Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology

Area of Science:

• Oncology
• Medical Informatics
• Data Science

Background:

• Big data and predictive analytics offer significant potential for improving risk stratification in oncology.
• Data-rich fields like oncology are well-positioned to leverage these advanced computational techniques.

Purpose of the Study:

• To review existing literature on the use cases and challenges of predictive analytics in oncology risk stratification.
• To identify current applications and future directions for big data in cancer care.

Main Methods:

• Literature review of published studies on predictive analytics in oncology.
• Categorization of evidence-based use cases into population health management, radiomics, and pathology.
• Identification of future use cases in clinical decision support and genomic risk stratification.

Main Results:

• Identified three key areas for current predictive analytics use cases: population health management, radiomics, and pathology.
• Highlighted promising future applications in clinical decision support and genomic risk stratification.
• Described significant challenges including data acquisition, lack of prospective validation, and risk of automating bias.

Conclusions:

• Computational techniques hold great promise for improving clinical risk stratification in cancer patients.
• Addressing challenges in data comprehensiveness, validation, and bias is crucial for realizing the full potential of big data in oncology.
• Successful implementation of these methods can lead to enhanced patient care and outcomes.