%0 Journal Article %1 noauthororeditor %A Kannareddy, Dhanalakshmi D | Vijaya Lakshmi %D 2024 %J INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT %K Case Electronic Forms, Report adaptability, anomaly clinical collection collection, data design, detection, eCRF learning, machine quality, real-time streamlined trials, validation, %N 1 %P 830-834 %T Streamlining Data Collection eCRF Design and Machine Learning %U https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d %V 8 %X Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d

@article{noauthororeditor, abstract = {Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d }, added-at = {2024-03-08T13:04:41.000+0100}, author = {Kannareddy, Dhanalakshmi D | Vijaya Lakshmi}, biburl = {https://www.bibsonomy.org/bibtex/2372ad196591385b4fda6c64f40c555c4/ijtsrd}, interhash = {64d753f905330675733edb5dd885bbc9}, intrahash = {372ad196591385b4fda6c64f40c555c4}, issn = {2456-6470}, journal = {INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT}, keywords = {Case Electronic Forms, Report adaptability, anomaly clinical collection collection, data design, detection, eCRF learning, machine quality, real-time streamlined trials, validation,}, language = {english}, month = feb, number = 1, pages = {830-834}, timestamp = {2024-03-08T13:04:41.000+0100}, title = {Streamlining Data Collection eCRF Design and Machine Learning }, url = {https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d}, volume = 8, year = 2024 }