References of "Kaderali, Lars"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailEvaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.
Bergquist, Timothy; Schaffter, Thomas; Yan, Yao et al

in Journal of the American Medical Informatics Association : JAMIA (2023)

OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on ... [more ▼]

OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI. [less ▲]

Detailed reference viewed: 224 (0 UL)
Full Text
Peer Reviewed
See detailFrom experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV-1 replication
Boerner, Kathleen; Hermle, Johannes; Sommer, Christoph et al

in Biotechnology Journal (2010), 5(1), 39-49

RNA interference (RNAi) has emerged as a powerful technique for studying loss-of-function phenotypes by specific down-regulation of gene expression, allowing the investigation of virus-host interactions ... [more ▼]

RNA interference (RNAi) has emerged as a powerful technique for studying loss-of-function phenotypes by specific down-regulation of gene expression, allowing the investigation of virus-host interactions by large-scale high-throughput RNAi screens. Here we present a robust and sensitive small interfering RNA screening platform consisting of an experimental setup, single-cell image and statistical analysis as well as bioinformatics. The workflow has been established to elucidate host gene functions exploited by viruses, monitoring both suppression and enhancement of viral replication simultaneously by fluorescence microscopy. The platform comprises a two-stage procedure in which potential host factors are first identified in a primary screen and afterwards re-tested in a validation screen to confirm true positive hits. Subsequent bioinformatics allows the identification of cellular genes participating in metabolic pathways and cellular networks utilised by viruses for efficient infection. Our workflow has been used to investigate host factor usage by the human immunodeficiency virus-1 (HIV-1), but can also be adapted to other viruses. Importantly, we expect that the description of the platform will guide further screening approaches for virus-host interactions. The ViroQuant-Cell Networks RNAi Screening core facility is an integral part of the recently founded BioQuant centre for systems biology at the University of Heidelberg and will provide service to external users in the near future. [less ▲]

Detailed reference viewed: 240 (0 UL)