Data Analytics in Clinical Trials: A Machine Learning Approach
Clinical trail analytics uses Machine Learning Algorithms to gain new insight into clinical trial data.
The clinical trails play a critical role in drug development, product testing and patient health.
The Role of Machine Learning in Clinical Research:
Interest in Machine Learning for healthcare has increased rapidly over last ten years Machine Learning has the potential to help, improve the success, generalizability, patient-centredness and efficiency of clinical trials.
Various ML approaches are available for managing large and heterogenous sources of data.
Has a result ML has value to add across the spectrum of clinical trials, from preclinical drug discovery to pretrial planning through study execution to data management and analysis.
The Role of ML in Preclinical Drug Discovery and Development Research:
Successful clinical trials require significant preclinical investigation and planning, during which promising candidate molecules and targets are identified and the investigation strategy to achieve regulatory approval is defined.
ML can help researchers leverage previous and ongoing research to decrease the inefficiencies of the preclinical process.
Data Analysis:
Data collected in clinical trials, registries, and clinical practices are fertile sources for hypothesis generation, risk modelling,
ML can potentially improve the ubiquitous practice of secondary trial analysis by more powerfully identifying treatment heterogeneity while still providing some protection against false-positive discoveries.
Data Collection and Management:
The use of ML in clinical trials can change the data collections, management and analysis techniques required.
However, ML methods can help address some of the difficulties associated with missing data and collecting real-world data.
Conclusion:
ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.
Student Name: Y. Sri Nithya
Student ID: CLS_211/102023
Qualification: M.sc (Biochemistry)
e-Mail ID: yanamalasrinithya99@gmail.com
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