Avoiding big data pitfalls.

Area of Science:

• Cardiology
• Computational Biology
• Data Science

Background:

• Clinical decisions integrate experience-based statistical models and physiology-based mechanistic models.
• Computers excel at inductive data pattern discovery but rarely combine it with deductive reasoning.
• Current trends emphasize big data and inductive methods, leading to potential pitfalls.

Purpose of the Study:

• To review the risks and limitations of purely inductive computational approaches in medicine.
• To recommend strategies for mitigating common flaws in data-driven predictions.
• To highlight the potential of integrating mechanistic and statistical models for precision cardiology.

Main Methods:

• Review of risks associated with inductive computational methods.
• Discussion of biases (selection, confounding), overfitting, and spurious correlations.
• Exploration of causality, statistical technique selection, and research transparency.

Main Results:

• Purely inductive computational approaches carry significant risks, including lack of generality and spurious correlations.
• Overfitting and various biases can lead to unreliable predictions.
• Integrating mechanistic and statistical models addresses these limitations.

Conclusions:

• A purely inductive approach in computational cardiology is prone to errors and biases.
• Recommendations include causal examination of data, careful statistical methods, and transparency.
• The synergy of mechanistic and statistical models (digital twins) promises a future for precision cardiology.