Digital Biomarkers Archives - Healthentia

Digital Therapeutics: Virtual Coaching Powered by Artificial Intelligence on Real-World Data

System — Fri, 17 Dec 2021 10:39:11 +0000

CATEGORY: eHealth, patient reported outcomes, e-clinical platform, smart eHealth

SOURCE: Frontiers Comp. Sci., 16 December 2021; BOOK DOI Link, Chapter DOI Link

Digital Therapeutics: Virtual Coaching Powered by Artificial Intelligence on Real-World Data

Harm op den Akker ; Miriam Cabrita; Aristodemos Pnevmatikakis ^*

Innovation Sprint, Brussels, Belgium
Abstract

An ever-increasing number of people need to cope with one or more chronic conditions for a significant portion of their life. Digital Therapeutics (DTx) focused on the prevention, management, or treatment of chronic diseases are promising in alleviating the personal socio-economic burden caused. In this paper we describe a proposed DTx methodology covering three main components: observation (which data is collected), understanding (how to acquire knowledge based on the data collected), and coaching (how to communicate the acquired knowledge to the user). We focus on an emerging form of automated virtual coaching, delivered through conversational agents allowing interaction with end-users using natural language. Our methodology will be applied in the new generation of the Healthentia platform, an eClinical solution that captures clinical outcomes from mobile, medical and Internet of Things (IoT) devices, using a patient-centric mobile application and offers Artificial Intelligence (AI) driven smart services. While we are unable to provide data to prove its effectiveness, we illustrate the potential of the proposed architecture to deliver DTx by describing how the methodology can be applied to a use-case consisting of a clinical trial for treatment of a chronic condition, combining testing of a new medication and a lifestyle intervention, which will be partly implemented and evaluated in the context of the European research project RE-SAMPLE (REal-time data monitoring for Shared, Adaptive, Multi-domain, and Personalised prediction, and decision making for Long-term Pulmonary care Ecosystems).

Keywords: digital biomarkers, machine learning, ai clinical trials, Healthentia, real-world data, e-clinical platform

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Discovering Composite Lifestyle Biomarkers With Artificial Intelligence From Clinical Studies to Enable Smart eHealth and Digital Therapeutic Services

System — Mon, 06 Sep 2021 15:01:45 +0000

Topics: eHealth, patient reported outcomes, e-clinical platform, smart eHealth

SOURCE: Front. Digit. Health, 06 September 2021; BOOK DOI Link, Chapter DOI Link

Discovering Composite Lifestyle Biomarkers With Artificial Intelligence From Clinical Studies to Enable Smart eHealth and Digital Therapeutic Services

Sofoklis Kyriazakos ^1,2* ; Aristodemos Pnevmatikakis ¹ ; Alfredo Cesario ^1,3 ; Konstantina Kostopoulou ¹ ; Luca Boldrini ⁴; Vincenzo Valentini ^4,5 ; Giovanni Scambia ⁴

¹Innovation Sprint Sprl, Brussels, Belgium

²Business Development and Technology, Aarhus University, Herning, Denmark

³Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy

⁴Advanced Radiation Therapy, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy

⁵Università Cattolica del Sacro Cuore, Rome, Italy

^*Author to whom correspondence should be addressed

Abstract

Discovery of biomarkers is a continuous activity of the research community in the clinical domain that recently shifted its focus toward digital, non-traditional biomarkers that often use physiological, psychological, social, and environmental data to derive an intermediate biomarker. Such biomarkers, by triggering smart services, can be used in a clinical trial framework and eHealth or digital therapeutic services. In this work, we discuss the APACHE trial for determining the quality of life (QoL) of cervical cancer patients and demonstrate how we are discovering a biomarker for this therapeutic area that predicts significant QoL variations. To this extent, we present how real-world data can unfold a big potential for detecting the cervical cancer QoL biomarker and how it can be used for novel treatments. The presented methodology, derived in APACHE, is introduced by Healthentia eClinical solution, and it is beginning to be used in several clinical studies.

Keywords: digital biomarkers, machine learning, ai clinical trials, Healthentia, real-world data, e-clinical platform

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‘Discovering biomarkers’

System — Mon, 19 Oct 2020 10:39:56 +0000

In Innovation Sprint we believe in the potential of the ‘missing data’ in clinical studies, such as lifestyle, activity, nutrition, sleep, to derive conclusions about the efficacy of treatments, as well as to bridge the gap between clinical research and eHealth/DTx. In the context of exploring ways to make use of such data, we started around a year ago the Digital Biotech activity, which involves the discovery of digital composite contextual biomarkers.

A biomarker is a naturally occurring characteristic by which a pathological or physiological process can be identified. A digital biomarker comprises of objective, quantifiable physiological and behavioral data, measured utilising digital portable, wearable, implantable or digestible devices, to be used to predict and manage health-related outcomes.

Innovation Sprint has built a composite contextual biomarker-based οn multiple aspects of Real-World Data (RWD), collected from people unobtrusively, while following-up their normal living routine. It is composite in the sense that it is not based on a single measurement, but rather on multiple diverse measurements (objective RWD) and peoples’ reports (subjective RWD). It is contextual in the sense that not only the person is measured, but also the person’s lifestyle context: social and environmental aspects complement the more traditional physiological and psychological ones.

Our RWD

At Innovation Sprint we are strong advocates of the empirical knowledge that lifestyle is a strong determinant of health. Hence our biomarker is based on RWD spanning four important aspects of a person’s lifestyle:

Physiological RWD quantifies physical behaviour (active vs sedentary lifestyle as measured by steps walked, floors climbed, activity types, minutes in different intensity levels or heart rate zones, resting heart rate, sleep characteristics) and includes body info (height, weight, gender, race), nutrition (water, other liquids, food) and symptoms (body temperature, cough, diarrhea, headache, nausea, pain, etc.).

Psychological RWD quantifies at a simple level mood, and in more complex situations mental state collected via elaborate, domain-specific questionnaires. Measurements can also play a role, either directly e.g. facial expression recognition, or indirectly, e.g. weather where people are living).

Social RWD quantifies social activity of people. This can be measured indirectly from the usage of the phone (diversity, duration, frequency of calls) and social media (diversity, number, frequency of interactions). More direct information can be reported using questionnaires on activities with friends, family or co-workers.

Environmental RWD indicates the quality of life. Usually, reported by the users. Measurements of living or working environment quality are made with commercial devices (e.g. air quality meters).

AI for discovering our biomarker

Biomarker discovery at Innovation Sprint is done in three stages

Definition stage, where the domain experts select the clinically significant outcomes that need to be predicted by the biomarker(s).

Manual RWD selection stage, where domain knowledge is applied to refine our generic RWD selection into those lifestyle aspects that are relevant to the disease/condition in question.

Iterative design stage: Machine Learning/AI algorithms are used to train a proprietary classifier using the elected RWD to predict the selected clinically significant outcomes. The classifier is applied on new data yielding predictions and insights leading to digital therapeutics.

Validating our approach

We employed RWD collected over 7 years to train a biomarker that predicts significant weight changes. Such a biomarker is important for patients with several diseases (e.g. NAFLD), as well as for the general population interested in well-being. We achieved over 80% or correct prediction of the outcome, while we also analysed the different RWD aspects that led each individual to positive or negative outcomes, in order to offer personalized coaching services.

As we speak, we are utilising the same approach in other therapeutic areas, e.g. cervical cancer, to predict low toxicity events. Starting from 2021 we will validate this hypothesis in much larger cohorts, targeting –among others- COPD patients with Cardiovascular Disease comorbidities.

We will keep you update on our observations and findings!

Aristodemos Pnevmatikakis
R&D Director, Innovation Sprint

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Boosting our Digital Biomarker Activity with the new Coaching Program

System — Tue, 01 Sep 2020 09:55:10 +0000

Innovation Sprint is participating in a new coaching program, developed by incubators.brussels, all incubators of the Brussels-Capital Region and supported by Innoviris Brussels, to boost our #HealthentiaDigitalBiomarker activity. In this program, Innovation Sprint is receiving coaching and mentoring services by domain experts to strengthen the Digital Biomarker activity as a new pillar for our product Healthentia.

Healthentia Digital Biomarker activity introduces a novel approach of using RWD in clinical studies (among them physiological, psychological, sociological, and environmental information) to discover digital contextual biomarkers by means of Machine Learning algorithms. This activity has already resulted in evidence about the prediction capability of such a digital biomarker that can project clinical endpoints and drive – if used outside the context of a clinical study – eHealth and DTx services.

As incubees of BLSI, one of the program's affiliated incubators, together with the relevance of our scientific research activity makes Innovation Sprint eligible to the program's priorities and therefore we can receive support and promotion of our progress from domain experts.

Program Features

This program has some features that make it different from the others.

• Initially, it will work as a diagnostic tool that will identify the points on which to work in priority and their progress during the program. It is essentially a personalized journey that will be adjusted according to the needs and the stage of maturity of each startup.

• It is also a “lean” and iterative approach as on the one hand the strategic aspects are discussed with an advisory board (composed of highly qualified coaches and entrepreneurs). And on the other hand, the operational aspects are dealt with by experienced coaches and experts.

• Finally there are the individual and collective coaching sessions, where the collective sessions may involve start-ups from the other 3 incubators.

Program Expectations

• We expect that the program will assist our effort to refine our strategy and address the new market of Digital Biotechnology, exploiting the prosperous ecosystem of the region; thus achieving a high impact.

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