Master of Science – Biomedical Engineering, Thesis

Imputation of Missing Tabular Data Using Implicit Neural Representations (INRs) for Predictive Modelling.

Master of Science – Biomedical Engineering, Thesis Proposal

Imputation of Missing Tabular Data Using Implicit Neural Representations (INRs) for Predictive Modelling.

Real-world medical datasets frequently contain missing values due

to irregular patient follow-ups, incomplete records, or data entry

errors.

Accurate imputation of missing values is critical for creating reliable

predictions for various tasks. Traditional imputation methods like

mean or median substitution can introduce biases, while more

advanced techniques like KNN imputation or regression may not

fully capture the underlying patterns in the downstream task.

The aim of this thesis is to investigate the use of Implicit Neural

Representations (INRs) for imputing missing data in medical

records and to evaluate the impact of these imputations on

predictive models for tabular datasets. INRs, which represent data

as continuous functions rather than discrete values, offer a novel

approach to imputing missing values by learning smooth and dataaware representations.

The project will compare INR-based imputation to traditional

methods (e.g., mean imputation, KNN, or iterative imputation) and

assess the downstream effect of various tasks.

Nature of the Thesis

Programming: 80%, Documentation: 20%

Specific Requirements

• Experience in machine learning

• Good programming skills (especially Python)

Contact: anas.taha@unibas.ch

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