Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development
AV Ponce‐Bobadilla, V Schmitt… - Clinical and …, 2024 - Wiley Online Library
Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML)
models for drug development, effectively interpreting their predictions remains a challenge …
models for drug development, effectively interpreting their predictions remains a challenge …
Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
[HTML][HTML] AI-driven 3D bioprinting for regenerative medicine: from bench to bedside
In recent decades, 3D bioprinting has garnered significant research attention due to its
ability to manipulate biomaterials and cells to create complex structures precisely. However …
ability to manipulate biomaterials and cells to create complex structures precisely. However …
Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis
X Lin, J Zhu, J Shen, Y Zhang, J Zhu - Biosensors and Bioelectronics, 2024 - Elsevier
Exosomes, as next-generation biomarkers, has great potential in tracking cancer
progression. They face many detection limitations in cancer diagnosis. Plasmonic …
progression. They face many detection limitations in cancer diagnosis. Plasmonic …
Eye tracking insights into physician behaviour with safe and unsafe explainable AI recommendations
We studied clinical AI-supported decision-making as an example of a high-stakes setting in
which explainable AI (XAI) has been proposed as useful (by theoretically providing …
which explainable AI (XAI) has been proposed as useful (by theoretically providing …
[HTML][HTML] Validation Requirements for AI-based Intervention-Evaluation in Aging and Longevity Research and Practice
The field of aging and longevity research is overwhelmed by vast amounts of data, calling for
the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the …
the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the …
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks
associated with their use has become one of the most urgent scientific and societal issues …
associated with their use has become one of the most urgent scientific and societal issues …
Enhancing psychiatric rehabilitation outcomes through a multimodal multitask learning model based on BERT and TabNet: An approach for personalized treatment …
H Yang, D Zhu, S He, Z Xu, Z Liu, W Zhang, J Cai - Psychiatry Research, 2024 - Elsevier
Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI)
necessitates a comprehensive analysis of multimodal data, including unstructured text …
necessitates a comprehensive analysis of multimodal data, including unstructured text …
Automated ensemble multimodal machine learning for healthcare
The application of machine learning in medicine and healthcare has led to the creation of
numerous diagnostic and prognostic models. However, despite their success, current …
numerous diagnostic and prognostic models. However, despite their success, current …
Machine learning with requirements: a manifesto
In the recent years, machine learning has made great advancements that have been at the
root of many breakthroughs in different application domains. However, it is still an open …
root of many breakthroughs in different application domains. However, it is still an open …