Exciting updates with Aiomic
Aiomic is one of 128 startups selected from a pool of 1,300 applications for the program.
We received the incredible news that we have been awarded a significant Innobooster grant to support the process of bringing our first product to market in the US and piloting it with some of the top US hospitals.
Aiomic has been featured in none other than Science Magazine! It's not every day that our journey and commitment to leveraging AI for improving postoperative care gets showcased on such a revered platform.
Aiomic is Texas-bound! We're thrilled to share that we have been selected to participate in a 6-month accelerator program at the renowned Texas Medical Center (TMC) in Houston, USA...
We're thrilled to announce that Aiomic has been featured in BioWorld MedTech for securing €1.3 million in funding from Denmark's Bioinnovation Institute (BII).
We are excited to announce that we have secured 10 million Danish Kroner (DKK) in funding through convertible loans to further develop and commercialize our platform, Aiomic360.
Our peer-reviewed publications
Postoperative complication rates are often assessed through administrative data, although this method has proven to be imprecise. Recently, new developments in natural language processing have shown promise in detecting specific phenotypes from free medical text. In this artcle we show that natural language processing can capture postoperative complications on a par with human-level curation from electronic health record free medical text.
High-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs).
The risks of post trauma complications are regulated by the injury, comorbidities, and the clinical trajectories, yet prediction models are often limited to single time-point data. We hypothesize that deep learning prediction models can be used for risk prediction using additive data after trauma using a sliding windows approach.
Postoperative complications continue to constitute a major issue for both the healthcare system and the individual patient and are associated with inferior outcomes and higher healthcare costs. The objective of this study was to evaluate the trends of postoperative complication rates over a 7-year period.
Early detection of postoperative complications, including organ failure, is pivotal in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an era of increasing health-care costs and limited financial resources, identifying surgical patients at a high risk of postoperative complications and providing personalised precision medicine-based treatment strategies provides an obvious pathway for reducing patient morbidity and mortality…
Trauma is associated with a significant risk of post-trauma complications (PTCs). These include thromboembolic events, strokes, infections, and failure of organ systems (eg, kidney failure). Although care of the trauma patient has evolved during the last decade, whether this has resulted in a reduction in specific PTCs is unknown. We hypothesize that the incidence of PTCs has been decreasing during a 10-year period from 2007 to 2017.
