EIT i-Days 2023: Artificial Intelligence and Machine Learning in Patient Journey
What are i-Days?
The i-Days event, powered by EIT Health, focuses on innovation and entrepreneurship in healthcare and associated domains. i-Days also provide a good environment to nurture soft skills such as teamwork and communication. This year, HIVE Innovation Center, through EIT Health i-DAYS 2023, brings in the public attention one of the most discussed topic: AI and Machine learning in healthcare.
Currently, intelligent tools and devices are the promise of easier, secure and exact prevention and treatment for any affection or medical situation. Even if the introduction in medicine of AI & Machine learning was made long ago, the presence of them is still limited. Lately, this way of innovation has started a huge path of growth, every health area being targeted in involving intelligent solutions in the ways of working.
EIT i-Days 2023: Artificial Intelligence and Machine Learning in Patient Journey - Hackathon
EIT i-Days 2023: Artificial Intelligence and Machine Learning in Patient Journey is the 5th hackathon in a row organized by HIVE Innovation Center and EIT Health, in which young minds have the opportunity to present innovative approaches for AI and Machine learning implementation in healthcare, under professional mentorship and inter-curricular environment.
About the Hackathon:
This hackathon will be a 2-day event, ONSITE, hosted by HIVE Innovation. The hackathon will concentrate on AI & Machine learning in health, having few broad challenges as guidance. In the 2 days, participants will be able to receive mentoring from various domains (IT, tech, healthcare, business, e-Health, innovation etc.) and to create conceptual innovative ideas based on the proposed challenges. These 2 days will be preceded by 2 online training sessions, so the participants will acquire a few new soft skills and knowledge.
When?
27-28 October 2023 + 2 days a priori for trainings (1-2h/day)
Where?
Onsite, John Snow room – 27-28 Ocotober (Universitas Hotel, Plopilor 52-54, Cluj Napoca, Romania)
Online (ZOOM, Discord) – 25-26 October (training hours)
The participation will be done on teams. Each team can contain 2-4 people each, preferably from at least 2 different areas (eg: IT/soft dev/computational sciences and medicine/pharmacy/nursing/biology etc.).
Participants can register by pre-formed teams or individuals and we will organize a team formation before the hackathon.
Click on the “REGISTER NOW” button and accept the challenge and improve the current neuro related issues.
Agenda
18:00-19:00 🕙 Training → Pitching your idea
Trainer: Razvan Chereches
18:00-19:00 🕓 Training → Innovation in healthcare and patient management
Trainer: Ion Gheorghe Petrovai
17:00-17:30 Arrival and welcome
17:30-18:30 Welcome & program presentation
18:30-19:30 Ideas presentation
19:30-20:00 Team development
20:00-20:30 Networking and planning
8:30-09:00 – Arrival and welcome
09:00-09:30 – Coffee, breakfast & Q&A
10:00-14:00 – Mentoring
Working hours, networking, lunch
17:00-18:30 – Final pitches
19:00 – Prizes & closing
Prizes
1st -1000 euro
2nd – 500 euro
Event Steps
- Event registration;
- Pick a challenge for your team and form an innovative idea;
- Mentorship and validation
- Polish the innovative idea
- Idea pitching & jury evaluation
- Win one of our prizes
Can I participate online?
The event is fully on site (except the trainings on 25-26 october) and we expect participants to be in the room. In this way you get the most value out of the buzzing interaction with mentors and speakers during the event. Unfortunately, we cannot accommodate travel expenses, but check with your local university or partners if they can help. We recommend using your student benefits for traveling and UBB student houses for accommodation.
Working language
The official language of the hack is English. But hey, you’ve got the freedom to team up and collaborate, so go ahead and chat in whatever lingo feels right to you when you’re with your crew.
Challenges
Intelligent Tools and Devices in Daily Life
Description: AI (Artificial Intelligent) and Machine Learning tools and devices for prevention and treatment in healthcare are currently the epitome of user-friendly and accessible technologies to enhance healthcare outcomes. These tools are designed to identify potential health risks, predict diseases, provide personalized treatments, and improve overall patient care, all while being easy to use and operate for healthcare professionals and individuals alike.
Challenge: Propose a device or tool that would be able to offer a better experience for patients and/or the general population.
Example: Propose a tool to predict the changes in blood pressure for diabetic patients based on daily activity.
Predictability in Electronic Medical Records
Description: Electronic Medical Records (EMRs) refer to digital versions of patients’ medical history, diagnoses, medications, treatment plans, and other essential healthcare information. EMRs are stored and managed electronically by healthcare providers or selected third parties, enabling easier access, sharing, and analysis of patient data within the various healthcare systems.
Challenge: Propose a solution that can be implemented in a private Romanian healthcare system which can provide possible future medical concepts (eg, disorders) from a patient’s EMR.
Example: Propose a solution to predict the possibility of developing high blood pressure related diseases, targeted on Regina Maria health network patients, based on the patient’s EMR.
AI and Machine Learning in Public and Environmental Health
Description: Human and environmental health are one of the main factors in improving a patient’ journey. This aspect should be evaluated by public health, medical and environmental monitors. However, due to the great variety of people and environments, monitors cannot provide very accurate data at a large scale regarding various aspects of living (patients’ monitoring, personalized medicine, mental health development and monitoring, predictive analyses in people’s health, early disease detection, air and water quality, pollution monitoring, biodiversity monitoring, quality of life monitoring etc.).
Challenge: Propose an intelligent tool or device which could be able to predict the incidence of a disease based on an environmental factor.
Example: Propose a tool that could predict an epidemic outburst based on public water network water quality in a rural area.
Strategies for Prolonging Life: Intelligent Solutions for Factors That Hasten the Aging Process
Description: Using machine learning algorithms vast datasets of biomarkers, including genetic, proteomic, and metabolomic data, can be analyzed to identify patterns associated with aging. These algorithms can detect subtle changes over time, helping to track the aging process more effectively. Machine learning models can utilize historical health data and biomarker information to predict an individual’s risk of age-related diseases and health outcomes. These predictions, embedded in AI-powered health apps and virtual assistants, can empower individuals to take preventive measures and make informed decisions about lifestyle, treatment, and interventions, by providing personalized recommendations for diet, exercise, sleep, and stress management based on individual health profiles.
Challenge: Propose an intelligent tool that can make lifestyle recommendations to patients with a specific disease.
Example: Propose a tool that can improve and prolong the life of patients with Diabetes II.
Mentors
