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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

Sanyam P Shah¹, Tanuj S Shekhawat¹, Vi-Anh Hoang²

¹Arizona State University, Tempe, AZ.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 23, 2026

Summary

Formal analyses of a scoring system for contraception decision aids were conducted for transgender and gender-nonconforming (TGNC) individuals. The study developed a methodology to identify critical values impacting system recommendations.

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Area of Science:

Reproductive Health
Health Informatics
Decision Science

Background:

Transgender and gender-nonconforming (TGNC) individuals assigned female at birth (AFAB) require tailored contraception decision support.
Existing decision aids may not adequately address the specific needs of TGNC AFAB individuals.
Formal validation of decision support systems is crucial for clinical utility.

Purpose of the Study:

To conduct formal analyses of a scoring system for a contraception decision aid.
To support transgender and gender-nonconforming (TGNC) assigned female at birth (AFAB) individuals in their contraceptive choices.
To develop and validate a methodology for assessing decision factors and system recommendations.

Main Methods:

Development of a methodology framework for assessing decision factor weights.
Conducting univariate and multivariate sensitivity analyses.
Utilizing data visualization to identify critical values in weight assignment.

Main Results:

Successful identification of critical values in weight assignment that influence system recommendations.
Contributions to the development and validation of the decision aid's knowledge base.
Enabled provision of explainable recommendations and informed future research directions.

Conclusions:

The developed methodology is critical for validating decision support systems for TGNC AFAB individuals.
Sensitivity analyses enhance the reliability and explainability of contraceptive recommendations.
Further research is needed to explore advanced analyses and generalizability to other applications.