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Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning.
Nat Comput Sci
December 2024
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Machine learning plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules; however, most existing machine learning models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work we developed a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with several widely used hybrid and double-hybrid functionals in terms of both computational cost and prediction accuracy of various quantum chemical properties.
View Article and Find Full Text PDFA Transfer Learning Radiomics Nomogram to Predict the Postoperative Recurrence of Advanced Gastric Cancer.
J Gastroenterol Hepatol
December 2024
Department of Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Background And Aim: In this study, a transfer learning (TL) algorithm was used to predict postoperative recurrence of advanced gastric cancer (AGC) and to evaluate its value in a small-sample clinical study.
Methods: A total of 431 cases of AGC from three centers were included in this retrospective study. First, TL signatures (TLSs) were constructed based on different source domains, including whole slide images (TLS-WSIs) and natural images (TLS-ImageNet).
Construction of a prediction model for Alzheimer's disease using an AI-driven eye-tracking task on mobile devices.
Aging Clin Exp Res
December 2024
Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yi Shan Road, Shanghai, 200233, China.
Background: Eye-movement can reflect cognition and provide information on the neurodegeneration, such as Alzheimer's disease (AD). The high cost and limited accessibility of eye-movement recordings have hindered their use in clinics.
Aims: We aim to develop an AI-driven eye-tracking tool for assessing AD using mobile devices with embedded cameras.
Specialist training in infectious diseases in Europe.
Clin Microbiol Infect
December 2024
Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Objectives: The objectives were to determine the structure of training programmes and assessment of physicians training to become infectious disease (ID) specialists in Europe in early 2024, and to document the provision of specialists, trainees and training centres in each country.
Methods: Delegates to the ID Section and Board of the European Union of Medical Specialists (UEMS) entered national data on a web-based survey tool in late 2023-early 2024. Results were compared to UEMS recommendations on the structure and content of postgraduate training in ID in Europe (2018), and to results of a similar survey in early 2021.
Optimizing Catheter Verification: An Understandable AI Model for Efficient Assessment of Central Venous Catheter Placement in Chest Radiography.
Invest Radiol
October 2024
From the Department of Radiology and Nuclear Medicine, UKSH Lübeck, Lübeck, Germany (J.S., M.M., L.B., Y.E., J.B., M.M.S.); Institute of Medical Informatics, University of Lübeck, Lübeck, Germany (L.H., M.P.H.); Philips Research Hamburg, Hamburg, Germany (A.S., H.S.); and Institute of Interventional Radiology, UKSH Lübeck, Lübeck, Germany (M.M.S.).
Purpose: Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection.
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