Explore new publications in health care and life sciences written by both SAS authors and authors outside SAS who used SAS software to conduct their research. Our new website includes a selection of the some of the most prominent and timely research available, demonstrating the key role SAS plays in driving innovation in health care and life sciences through advanced analytics and AI.   Check out our new Scientific Publications page.   Here is a selection of the great research our internal colleagues have contributed to.   Kjølseth Møller, J., Sørensen, M., Hardahl, C. (2021), Plos One, 16(3). Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort Study   Soerensen, P.D., Christensen, H.,  Worsoe Laursen, S. G., Hardahl, C.,  Brandslund, I, and Skov Madsen, J. (2021), Clinical Medicine and Laboratory Medicine (CCLM). Using artificial intelligence in a primary care setting to identify patients at risk for cancer: a risk prediction model based on routine laboratory tests   Reis Pinheiro, C.A., Galati, M., Summerville, N., Lambrecht, M. (2021), Big Data Research, 25. Using Network Analysis and Machine Learning to Identify Virus Spread Trends in COVID-19   Janssen, B.V., Theijse, R., van Roessel, S., de Ruiter, R., Berkel, A., Huiskens, J., Busch, O.R., Wilmink J.W., Kazemier, G., Valkema, P., Farina, A., Verheij, J., de Boer, O.J., Besselink, M.G. (2021), Cancers (Basel), 13(20), p. 5089. Artificial Intelligence-Based Segmentation of Residual Tumor in Histopathology of Pancreatic Cancer after Neoadjuvant Treatment   ... View more

Published in Cancers (Basel), 13(20), p. 5089. Abstract: The use of neoadjuvant therapy (NAT) in patients with pancreatic ductal adenocarcinoma (PDAC) is increasing. Objective quantification of the histopathological response to NAT may be used to guide adjuvant treatment and compare the efficacy of neoadjuvant regimens. However, current tumor response scoring (TRS) systems suffer from interobserver variability, originating from subjective definitions, the sometimes challenging histology, and response heterogeneity throughout the tumor bed. This study investigates if artificial intelligence-based segmentation of residual tumor burden in histopathology of PDAC after NAT may offer a more objective and reproducible TRS solution.   Janssen, B.V., Theijse, R., van Roessel, S., de Ruiter, R., Berkel, A., Huiskens, J., Busch, O.R., Wilmink J.W., Kazemier, G., Valkema, P., Farina, A., Verheij, J., de Boer, O.J., Besselink, M.G. (2021), Cancers (Basel), 13(20), p. 5089. Artificial Intelligence-Based Segmentation of Residual Tumor in Histopathology of Pancreatic Cancer after Neoadjuvant Treatment   Read this publication and more online on ourScientific Publications page on sas.com. ... View more

Published in Big Data Research, 25. Abstract: The outbreak of Coronavirus Disease 2019 (COVID-19) has infected and killed millions of people globally, resulting in a pandemic with enormous global impact. This disease affects the respiratory system, and the viral agent that causes it, SARS-CoV-2, spreads through droplets of saliva, as well as through coughing and sneezing. The most effective approach to reduce viral spread and avoid a substantial collapse of the health system, in the absence of vaccines, is nonpharmaceutical interventions (NPI) such as enforcing social containment restrictions, monitoring overall population mobility, implementing widespread viral testing, and increasing hygiene measures. Our approach consists of combining network analytics with machine learning models by using a combination of anonymized health and telecommunications data to better understand the correlation between population movements and virus spread. This approach, called location network analysis (LNA), allows for accurate prediction of possible new outbreaks. It gives governments and health authorities a crucial tool that can help define more accurate public health metrics and can be used either to intensify social containment policies to avoid further spread or to ease them to reopen the economy.    Reis Pinheiro, C.A., Galati, M., Summerville, N., Lambrecht, M. (2021), Big Data Research, 25. Using Network Analysis and Machine Learning to Identify Virus Spread Trends in COVID-19   Read this publication and more online on our Scientific Publications page on sas.com. ... View more

Published in Clinical Medicine and Laboratory Medicine, 60(12), pp. 2005-2016. Objectives: To evaluate the ability of an artificial intelligence (AI) model to predict the risk of cancer in patients referred from primary care based on routine blood tests. Results obtained with the AI model are compared to results based on logistic regression (LR). Results: The AI model based on routine laboratory blood tests can provide an easy-to use risk score to predict cancer within 90 days. Results obtained with the AI model were comparable to results from the LR model. In the internal validation Cohort IB, the AI model provided slightly better results than the LR analysis both in terms of the area under the receiver operating characteristics curve (AUC) and PPV, sensitivity/specificity while in the “out of time” validation test Cohort II, the obtained results were comparable.     Soerensen, P.D., Christensen, H.,  Worsoe Laursen, S. G., Hardahl, C.,  Brandslund, I, and Skov Madsen, J. (2021), Clinical Medicine and Laboratory Medicine (CCLM), 60(12), pp. 2005-2016. Using artificial intelligence in a primary care setting to identify patients at risk for cancer: a risk prediction model based on routine laboratory tests   Read this publication and more online on our Scientific Publications page on sas.com. ... View more

Published in Plos One, 16(3) Abstract: Healthcare associated infections (HAI) are a major burden for the healthcare system and associated with prolonged hospital stay, increased morbidity, mortality and costs. Healthcare associated urinary tract infections (HA-UTI) accounts for about 20–30% of all HAI’s, and with the emergence of multi-resistant urinary tract pathogens, the total burden of HA-UTI will most likely increase. Article authors include SAS authors using SAS Software.    Kjølseth Møller, J., Sørensen, M., Hardahl, C. (2021), Plos One, 16(3). Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort Study   Read this publication and more online on our Scientific Publications page on sas.com. ... View more