To which idea of truth may medicine refer? Evidence-based medicine (EBM) is rooted in the scientific truth. To explain the meaning and to trace the evolution of scientific truth, this article outlines the history of the Scientific Revolution and of the parable of Modernity, up to the arrival of pragmatism and hermeneutics. Here, the concept of truth becomes somehow discomfiting and the momentum leans towards the integration of different points of view. The fuzzy set theory for the definition of disease, as well as the shift from disease to syndrome (which has operational relevance for geriatrics), seems to refer to a more complex perspective on knowledge, albeit one that is less defined as compared to the nosology in use. Supporters of narrative medicine seek the truth in the interpretation of the patients' stories, and take advantage of the medical humanities to find the truth in words, feelings and contact with the patients. Hence, it is possible to mention the parresia, which is the frank communication espoused by stoicism and epicureanism, a technical and ethical quality which allows one to care in the proper way, a true discourse for one's own moral stance. Meanwhile, EBM and narrative medicine are converging towards a point at which medicine is considered a practical knowledge. It is the perspective of complexity that as a zeitgeist explains these multiple instances and proposes multiplicity and uncertainty as key referents for the truth and the practice of medicine.
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Sci Data
January 2025
Faculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham, B4 7XG, UK.
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland.
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Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China; National Center for Stomatology, Beijing, China; National Clinical Research Center for Oral Diseases, Beijing, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China. Electronic address:
With developments in computer science and technology, great progress has been made in three-dimensional (3D) ultrasound. Recently, ultrasound-based 3D bone modelling has attracted much attention, and its accuracy has been studied for the femur, tibia, and spine. The use of ultrasound allows data for bone surface to be acquired non-invasively and without radiation.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-ku, Tokyo 135-8181, Japan. Electronic address:
This paper outlines key machine learning principles, focusing on the use of XGBoost and SHAP values to assist researchers in avoiding analytical pitfalls. XGBoost builds models by incrementally adding decision trees, each addressing the errors of the previous one, which can result in inflated feature importance scores due to the method's emphasis on misclassified examples. While SHAP values provide a theoretically robust way to interpret predictions, their dependence on model structure and feature interactions can introduce biases.
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Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, 0372, Norway.
Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient.
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January 2025
Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, United States.
Background: Effective management of dual antiplatelet therapy (DAPT) following drug-eluting stent (DES) implantation is crucial for preventing adverse events. Traditional prognostic tools, such as rule-based methods or Cox regression, despite their widespread use and ease, tend to yield moderate predictive accuracy within predetermined timeframes. This study introduces a new contrastive learning-based approach to enhance prediction efficacy over multiple time intervals.
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