2 research outputs found
Using Undersampling with Ensemble Learning to Identify Factors Contributing to Preterm Birth
In this paper, we propose Ensemble Learning models to identify factors
contributing to preterm birth. Our work leverages a rich dataset collected by a
NIEHS P42 Center that is trying to identify the dominant factors responsible
for the high rate of premature births in northern Puerto Rico. We investigate
analytical models addressing two major challenges present in the dataset: 1)
the significant amount of incomplete data in the dataset, and 2) class
imbalance in the dataset. First, we leverage and compare two types of missing
data imputation methods: 1) mean-based and 2) similarity-based, increasing the
completeness of this dataset. Second, we propose a feature selection and
evaluation model based on using undersampling with Ensemble Learning to address
class imbalance present in the dataset. We leverage and compare multiple
Ensemble Feature selection methods, including Complete Linear Aggregation
(CLA), Weighted Mean Aggregation (WMA), Feature Occurrence Frequency (OFA), and
Classification Accuracy Based Aggregation (CAA). To further address missing
data present in each feature, we propose two novel methods: 1) Missing Data
Rate and Accuracy Based Aggregation (MAA), and 2) Entropy and Accuracy Based
Aggregation (EAA). Both proposed models balance the degree of data variance
introduced by the missing data handling during the feature selection process
while maintaining model performance. Our results show a 42\% improvement in
sensitivity versus fallout over previous state-of-the-art methods
The Risk to Population Health Equity Posed by Automated Decision Systems: A Narrative Review
Artificial intelligence is already ubiquitous, and is increasingly being used
to autonomously make ever more consequential decisions. However, there has been
relatively little research into the consequences for equity of the use of
narrow AI and automated decision systems in medicine and public health. A
narrative review using a hermeneutic approach was undertaken to explore current
and future uses of AI in medicine and public health, issues that have emerged,
and longer-term implications for population health. Accounts in the literature
reveal a tremendous expectation on AI to transform medical and public health
practices, especially regarding precision medicine and precision public health.
Automated decisions being made about disease detection, diagnosis, treatment,
and health funding allocation have significant consequences for individual and
population health and wellbeing. Meanwhile, it is evident that issues of bias,
incontestability, and erosion of privacy have emerged in sensitive domains
where narrow AI and automated decision systems are in common use. As the use of
automated decision systems expands, it is probable that these same issues will
manifest widely in medicine and public health applications. Bias,
incontestability, and erosion of privacy are mechanisms by which existing
social, economic and health disparities are perpetuated and amplified. The
implication is that there is a significant risk that use of automated decision
systems in health will exacerbate existing population health inequities. The
industrial scale and rapidity with which automated decision systems can be
applied to whole populations heightens the risk to population health equity.
There is a need therefore to design and implement automated decision systems
with care, monitor their impact over time, and develop capacities to respond to
issues as they emerge.Comment: 22 pages (12 pages excluding references), 1 figur