scholarly journals Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies

2021 ◽  
Author(s):  
Maurice D. Mulvenna ◽  
Raymond Bond ◽  
Jack Delaney ◽  
Fatema Mustansir Dawoodbhoy ◽  
Jennifer Boger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ethical Issues ◽  
Digital Health ◽  
Time Data ◽  
Digital Products ◽  
Health Technologies ◽  
Digital Phenotyping ◽  
Phenotype Data

AbstractDigital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

scholarly journals Digital Health Interventions for Healthy Aging: A Qualitative User Evaluation and Ethical Assessment

2020 ◽  
Author(s):  
Marcello Ienca ◽  
Christophe Schneble ◽  
Reto Kressig ◽  
Tenzin Wangmo
Keyword(s):  
Older Adults ◽  
Older People ◽  
Healthy Aging ◽  
Ethical Issues ◽  
Digital Health ◽  
Functional Independence ◽  
Community Dwelling ◽  
Healthy Ageing ◽  
Health Technologies ◽  
Community Dwelling Older Adults

Abstract BackgroundDigital health technologies are being increasingly developed with the aim of allowing older adults to maintain functional independence throughout the old age, a process known as healthy ageing. Such digital health technologies for healthy ageing are expected to mitigate the socio-economic effects of population ageing and improve the quality of life of older people. However, little is known regarding the views and needs of older people regarding these technologies. AimThe aim of this study is to explore the views, needs and perceptions of community-dwelling older adults regarding the use of digital health technologies for healthy ageing. MethodFace-to-face, in-depth qualitative interviews were conducted with community-dwelling older adults (median age 79.6 years). The interview process involved both abstract reflections and practical demonstrations. The interviews were transcribed verbatim and analyzed according to inductive content analysis. ResultsThree main themes and twelve sub-themes emerged. The three main themes revolved around the following thematic areas: favorable views and perceptions on technology-assisted living, usability evaluations and ethical considerations. ConclusionsOur study reveals a generally positive attitude towards digital health technologies as participants believed digital tools could positively contribute to improving their overall wellbeing, especially if designed in a patient-centered manner. Safety concerns and ethical issues related to privacy, empowerment and lack of human contact also emerged as key considerations.

scholarly journals The Real Era of the Art of Medicine Begins with Artificial Intelligence (Preprint)

2019 ◽  
Author(s):  
Bertalan Meskó
Keyword(s):  
Artificial Intelligence ◽  
Digital Health ◽  
Medical Science ◽  
Three Dimensional ◽  
Three Dimensional Printing ◽  
The Real ◽  
Art Of Medicine ◽  
Health Technologies ◽  
Based Medicine ◽  
Dimensional Printing

UNSTRUCTURED Physicians have been performing the art of medicine for hundreds of years, and since the ancient era, patients have turned to physicians for help, advice, and cures. When the fathers of medicine started writing down their experience, knowledge, and observations, treating medical conditions became a structured process, with textbooks and professors sharing their methods over generations. After evidence-based medicine was established as the new form of medical science, the art and science of medicine had to be connected. As a result, by the end of the 20th century, health care had become highly dependent on technology. From electronic medical records, telemedicine, three-dimensional printing, algorithms, and sensors, technology has started to influence medical decisions and the lives of patients. While digital health technologies might be considered a threat to the art of medicine, I argue that advanced technologies, such as artificial intelligence, will initiate the real era of the art of medicine. Through the use of reinforcement learning, artificial intelligence could become the stethoscope of the 21st century. If we embrace these tools, the real art of medicine will begin now with the era of artificial intelligence.

Evolution of Digital Technologies and Use of Virtual Assistants in Drug Development

2019 ◽  
pp. 1-20
Author(s):  
Manu Venugopal
Keyword(s):  
Drug Development ◽  
Digital Health ◽  
Critical Role ◽  
Life Sciences ◽  
Digital Technologies ◽  
Development Phase ◽  
Health Technologies ◽  
Daunting Task ◽  
The World

The drug development phase is one of the most time-consuming and expensive stages in the lifecycle of a drug. Marred by patent expirations, price regulations, complexities in disease conditions, life sciences companies are facing a daunting task to bring new molecular entities into the market. Digital health technologies are playing a critical role in addressing some of the challenges faced during drug development. In this chapter, the author talks about the challenges and key trends in the world of drug development, use of new digital health technologies, and the future of drug development. As an example, the author dives into a specific case study on the use of virtual assistants in clinical trials and the benefits of its usage on patients, healthcare professionals, and life sciences companies.

