17/09/2018
Workshop ช่วงบ่าย Day1
Group 1. Pitfalls of health Big Data
Leo Anthony Celi / Atipong Pathanasethpong
Group 2. Applied Statistical Learning in Python
Calvin Chiew
Group 3. Design Thinking and Human-Centered Design
Tony Gallanis
Group 4. Image Recognition in Healthcare
Guido Davidzon
Group 5. Machine Learning Tools in Healthcare Analytic
Wanida Kanarkard
Group 1. Pitfalls of health Big Data
Leo Anthony Celi / Atipong Pathanasethpong
The adoption of electronic health records (EHR) has created new opportunities for clinical research using large, rich patient-level databases. With these data, researchers are in a position to approach questions with statistical power previously unheard of in medical research. In this workshop, we present and discuss challenges in the use of EHR data for research, as well as explore the unique opportunities provided by these data.
Group 2. Applied Statistical Learning in Python
Calvin Chiew
This workshop aims to introduce clinicians to popular statistical methods used in machine learning, without delving into the underlying mathematical theory. We will focus on the random forest and support vector machine for classification, as well as general concepts of model fit and cross-validation. In the hands-on exercise, you will be asked to implement and evaluate these models on a clinical prediction problem. No prior programming experience is assumed. Basics of the Python language and Jupyter notebook environment will be covered.
Group 3.Design Thinking and Human-Centered Design
Tony Gallanis
Frailty is an unmet challenge around the world. With an aging population that will live longer than ever before, the needs of the elderly are of great importance. The science and research behind aging and the frailty phenotype are increasing rapidly to address our aging society. This presentation will cover the influx of new research on frailty among the elderly and teach design thinking solutions through human-centered design to rapidly overcome the challenges of an aging population. The presentation will specifically showcase avenues for machine learning in relation to human-centered design and propose applications in society.
Group 4. Image Recognition in Healthcare
Guido Davidzon
Interpretation of imaging data is paramount in the practice of modern medicine. The number and complexity of medical images continues to grow together with the awareness of human errors in healthcare. These could challenge the scalability of human-only interpretation of medical images. Recent advances in deep learning show that computers can aid humans in the task of analyzing medical images with the promise of increasing accuracy and maybe improving clinical outcomes. This workshop will introduce participants to the use of machine learning for image classification. Participants will initially learn about recent work in AI and medical imaging and then apply newfound knowledge.
Group 5. Machine Learning Tools in Healthcare Analytic
Wanida Kanarkard
Machine Learning (ML) is the fastest rising topic in health analytics is of extreme challenge. The aim of Machine Learning is to develop algorithms which can learn and progress over time and can be used for predictions. It offers a variety of alerting and risk management decision support tools, targeted at improving patients' safety and healthcare quality. This workshop will introduce machine learning tools e.g. H2O, Google Colaboratory, Microsoft ML are built to solve different challenging problems in healthcare.
