Deep learning-based risk prediction for interventional clinical trials based on protocol design: A retrospective study

Ferdowsi, Sohrab; Knafou, Julien; Borissov, Nikolay; Alvarez, David Vicente; Mishra, Rahul; Amini, Poorya; Teodoro, Douglas

doi:10.1016/j.patter.2023.100689

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…Due to the substantial imbalance between normal and abnormal datasets in this study, both the FPRs and FNRs were significantly influenced by the chosen threshold. Consequently, this study employed the area under the receiver operating characteristic curve (AUROC) [36,37] as it is an evaluative measure that is unaffected by threshold values.…”

Section: Assessment Metricsmentioning

confidence: 99%

Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading

Wu,

Hu,

Liu

et al. 2024

Sensors

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Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting.

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Section: Assessment Metricsmentioning

confidence: 99%

Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading

Wu,

Hu,

Liu

et al. 2024

Sensors

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“…Deep learning approaches like Convolutional Neural Networks (CNN) (Lecun et al, 1998;Teodoro et al, 2020), Recurrent Neural Networks (RNN) (Rumelhart et al, 1986), Long Short-Term Memory Networks (LSTM) (Hochreiter and Schmidhuber, 1997), and Transformer-based architectures (Vaswani et al, 2017), including pretrained language models such as BERT (Devlin et al, 2018), RoBERTa (Liu et al, 2019), and XL-Net (Yang et al, 2019), have demonstrated stateof-the-art efficacy in a diverse range of domains . Leveraging the hierarchical structure of documents, graph neural networks (GNNs) have also been effectively proposed to assign categories to biomedical documents (Ferdowsi et al, 2023(Ferdowsi et al, , 2022(Ferdowsi et al, , 2021. Compared to deep learning models, SVM requires lower computational resources and training time and is a more efficient choice for certain applications (Sakr et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

DS4DH at MEDIQA-Chat 2023: Leveraging SVM and GPT-3 Prompt Engineering for Medical Dialogue Classification and Summarization

Zhang

Mishra

Teodoro

2023

Preprint

Self Cite

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This paper presents the results of the Data Science for Digital Health (DS4DH) group in the MEDIQA-Chat Tasks at ACL-ClinicalNLP 2023. Our study combines the power of a classical machine learning method, Support Vector Machine, for classifying medical dialogues, along with the implementation of one-shot prompts using GPT-3.5. We employ dialogues and summaries from the same category as prompts to generate summaries for novel dialogues. Our findings exceed the average benchmark score, offering a robust reference for assessing performance in this field.

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Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data

Proios,

Yazdani,

Bornet

et al. 2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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Deep learning-based risk prediction for interventional clinical trials based on protocol design: A retrospective study

Cited by 4 publications

References 39 publications

Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading

Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading

DS4DH at MEDIQA-Chat 2023: Leveraging SVM and GPT-3 Prompt Engineering for Medical Dialogue Classification and Summarization

Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data

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