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#008 Machine Learning Modeling in Neuroscience Clinical Trials Design
#datascience #dataanalysis #technology #machinelearning #clinicaltrials #placebo
In this episode, Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, shares insights from the PHUSE US Connect conference and her work on applying machine learning to neuroscience clinical trials. She discusses challenges like high placebo response and attrition, the value of interdisciplinary collaboration, and how AI/ML can shape trial design, improve regulatory readiness, and move the field toward more objective, data-driven outcomes.
In this episode, you will learn:
Jing Dai https://www.linkedin.com/in/jingdai1009/Â
__________________________________________
More about Appsilon: â–º https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert
â–º https://www.appsilon.com/contact-usÂ
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog: â–º http://appsilon.com/blogÂ
LinkedIn: https://www.linkedin.com/company/appsilon/Â
In this episode, Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, shares insights from the PHUSE US Connect conference and her work on applying machine learning to neuroscience clinical trials. She discusses challenges like high placebo response and attrition, the value of interdisciplinary collaboration, and how AI/ML can shape trial design, improve regulatory readiness, and move the field toward more objective, data-driven outcomes.
In this episode, you will learn:
- How machine learning can help address high placebo response and attrition in neuroscience clinical trials.
- Why traditional statistical models struggle with high-dimensional clinical data.
- Key regulatory frameworks (GxP, GMLP) for ensuring AI/ML models meet compliance standards in drug development.
- Practical tips for fostering interdisciplinary collaboration between biostatisticians, clinicians, and data scientists.
Jing Dai https://www.linkedin.com/in/jingdai1009/Â
__________________________________________
More about Appsilon: â–º https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert
â–º https://www.appsilon.com/contact-usÂ
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog: â–º http://appsilon.com/blogÂ
LinkedIn: https://www.linkedin.com/company/appsilon/Â
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By Appsilon#datascience #dataanalysis #technology #machinelearning #clinicaltrials #placebo
In this episode, Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, shares insights from the PHUSE US Connect conference and her work on applying machine learning to neuroscience clinical trials. She discusses challenges like high placebo response and attrition, the value of interdisciplinary collaboration, and how AI/ML can shape trial design, improve regulatory readiness, and move the field toward more objective, data-driven outcomes.
In this episode, you will learn:
Jing Dai https://www.linkedin.com/in/jingdai1009/Â
__________________________________________
More about Appsilon: â–º https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert
â–º https://www.appsilon.com/contact-usÂ
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog: â–º http://appsilon.com/blogÂ
LinkedIn: https://www.linkedin.com/company/appsilon/Â
In this episode, Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, shares insights from the PHUSE US Connect conference and her work on applying machine learning to neuroscience clinical trials. She discusses challenges like high placebo response and attrition, the value of interdisciplinary collaboration, and how AI/ML can shape trial design, improve regulatory readiness, and move the field toward more objective, data-driven outcomes.
In this episode, you will learn:
- How machine learning can help address high placebo response and attrition in neuroscience clinical trials.
- Why traditional statistical models struggle with high-dimensional clinical data.
- Key regulatory frameworks (GxP, GMLP) for ensuring AI/ML models meet compliance standards in drug development.
- Practical tips for fostering interdisciplinary collaboration between biostatisticians, clinicians, and data scientists.
Jing Dai https://www.linkedin.com/in/jingdai1009/Â
__________________________________________
More about Appsilon: â–º https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert
â–º https://www.appsilon.com/contact-usÂ
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog: â–º http://appsilon.com/blogÂ
LinkedIn: https://www.linkedin.com/company/appsilon/Â