Using Big Data to Improve Health

PROJECTS

Seasonal flu places heavy burden on human populations and healthcare systems, thus, require permanent surveillance. Current surveillance methods are robust yet slow. With the collaboration of national and international public health institutions, we are developing models that can timely predict flu levels by using a combination of offline and online data (such as search trends and social media shares).

2017 – Ongoing

FUNDING | PTDC IVC ESCT 5337 2012, Fundação para a Ciência e a Tecnologia and DSAIPA/AI/0087/2018, Fundação para a Ciência e a Tecnologia

TEAM | Cláudio Vieira, Miguel Won (past member)

PUBLICATIONS

https://journals.plos.org/ploscompbiol/article/comments?id=10.1371/journal.pcbi.1005330

Antibiotics (Ab) are one of the most important class of medical drugs. Thanks to their discovery and widespread use, bacterial infections that used to be fatal are now treated in a few days. However, these advances came at a cost. When exposed to Ab, bacterial populations can quickly become resistant to them. Indeed, nowadays infections caused by antibiotic resistant bacteria are a serious health problem.

The best way to prevent the evolution of new resistances is to only use Ab when they are necessary. In order to assess how antibiotics are prescribed in Portugal and what we can do to reduce their inappropriate use, we have initiated a collaboration with the Ministry of Health. With their database of medical prescriptions we will: 1) characterize the distribution of antibiotic prescription by medical doctors and identify causes of over-prescription; 2)identify the gold standard for antibiotic prescription for the Portuguese population; and 3) propose interventions to reduce Ab prescription.

October 2017 – March 2020 (predicted end date)

FUNDING | Fundação para a Ciência e Tecnologia – Projeto Piloto em Ciência dos Dados e Inteligência Artificial na Administração Pública

TEAM | Lília Perfeito, Sofia Pinto, Sara Mesquita

Emergency Care Units (ECUs) are medical facilities that deal with unplanned patient turnout, for a very large range of conditions, often urgent or acute, and frequently life-threatening. Therefore, ECUs need to find a difficult balance between having enough resources (human and others) to deal with an unexpected surge in patients, while reducing wasteful practices of sustaining more resources than required. Thus, timely information regarding possible variations in patient inflow is fundamental for proper planning and quality of service. But since a broad spectrum of reasons lead people to ECUs, hospital admissions vary significantly. From acute events, to lack of alternatives, or just out of concern, different reasons have different underlying dynamics, are guided by different factors, timings, and motivations. Thus, a combination of uncertainty and large variability, makes the problem of emergency forecasting a very complex challenge, with great impact on quality of care.

We focus on top drivers of ECU seeking behavior and use a Data Science and Machine Learning (ML) approach to study variations in emergency peaks and possible factors that might predict them. We expect to offer a simple prediction, that can be used by decision-makers and reduce uncertainty in ECU patient inflow.

January 2019 – December 2022 (predicted end date)

FUNDING | Fundação para a Ciência e tecnologia – DSAIPA/AI/0087/2018ADD FCT FUNDING

TEAM | Lília Perfeito (we will be hiring soon, for this project!)

COLLABORATORS | Cláudia Soares, IST, Portugal