arXiv subjects

Maimuna Majumder

Publications and source records attributed to Maimuna Majumder.

3 recordsLinked to original sources

arXiv2025

Statistical vs. Deep Learning Models for Estimating Substance Overdose Excess Mortality in the US

Substance overdose mortality in the United States claimed over 80,000 lives in 2023, with the COVID-19 pandemic exacerbating existing trends through healthcare disruptions and behavioral changes. Estimating excess mortality, defined as deaths beyond expected levels based on pre-pandemic patterns, is essential for understanding pandemic impacts and informing intervention strategies. However, traditional statistical methods like SARIMA assume linearity, stationarity, and fixed seasonality, which may not hold under structural disruptions. We present a systematic comparison of SARIMA against three deep learning (DL) architectures (LSTM, Seq2Seq, and Transformer) for counterfactual mortality estimation using national CDC data (2015-2019 for training/validation, 2020-2023 for projection). We contribute empirical evidence that LSTM achieves superior point estimation (17.08% MAPE vs. 23.88% for SARIMA) and better-calibrated uncertainty (68.8% vs. 47.9% prediction interval coverage) when projecting under regime change. We also demonstrate that attention-based models (Seq2Seq, Transformer) underperform due to overfitting to historical means rather than capturing emergent trends. Ourreproducible pipeline incorporates conformal prediction intervals and convergence analysis across 60+ trials per configuration, and we provide an open-source framework deployable with 15 state health departments. Our findings establish that carefully validated DL models can provide more reliable counterfactual estimates than traditional methods for public health planning, while highlighting the need for calibration techniques when deploying neural forecasting in high-stakes domains.

cs.LG↗

arXiv2025

The AI Committee: A Multi-Agent Framework for Automated Validation and Remediation of Web-Sourced Data

Many research areas rely on data from the web to gain insights and test their methods. However, collecting comprehensive research datasets often demands manually reviewing many web pages to identify and record relevant data points, which is labor-intensive and susceptible to error. While the emergence of large language models (LLM)-powered web agents has begun to automate parts of this process, they often struggle to ensure the validity of the data they collect. Indeed, these agents exhibit several recurring failure modes - including hallucinating or omitting values, misinterpreting page semantics, and failing to detect invalid information - which are subtle and difficult to detect and correct manually. To address this, we introduce the AI Committee, a novel model-agnostic multi-agent system that automates the process of validating and remediating web-sourced datasets. Each agent is specialized in a distinct task in the data quality assurance pipeline, from source scrutiny and fact-checking to data remediation and integrity validation. The AI Committee leverages various LLM capabilities - including in-context learning for dataset adaptation, chain-of-thought reasoning for complex semantic validation, and a self-correction loop for data remediation - all without task-specific training. We demonstrate the effectiveness of our system by applying it to three real-world datasets, showing that it generalizes across LLMs and significantly outperforms baseline approaches, achieving data completeness up to 78.7% and precision up to 100%. We additionally conduct an ablation study demonstrating the contribution of each agent to the Committee's performance. This work is released as an open-source tool for the research community.

cs.MA↗

arXiv2024

Chimeric Forecasting: Blending Human Judgment and Computational Methods for Improved, Real-time Forecasts of Influenza Hospitalizations

Infectious disease forecasts can reduce mortality and morbidity by supporting evidence-based public health decision making. Most epidemic models train on surveillance and structured data (e.g. weather, mobility, media), missing contextual information about the epidemic. Human judgment forecasts are novel data, asking humans to generate forecasts based on surveillance data and contextual information. Our primary hypothesis is that an epidemic model trained on surveillance plus human judgment forecasts (a chimeric model) can produce more accurate long-term forecasts of incident hospitalizations compared to a control model trained only on surveillance. Humans have a finite amount of cognitive energy to forecast, limiting them to forecast a small number of states. Our secondary hypothesis is that a model can map human judgment forecasts from a small number of states to all states with similar performance. For the 2023/24 season, we collected weekly incident influenza hospitalizations for all US states, and 696 human judgment forecasts of peak epidemic week and the maximum number of hospitalizations (peak intensity) for ten of the most populous states. We found a chimeric model outperformed a control model on long-term forecasts. Compared to human judgment, a chimeric model produced forecasts of peak epidemic week and peak intensity with similar or improved performance. Forecasts of peak epidemic week and peak intensity for the ten states where humans input forecasts vs a model that extended these forecasts to all states showed similar performance to one another. Our results suggest human judgment forecasts are a viable data source that can improve infectious disease forecasts and support public health decisions.

stat.AP↗