Machine learning for emerging infectious disease field responses.

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.

The COVID-19 crisis silver lining: interprofessional education to guide future innovation.

Globally, the advent and rapid spread of the COVID-19 virus has created significant disruption to health professions education and practice, and consequently interprofessional education, leading to a model of learning and practicing where much is unknown. Key questions for this ongoing evolution emerge for the global context leading to reflections on future directions for the interprofessional education field and its role in shaping future practice models. Health professions programs around the world have made a dramatic shift to virtual learning platforms in response to closures of academic institutions and restrictions imposed on learners accessing practice settings. Telemedicine, slow to become established in many countries to date, has also revolutionized practice in the current environment. Within the state of disruption and rapid change is the awareness of a silver lining that provides an opportunity for future growth. Key topics explored in this commentary include reflection on the application of existing competency frameworks, consideration of typology of team structures, reconsideration of theoretical underpinnings, revisiting of core dimensions of education, adaptation of interprofessional education activities, and the role in the future pandemic planning. As an international community of educators and researchers, the authors consider current observations relevant to interprofessional education and practice contexts and suggest a response from scholarship voices across the globe. The current pandemic offers a unique opportunity for educators, practitioners, and researchers to retain what has served interprofessional education and practice well in the past, break from what has not worked as well, and begin to imagine the new.