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AI and ML for the Data Quality of Clinical Trial Studies /
Presented by Hemalatha B Raju - Lead Data Scientist at Biorasi Running a clinical trial is always very complex. But with the implementation of machine learning we can significantly improve the efficiency of clinical trial development. Machine learning logic and algorithms can help us advance the pat...
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| Formato: | Video |
| Idioma: | Inglés |
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[Erscheinungsort nicht ermittelbar] :
Data Science Salon,
2019
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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| 520 | |a Presented by Hemalatha B Raju - Lead Data Scientist at Biorasi Running a clinical trial is always very complex. But with the implementation of machine learning we can significantly improve the efficiency of clinical trial development. Machine learning logic and algorithms can help us advance the patient selection process for clinical trials, improve the data quality, reduce the time and cost in the execution of clinical trials. for accurate predictions of outcomes using pattern recognition. Machine learning algorithms could be potentially applied to develop comprehensive risk-based monitoring tools, fraud detection pipeline, on-study analytics models and solutions for various clinical trials that alerts the clinical monitors and the pharmaceutical companies about the quality of clinical sites . Clinical data can also be reviewed at aggregate level regularly throughout assigned studies using analytical reporting tools to support the identification of risks and data trends. The rules-based logic could be used throughout the clinical trial studies to analyze the primary and secondary endpoints, required for the safety and efficacy analysis of the investigational drug and thus improve the efficiency of clinical trial studies. | ||
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