures like authentication, access control, key management and intrusion detection. Therefore, in this review article, we propose the design of a secure generalized healthcare 5.0 framework. The details of various applications of healthcare 5.0 along with the security requirements and threat model of healthcare 5.0 are provided. Next, we discuss about the existing security mechanisms in healthcare 5.0 along with their performance comparison. Some future research directions are finally discussed for the researchers working in healthcare 5.0 domain

Using Blockchain Technology in Smart Life Applications

مقدمة: Blockchain هي قاعدة بيانات يتم تخزينها بترتيب زمني بطريقة آمنة ومستقرة. كانت Bitcoin هي التطبيق الأولي لتقنية Blockchain ، ولكن نظرًا لفوائدها من حيث الأمان والخصوصية والتحكم الذاتي ، فقد تم اعتمادها منذ ذلك الحين من قبل مجموعة متنوعة من التطبيقات. يتم إنشاء تقنية Blockchain عن طريق ربط الكتل معًا بشكل مشفر. نظرًا لأن كل كتلة تحتوي على كلٍ من التجزئة الخاصة بها وتجزئة الكتلة السابقة لها ، فلا يمكن لأي شخص خارجي كسر السلسلة. تُستخدم تقنية Blockchain ضمن مجموعة متنوعة من التطبيقات ، بما في ذلك الصناعية والتجارية والأمنية وسلسلة التوريد وإنترنت الأشياء وغيرها. هذا لأنه يتميز بمزايا التحكم في البيانات وتنظيمها وتخزينها. الغرض من هذه المقالة هو سرد بعض التطبيقات ومجالات التطوير الخاصة بـ Blockchain.   طرق العمل: في تقنية Blockchain ، يعد الأمان والاستقرار (الثبات) واللامركزية من بين الأشياء المهمة التي تجعل هذه التقنية مفيدة في مختلف مجالات الحياة التي تخدم المستخدم. تم استخدام هذه المزايا لحل العديد من المشكلات التي تواجه الشبكة ، بما في ذلك المشكلات المتعلقة بالإنتاجية ووقت المعالجة وقابلية التوسع. تم استخدام طرق مختلفة في الحل ، بما في ذلك العمل على تغيير هيكل الشبكة ، واختيار عقدة أساسية (المدير) ، والتعدين الموازي ، والتنافس مع عمال المناجم الآخرين. الاستنتاجات: ازداد الميل إلى استخدام تقنية Blockchain في العديد من المجالات ، بما في ذلك المالية والزراعية والتجارية والصحية وإنترنت الأشياء وغيرها ، نظرًا لمزاياها ، بما في ذلك اللامركزية والتوزيع والموثوقية والاستقرار. هناك اتجاهات أخرى عملت على تطوير أداء وكفاءة وأمان نظام Blockchain نفسه في إنترنت الأشياء والرعاية الصحية وسلسلة التوريد والقطاع المصرفي والتسويق الرقمي بالإضافة إلى الدراسات التي تشمل تحسين الكفاءة والأمان وتطوير النظام.Background: Materials and Methods:      In Blockchain technology, security, stability (immutability), and decentralization are among the important things that make this technology useful in various areas of life that serve the user. These advantages were used to solve many problems facing the network, including those of productivity, processing time, and scalability. Various methods were used in the solution, including working on a change in the network structure, choosing a basic node (the manager), parallel mining, and competing with other miners. Results:     Through recent studies, it has been shown that the Blockchain technology has been used in various fields, as it is characterized by many advantages, the most important of which are security, decentralization, and stability. Because of these advantages, it outperforms other technologies. Conclusion:     Due to its advantages, the tendency to use blockchain technology has increased in many fields, including financial, agricultural, commercial, health, the Internet of Things, and others. It includes decentralization, distribution, reliability, and stability. There are other trends that have worked to improve the performance, efficiency, and security of the blockchain system itself, In the Internet of Things, healthcare, supply chain management, the banking sector, and digital marketing In addition to studies that include improving the efficiency, security, and development of the system

Model Cards for Model Reporting

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation