Biography:

Sunandan Chakraborty focuses on data science for social good. Building computational models that leverage vast data sets, he applies them to a broad spectrum of problems in social and environmental science, agriculture, health, and other fields. He draws on diverse data sets (news, social media, images, etc.) and uses tools such as big data analytics, machine learning, information extraction, and time series analysis to compile information and discover knowledge that can lead to solutions.

Before becoming an assistant professor of data science at SOIC, Chakraborty worked with Jennifer Jacquet as a Moore-Sloan postdoctoral researcher at the NYU Center for Data Science. Their award-winning research explored the problem of illegal online wildlife trading, utilizing complex digital text analyses.

Chakraborty was part of the Big Data Group and the Center for Technology and Economic Development while earning his doctorate. He did his research under the supervision of Lakshminarayanan Subramanian at the Courant Institute of Mathematical Sciences of New York University.

Abstract:

Introduction and wide-spread use of Electronic Health Record (EHR), supplemented by scholarly articles published in medical journals, led to a huge repository of information and data about medical and health related topics. However, this data can be overwhelming due to its size and can be hard to analyze because of it heterogeneity and unstructured nature. Automatic text mining approaches on this data can help to minimize the information overload problem, integrate knowledge from disparate sources and discover latent patterns within the data. All this can greatly benefit health care by not only being able to provide better services to the patients but also to the clinicians, service providers and medical researchers.  In this talk I intend to provide a brief survey of various medical text mining techniques used and some typical problems addressed in the domain using these techniques. I shall conclude the talk by giving an overview of my own research in the medical text mining area, which will include "Disease Outbreak Prediction using News Events" and "Unearthing Causal Cues from Medical Texts".

Biography:

Eric V. Holtzclaw is Chief Strategist at PossibleNOW. He’s a researcher, writer, and challenger-of-conventional wisdom. Eric helps strategically guide companies with the implementation of enterprise-wide consent and preference management solutions. Check out his book with Wiley Publishing on consumer behavior – “Laddering: Unlocking the Potential of Consumer Behavior” and his weekly column on Inc.com, “Lean Forward”.

Abstract:

Increasing legislation (such as GDPR) combined with groundbreaking technologies that provide greater insight into consumers' daily lives means healthcare organizations embarking on digital health initiatives must strike a balance between collecting consent and ensuring the consumer is fully informed. Failure to do so impacts the individual's ability to receive the full benefit of care.  PossibleNOW has over 15 years of experience assisting the largest healthcare companies on how to properly collect and capitalize on customer consent.

Biography:

 Since her childhood, PAIN was part of her daily life. Polypathological, her only escape was to study it in order to understand it. Her multidisciplinary university career was the meeting point between her personal and professional life (...).The desire to undertake and act in order to improve her daily life are unifying and essential values for her. In 2016, after three years of technological, economic and social feasibility study, she decided to launch the LUCINE project with the objectives of relieving millions of patients suffering from chronic pain and improving the working conditions of health professionals.

Abstract:

The word is liberated, the pain is expressed. Through #mydoctorsaid, millions of women tell the story of medical erudition and difficult pain management. In response, my enterprise and my partners decided to help billions of women and girls in their daily lives. We targeted the problem of menses. Because today there are 3 774 991 488   women in the world, 1 893 750 607 women of childbearing, and more than 90% affected by Dysmenorrhea. We conducted a first study in January 2018 on 1200 women. Results: 86% of women have menstrual pain. 15% consume level 2 painkillers medication during menses and 30% of women over 35 years have never been to a gynecologist. In consequence, we have created the first digital solution with AI able to measure in 15-seconds the sensibility of pain. And realize a pre-diagnosis action to be carried out to support women's health throughout their lives. But how to reduce the feeling of pain? This digital therapeutic for women health reduces the feeling of pain on patient who suffer from endometriosis. We used neurostimulation to produce a natural painkiller in a patient brain. During this congress, we present the preliminary result of the Lucine DTX clinical trial.

In parallel, we are working with an association whose aim is to support students to create a virtual company. in exclusivity for this congress, we decided to share the first result of this experience. Lucine help young people between the ages of 12 and 25 build their DTX. This year, the important challenge is student in mixed team (boys and girls) to solve a big and difficult problem: "I have pain during my menses".

Biography:

Abstract:

Biography:

Charles Chukwuemeka Onoh has his expertise in Environmental toxicology, Therapeutics, Occupational health and safety. He is well experienced in research methodologies and has supervised both undergraduates and postgraduate students. He is a resource person for so many institutions including the Centre for Occupational Health, Safety and Environment, Port Harcourt and Department of Gender and Global Health, Institute of Women, Gender and Development Studies, Owerri, Nigeria.  Presently, he is working on the use of smart phones in the measurement of blood pressure and detection of some emergency cardiac abnormalities.

Abstract:

Cardiometabolic risk is a common occurrence among workers and its interaction on health is devastating especially in the developing world where there is a double burden of disease. Cardiometabolic risk is a constellation of factors which includes type-2 diabetes, hypertension, dyslipidaemia, insulin resistance and obesity. It is a time-bomb waiting to explode and Nigeria is witnessing an increasing trend. This study was designed to develop a Computer Software for the assessment of cardiometabolic risk. The study involved work–site based cross-sectional, analytical, questionnaire administration, laboratory estimation, anthropometric and clinical measurements. Blood glucose estimation was determined by the Glucose oxidase/Peroxidase method of Trinder. The lipid indices were  determined using a Standard method of Freidewald. Data were analyzed using Microsoft Excel 2016, XLSTAT 2015, Minitab version 17 and Microsoft Corporation Visual Basic. R- Programming and Microsoft Virtual Basic were used for the development of the Computer Software for cardiometabolic risk assessment. The Computer Software was displayed as a Plate. In Nigeria, there is paucity of funds and application of digital health. This Software serves as a comprehensive medical surveillance for cardiometabolic risks.

Biography:

Mahrin Rahman is a third year medical student at the Burrell College of Osteopathic Medicine at New Mexico State University. Her passion for digital healthcare and neurology led her to working with the Bhavya Rehani, Ankur Bharija and William Dillon, the founders of Health 4 The World. Along with a stroke app they built the largest online free education platform to benefit medical professionals in under-resourced areas. With their advanced technology they utilize virtual reality and machine learning for education in medicine and brain exercises.
 

Abstract:

Health4TheWorld is a nonprofit organization that was founded with the intent to utilize technology as a way to empower doctors and patients in low resource communities globally. Via the creation of a mobile technology application, stroke patients and their caregivers are given the opportunity to communicate via the app, learn mobility exercises, increase stroke knowledge, and schedule reminders to perform activities of daily living. Preliminary usability data from the pilot site at the National Institute of Neurological Sciences in Nepal are analyzed here as a way to understand usability, learnability, efficiency, satisfaction, and consistency. The survey was available to registered users in 2017-2018 and 55 users responded. The majority were family members (74.6%) with 14.6% being patients. The mean age was 58.5, and 58.2% were male. 63.6% of respondents had 0-6 years of education, 27.3% had 7-12 years and 7.27% had 12+ years of education. 96.4% of participants strongly agreed that they would use the app frequently. 98.2% strongly agreed/agreed that the mobile app was easy to use. 92.7% of respondents found the various functions in the app were well integrated. 92.7% strongly agreed that most people would learn to use the app quickly. 90.9% felt confident using the app. This preliminary data shows that this stroke app is fairly intuitive to use, and that it helps to fill a gap in post stroke care that was previously unavailable. Respondents liked to use the app frequently, and felt confident navigating through the various portals.