Summary of Advancing Clinical Trial Outcomes Using Deep Learning and Predictive Modelling: Bridging Precision Medicine and Patient-centered Care, by Sydney Anuyah et al.
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care
by Sydney Anuyah, Mallika K Singh, Hope Nyavor
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of deep learning techniques, such as convolutional neural networks (CNNs) and transformer-based models, to optimize clinical trial design, patient recruitment, and real-time monitoring. It also employs predictive modelling approaches, including survival analysis and time-series forecasting, to predict trial outcomes and reduce failure rates. The study utilizes natural language processing (NLP) techniques for extracting actionable insights from unstructured clinical data, such as patient notes and trial protocols. A custom dataset is curated for training and validating these models, with key metrics like precision, recall, and F1 scores used to evaluate model performance. The research highlights the potential of AI-driven methods to streamline clinical trial workflows, improve patient-centric outcomes, and reduce costs associated with trial inefficiencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how artificial intelligence (AI) can help make clinical trials better and more efficient. It uses special kinds of computer programs called deep learning models to sort patients, predict bad events, and create personalized treatment plans. The study also uses other AI tools like survival analysis and time-series forecasting to guess what will happen in a trial. This helps reduce the number of failed trials and makes it easier to get good results. The researchers used special techniques to make sense of lots of unstructured data, like patient notes and trial information. They created their own dataset for training and testing these models, and looked at things like how accurate they were. Overall, the study shows that AI can really help clinical trials by making them more efficient, personalized, and cost-effective. |
Keywords
» Artificial intelligence » Deep learning » Natural language processing » Nlp » Precision » Recall » Time series » Transformer
