Predicting Responses to Mechanical Ventilation for Preterm Infants with Acute Respiratory Illness using Artificial Neural Networks

Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real‐time, based on ventilation monitoring up to the point of analysis. In this work, recurrent neural networks are proposed for predicting future ventilation parameters due to the highly nonlinear behavior of the ventilation measures of interest and the ability of recurrent neural networks to model complex nonlinear functions. The resulting application of this particular class of neural networks shows promise in its ability to predict future responses for different ventilation modes. Towards improving care and treatment of preterm newborns, further development of this prediction process for ventilation could potentially aid in important clinical decisions or studies to improve preterm infant health

A Decision Support System for Diagnostics and Treatment Planning in Traumatic Brain Injury.

Traumatic brain injury (TBI) occurs when an external force causes functional or structural alterations in the brain. Clinical characteristics of TBI vary greatly from patient to patient, and a large amount of data is gathered during various phases of clinical care in these patients. It is hard for clinicians to efficiently integrate and interpret all of these data and plan interventions in a timely manner. This paper describes the technical architecture and functionality of a web-based decision support system (DSS), which not only provides advanced support for visualizing complex TBI data but also predicts a possible outcome by using a state-of-the-art Disease State Index machine-learning algorithm. The DSS is developed by using a three-layered architecture and by employing modern programming principles, software design patterns, and using robust technologies (C#, ASP.NET MVC, HTML5, JavaScript, Entity Framework, etc.). The DSS is comprised of a patient overview module, a disease-state prediction module, and an imaging module. After deploying it on a web-server, the DSS was made available to two hospitals in U.K. and Finland. Afterwards, we conducted a validation study to evaluate its usability in clinical settings. Initial results of the study indicate that especially less experience clinicians may benefit from this type of decision support software tool

A Decision Support System for Diagnostics and Treatment Planning in Traumatic Brain Injury

Traumatic brain injury (TBI) occurs when an external force causes functional or structural alterations in the brain. Clinical characteristics of TBI vary greatly from patient to patient, and a large amount of data is gathered during various phases of clinical care in these patients. It is hard for clinicians to efficiently integrate and interpret all of these data and plan interventions in a timely manner. This paper describes the technical architecture and functionality of a web-based decision support system (DSS), which not only provides advanced support for visualizing complex TBI data but also predicts a possible outcome by using a state-of-the-art Disease State Index machine-learning algorithm. The DSS is developed by using a three-layered architecture and by employing modern programming principles, software design patterns, and using robust technologies (C#, ASP.NET MVC, HTML5, JavaScript, Entity Framework, etc.). The DSS is comprised of a patient overview module, a disease-state prediction module, and an imaging module. After deploying it on a web-server, the DSS was made available to two hospitals in U.K. and Finland. Afterwards, we conducted a validation study to evaluate its usability in clinical settings. Initial results of the study indicate that especially less experience clinicians may benefit from this type of decision support software tool

Methode zur Gestaltung sicherer präskriptiver Systeme für die Therapieunterstützung

Die zunehmende Geschwindigkeit der Digitalisierung im Gesundheitsbereich bietet diesem neue Möglichkeiten, stellt die Branche aber auch vor neue Herausforderungen. Mithilfe von präskriptiven Systemen können Langzeittherapien unterstützt werden, um deren Therapietreue zu verbessern. Hierfür müssen jedoch bei der Konzeption und Entwicklung solcher Systeme verschiedenste Faktoren, wie die Therapietreue und die Algorithmensicherheit, mit einbezogen werden, um einen gesamtheitlichen Mehrwert schaffen zu können. Die Arbeit stellt eine Methode zur Konzeption und Entwicklung therapieunterstützender präskriptiver Systeme vor, wobei sowohl die Therapietreue als auch die Sicherheit der Entscheidungen im Vordergrund steht