Results 41 to 50 of about 244,877 (310)

SISC: End-to-End Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells

IEEE Access, 2019
Lung cancer is the leading cause of cancer-related death worldwide. Computer-aided diagnosis (CAD) systems have shown significant promise in recent years for facilitating the effective detection and classification of abnormal lung nodules in computed ...
Devinder Kumar, Vignesh Sankar, David Clausi, Graham W. Taylor, Alexander Wong   +4 more
doaj   +1 more source

Early Body Mass Index z‐Score Change and Resolution of Severe Malnutrition in Children With Sickle Cell Anemia in a Low‐Income Setting: A Prospective Single‐Arm Extension Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Children with sickle cell anemia (SCA) in low‐income settings are at risk of severe malnutrition, but optimal nutritional management has not been established. We evaluated an intensified ready‐to‐use therapeutic food (RUTF) regimen in children with persistent severe malnutrition after initial treatment and assessed whether early ...
Safiya Gambo, Shehu Umar Abdullahi, Hassan Adam Murtala, Halima Kabir, Khadija A. Shamsu, Sari A. Acra, Virginia A. Stallings, Mark Rodeghier, Michael R. DeBaun, Lauren J. Klein   +9 more
wiley   +1 more source

MoEPH: an adaptive fusion-based LLM for predicting phage-host interactions in health informatics

Frontiers in Microbiology
Phage-host interaction prediction plays a crucial role in the development of phage therapy, particularly in combating antimicrobial resistance (AMR). Current in silico models often suffer from limited generalizability and low interpretability. To address
Qian Chen, Zihang Zhao, Min Li, Min Li, Wenchen Song, Minfeng Xiao, Min Fang   +6 more
doaj   +1 more source

Central Nervous System Tumors Among Infants in Canada: A Report From CYP‐C

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Central nervous system (CNS) tumors in infants are rare, pose unique clinical challenges, and lack large‐scale evidence‐based data to guide management. This study seeks to describe CNS tumors in Canadian infants and to compare their outcomes with those of older children.
Samuel Sassine, Hallie Coltin, Sylvia Cheng, Lucie Lafay‐Cousin, Vanan Issai Magimairajan, Craig Erker, Stéphanie Vairy, Lynette Bowes, Adam Fleming, Donna Johnston, Sarah McKillop, Roona Sinha, Julie Bennett, Christina Coleman, Laura Wheaton, Shayna Zelcer, Samuele Renzi, Thai Hoa Tran   +17 more
wiley   +1 more source

Development and validation of an interpretable machine learning-based predictive model for breast cancer bone metastasis

Frontiers in Oncology
BackgroundBreast cancer is one of the most common malignancies worldwide, with bone metastasis representing its most frequent distant metastatic form, significantly worsening patient prognosis.
Caiyun Fan, Ming Tian, Zhendong Ding, Zhendong Ding, Jun Peng, Jun Peng, Gulisitan Yiliyiming, Mingjiang Fan, Abuduaini Tuerxun, Binxu Qiu, Xiaojuan Zhu   +10 more
doaj   +1 more source

An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause

Space Weather, 2023
In this study, we propose an interpretable machine learning procedure to unravel the importance of multiple interplanetary parameters to the Earth's magnetopause standoff distance (MSD).
Sheng Li, Yang‐Yi Sun, Chieh‐Hung Chen   +2 more
doaj   +1 more source

Comparative Drug Response Profiling in Neuroblastoma Cell Lines and Patient‐Derived Tumor Organoids

Pediatric Blood &Cancer, EarlyView.
ABSTRACT High‐risk neuroblastoma remains a leading cause of pediatric cancer mortality, and improved preclinical models are needed to guide therapeutic developments. We screened seven high‐risk neuroblastoma cell lines and three patient‐derived tumor organoids with 528 compounds alongside bone marrow controls, and compared them with external datasets ...
Krzysztof Wierbiłowicz, Se Whee Sammy Park, Aironas Los, Ernest Liu, Nona Struyf, Tom Erkers, Sören Lehmann, Jan J. Molenaar, Jakob Stenman, Olli Kallioniemi, Päivi Östling, Brinton Seashore‐Ludlow, Kasper Karlsson   +12 more
wiley   +1 more source

Towards Explainable Anomaly Detection in Safety-critical Systems: Employing FRAM and SpecTRM in International Space Station Telemetry

International Journal of Prognostics and Health Management
Ensuring the reliability and safety of space missions necessitates advanced anomaly detection systems capable of not only identifying deviations but also providing clear, understandable insights into their causes.
Shota Iino, Hideki Nomoto, Takashi Fukui, Sayaka Ishizawa, Yohei Yagisawa, Takayuki Hirose, Yasutaka Michiura   +6 more
doaj   +1 more source

A layered computer-interpretable guideline model for easing the update of locally adapted clinical guidelines

Health Informatics Journal, 2020
Maintenance of computer-interpretable guidelines is complicated by evolving medical knowledge and by the requirement to customize content to local practice settings. We developed a framework to support knowledge engineers in customization and maintenance
Adi Fux, Pnina Soffer, Mor Peleg
doaj   +1 more source

Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning [PDF]

, 2022
Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. So far, evolutionary computation (EC), in particular in the form of genetic programming (GP), represents a key enabler for the ...
Peter A. N. Bosman, Tanja Alderliesten, Marco Virgolin, Eric Medvet   +3 more
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