arXiv subjects

Anuj J. Kapadia

Publications and source records attributed to Anuj J. Kapadia.

3 recordsLinked to original sources

arXiv2025

Investigating the importance of county-level characteristics in opioid-related mortality across the United States

The opioid crisis remains a critical public health challenge in the United States. Despite national efforts which reduced opioid prescribing rates by nearly 45\% between 2011 and 2021, opioid-related overdose deaths more than tripled during the same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although much attention has been given to supply-side factors fueling this transition, the underlying structural conditions that perpetuate and exacerbate opioid misuse remain less understood. Moreover, the COVID-19 pandemic intensified the opioid crisis through widespread social isolation and record-high unemployment; consequently, understanding the underlying drivers of this epidemic has become even more crucial in recent years. To address this need, our study examines the correlation between opioid-related mortality and thirteen county-level characteristics related to population traits, economic stability, and infrastructure. Leveraging a nationwide county-level dataset spanning consecutive years from 2010 to 2022, this study integrates empirical insights from exploratory data analysis with feature importance metrics derived from machine learning models. Our findings highlight critical regional characteristics strongly correlated with opioid-related mortality, emphasizing their potential roles in worsening the epidemic when their levels are high and mitigating it when their levels are low.

cs.CY↗

arXiv2024

A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatric mental health screening. Our objectives are: (1) to assess the presence of bias by evaluating outcome parity across sex subgroups, (2) to identify bias sources through textual distribution analysis, and (3) to develop a de-biasing method for mental health text data. Methods: We examined classification parity across demographic groups and assessed how gendered language influences model predictions. A data-centric de-biasing method was applied, focusing on neutralizing biased terms while retaining salient clinical information. This methodology was tested on a model for automatic anxiety detection in pediatric patients. Results: Our findings revealed a systematic under-diagnosis of female adolescent patients, with a 4% lower accuracy and a 9% higher False Negative Rate (FNR) compared to male patients, likely due to disparities in information density and linguistic differences in patient notes. Notes for male patients were on average 500 words longer, and linguistic similarity metrics indicated distinct word distributions between genders. Implementing our de-biasing approach reduced diagnostic bias by up to 27%, demonstrating its effectiveness in enhancing equity across demographic groups. Discussion: We developed a data-centric de-biasing framework to address gender-based content disparities within clinical text. By neutralizing biased language and enhancing focus on clinically essential information, our approach demonstrates an effective strategy for mitigating bias in AI healthcare models trained on text.

cs.CL↗

arXiv2022

F*** workflows: when parts of FAIR are missing

The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can be applied to the data being handled by a scientific workflow as well as the processes, software, and other infrastructure which are necessary to specify and execute a workflow. The FAIR principles were designed as guidelines, rather than rules, that would allow for differences in standards for different communities and for different degrees of compliance. There are many practical considerations which impact the level of FAIR-ness that can actually be achieved, including policies, traditions, and technologies. Because of these considerations, obstacles are often encountered during the workflow lifecycle that trace directly to shortcomings in the implementation of the FAIR principles. Here, we detail some cases, without naming names, in which data and workflows were Findable but otherwise lacking in areas commonly needed and expected by modern FAIR methods, tools, and users. We describe how some of these problems, all of which were overcome successfully, have motivated us to push on systems and approaches for fully FAIR workflows.

cs.DL↗