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Using Social Media to Discover Infectious Diseases

Most Infectious diseases spread within a short period of time in a given population segment. Therefore, early detection of disease activity and rapid response are required to prevent growing numbers of infected patients and excessive medical expenses. A user’s symptom-related expressions on social media can be used to identify new infectious diseases in real time with high accuracy.  However, the naming or defining of a new infectious disease is a longer process that happens over a longer period of time.  For example, individuals did not use the term “Zika” on social media when expressing their symptoms before the CDC gave the new virus a name. This work proposes a bottom-up approach that uses social media data to discover new infectious diseases by collecting symptom data without prior information, such as disease names. This research can help biomedical professionals expedite identification to prevent diseases from spreading. This may also translate to lower costs for the healthcare system due to more accurate resource allocation decisions.

Lin, S., Tucker, C. S., & Kumara, S. (2017). An unsupervised machine learning model for discovering latent infectious diseases using social media data. Journal of Biomedical Informatics

For more information, please contact: Conrad Tucker, PhD at

Collaborative Learning Beneficial in the Healthcare Setting

Collaborative learning is a technique through which individuals or teams learn together by capitalizing on one another’s knowledge, skills, resources, experience, and ideas. Clinicians providing congenital cardiac care may benefit from collaborative learning given the complexity of the patient population and team approach to patient care.  Systems engineers performed broad-based time-motion and process analyses of congenital cardiac care programs at five Pediatric Heart Network core centers. Machine learning was performed to identify and prioritize areas of improvement for best return of investment. Rotating multidisciplinary team conducted site visits and facilitated deep learning and information exchange.

Collaborative discussion and machine learning findings both pinpointed that duration of mechanical ventilation following infant cardiac surgery was one key variation that could impact a number of clinical outcomes. A consensus clinical practice guideline was developed and implemented by multidisciplinary teams from the same five centers. The 1-year prospective initiative was completed in May 2015 and the findings were published in 2016. The study demonstrated a remarkable increase in early extubation from 12% to 67% utilizing this approach. Collaborative learning that uses multidisciplinary team site visits and information sharing allows for rapid structured fact-finding and dissemination of expertise among institutions. System modeling and machine learning approaches objectively identify and prioritize focused areas for guideline development. The collaborative learning framework can potentially be applied to other components of congenital cardiac care and provide a complement to randomized clinical trials as a method to rapidly inform and improve the care of children with congenital heart disease. The design concept is generalizable and applicable to other clinical care areas. This framework is particularly valuable in complexes processes that require participation of various health professionals such as nurses, physicians, respiratory therapists and other allied health personnel. Moreover, the model allows clinicians at peer institutions and opportunity to share experience regarding the implementation process of a clinical practice guideline.

This model is of added importance in pediatric hospitals since there have not been strong external forces, such as Medicare, to establish a standard practice. The collaborative learning model allows centers to understand how health care teams can perform at the highest level.  This knowledge cannot be transferred without direct observation and discussions between host and visiting programs.  Collaborative learning has proven to be a powerful tool in advance medicine in adults. This promising application in pediatrics should serve as a launching point for numerous projects.  It will allow rapid transmission of best practice, thereby improving the outcomes for children with complex medical conditions such as congenital heart disease.

Wolf, M. J., Lee, E. K., Nicolson, S. C., Pearson, G. D., Witte, M. K., Huckaby, J., … & Pediatric Heart Network Investigators. (2016). Rationale and Methodology of a Collaborative Learning Project in Congenital Cardiac Care. American Heart Journal, 174, 129-137.

For more information, please contact: Eva Lee, PhD at