Clinical Integration of Digital Solutions in Health Care: An Overview of the Current Landscape of Digital Technologies in Cancer Care

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Shivank Garg ◽  
Noelle L. Williams ◽  
Andrew Ip ◽  
Adam P. Dicker
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Health Care ◽  
Data Collection ◽  
Patient Outcomes ◽  
Cancer Care ◽  
Digital Health ◽  
Health Technologies ◽  
Health Care Value

Digital health constitutes a merger of both software and hardware technology with health care delivery and management, and encompasses a number of domains, from wearable devices to artificial intelligence, each associated with widely disparate interaction and data collection models. In this review, we focus on the landscape of the current integration of digital health technology in cancer care by subdividing digital health technologies into the following sections: connected devices, digital patient information collection, telehealth, and digital assistants. In these sections, we give an overview of the potential clinical impact of such technologies as they pertain to key domains, including patient education, patient outcomes, quality of life, and health care value. We performed a search of PubMed ( www.ncbi.nlm.nih.gov/pubmed ) and www.ClinicalTrials.gov for numerous terms related to digital health technologies, including digital health, connected devices, smart devices, wearables, activity trackers, connected sensors, remote monitoring, electronic surveys, electronic patient-reported outcomes, telehealth, telemedicine, artificial intelligence, chatbot, and digital assistants. The terms health care and cancer were appended to the previously mentioned terms to filter results for cancer-specific applications. From these results, studies were included that exemplified use of the various domains of digital health technologies in oncologic care. Digital health encompasses the integration of a vast array of technologies with health care, each associated with varied methods of data collection and information flow. Integration of these technologies into clinical practice has seen applications throughout the spectrum of care, including cancer screening, on-treatment patient management, acute post-treatment follow-up, and survivorship. Implementation of these systems may serve to reduce costs and workflow inefficiencies, as well as to improve overall health care value, patient outcomes, and quality of life.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

Implementation of an on-line artificial intelligence district meter area flow meter data analysis system for abnormality detection: a case study

2010 ◽  
Vol 10 (3) ◽  
pp. 437-444 ◽  
Author(s):  
S. R. Mounce ◽  
J. B. Boxall
Keyword(s):  
Artificial Intelligence ◽  
Customer Service ◽  
Service Providers ◽  
Software Tool ◽  
Time Data ◽  
Water Service ◽  
Intelligence System ◽  
Flow Monitoring ◽  
Data Analysis System

Faster detection of bursts saves water, minimises the inconvenience of interruption to customers and decreases the damaging consequences to infrastructure. Flow monitoring techniques are used by water service providers to monitor leakage, generally through offline application of mass balance type calculations and manual observations of change in night line values. This paper presents the combination of real-time data collection (using cello loggers with General Packet Radio Service communications) and a self-learning, online Artificial Intelligence system for detection of bursts at the District Meter Area level. The system components consist of communications software, a data warehouse and a MATLAB application. The online system continuously analysed a set of 146 DMAs in a case study area every hour generating automated alerts in response to abnormal flow. Specific examples are given, including a validation field test, and overall results are presented for a one year period. 36% of alerts were found to correspond to bursts confirmed by repair data or customer burst reports with only 18% ghosts. The results indicate that the software tool has the potential to reduce lost water and improve customer service hence enhancing water service provider's reputations.

scholarly journals Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists (Preprint)

2019 ◽  
Author(s):  
Nicoletta Musacchio ◽  
Annalisa Giancaterini ◽  
Giacomo Guaita ◽  
Alessandro Ozzello ◽  
Maria A Pellegrini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Diabetes Care ◽  
Digital Health ◽  
Descriptive Analysis ◽  
Careful Analysis ◽  
Great Opportunity ◽  
Italian Association ◽  
Number Of Patients

UNSTRUCTURED Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

Sign in / Sign up

Export Citation Format

Share Document