Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization

Shanmugam, Divya; Dı́az, Fernando; Shabanian, Samira; Finck, Michèle; Biega, Asia J.

doi:10.1145/3531146.3533148

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…However, to abide to the data minimisation principle, a new trend has emerged in AI and ML, namely the artificialdevised data minimisation. 49,50 We observed, however, that there is not always the need to minimise data artificially, since many studies (especially in medicine) are focused and restricted to limited cohorts but produce indeed valuable data. Normally, this data is deemed to be too small to be used with ML.…”

Section: Discussionmentioning

confidence: 99%

Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Jacobson¹,

Lind²,

Persson³

2023

COPD

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Introduction In this article, we explore to what extent it is possible to leverage on very small data to build machine learning (ML) models that predict acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Methods We build ML models using the small data collected during the eHealth Diary telemonitoring study between 2013 and 2017 in Sweden. This data refers to a group of multimorbid patients, namely 18 patients with chronic obstructive pulmonary disease (COPD) as the major reason behind previous hospitalisations. The telemonitoring was supervised by a specialised hospital-based home care (HBHC) unit, which also was responsible for the medical actions needed. Results We implement two different ML approaches, one based on time-dependent covariates and the other one based on time-independent covariates. We compare the first approach with standard COX Proportional Hazards (CPH). For the second one, we use different proportions of synthetic data to build models and then evaluate the best model against authentic data. Discussion To the best of our knowledge, the present ML study shows for the first time that the most important variable for an increased risk of future AECOPDs is “maintenance medication changes by HBHC”. This finding is clinically relevant since a sub-optimal maintenance treatment, requiring medication changes, puts the patient in risk for future AECOPDs. Conclusion The experiments return useful insights about the use of small data for ML.

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Section: Discussionmentioning

confidence: 99%