Academic Articles & Research Papers

Bias and Fairness in Natural Language Processing

Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing applications. These approaches automatically learn how to make decisions based on the statistics an...

Aligning machines with human values: opacity and ethical sustainability of AI

Abstract The increasingly rapid development of deep learning algorithms is radically transforming society. The potential of neural models is paving the way for the automation of areas previously unrelated to AI, from medical diagnostics to ...

Machine Unlearning: A Perspective, Taxonomy, and Benchmark Evaluation

Machine Learning (ML) models trained on large-scale datasets learn useful predictive patterns, but they may also memorize undesired information, leading to risks such as information leakage, bias, copyright violations, and privacy attacks. ...

Developing Clinical Risk Prediction Models for Worsening Heart Failure Events and Death by Left Ventricular Ejection Fraction

Background There is a need to develop electronic health record–based predictive models for worsening heart failure (WHF) events across clinical settings and across the spectrum of left ventricular ejection fraction (LVEF). Methods and Resul...

Natural language processing applied to mental illness detection: a narrative review

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people's life with impact on society's health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors...

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

BACKGROUND: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the seconda...

SAM-trace: a fuzzy logic approach in identifying interaction effects in the exacerbation of childhood malnutrition severity

Abstract Pediatric malnutrition remains a major global health challenge. Conventional WHO-based binary classification (Severe Acute Malnutrition (SAM) / Moderate Acute Malnutrition (MAM) relies solely on anthropometric thresholds. It fails ...

Natural Language Processing of Social Media as Screening for Suicide Risk

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people's lives are meaningfully affected, and almost any other statistic around suicide deaths i...

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Ensembles machine learning for short duration rainfall intensity forecasting

Abstract This study introduces an ensemble machine learning (EML) framework to enhance short-duration rainfall-intensity prediction under limited-data conditions. By integrating multiple base learners within a stacking architecture, the pro...

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

Purpose NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analy...

Empowering text classification with NLP and explainable AI for enhanced interpretability

Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address...

Theoretical Issues in Natural Language Processing

Accompanying continued industrial production and sales of artificial intelligence and expert systems is the risk that difficult and resistant theoretical problems and issues will be ignored. The participants at the Third Tinlap Workshop, wh...

Natural Language Processing in Surgery

OBJECTIVE: The aim of this study was to systematically assess the application and potential benefits of natural language processing (NLP) in surgical outcomes research. SUMMARY BACKGROUND DATA: Widespread implementation of electronic health...

USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION

The expansion of nuclear technologies in various industries, combined with the constant threat of radiation-related incidents, highlights the urgent need for effective radiation education. This study is devoted to an empirical investigation...

Explainable Sentiment-Based Reputational Risk Modelling in Ugandan Megaprojects

Reputational risk reflected in digital public discourse can significantly influence stakeholder perceptions, public trust, and decision-making surrounding government megaprojects. Existing computational approaches primarily focus on sentime...

Pathways to schizophrenia: Divergent effects of childhood trauma and polygenic risk on cognition and subcortical dynamics.

The Social Impact of Natural Language Processing

Medical sciences have long since established an ethics code for experiments, to minimize the risk of harm to subjects.Natural language processing (NLP) used to involve mostly anonymous corpora, with the goal of enriching linguistic analysis...

Unify Framework for Crime Data Summarization using RSS Feed Service

This research presents online crime news analysis using text mining, Natural Language Processing framework (General Framework for Text Mining: GATE), and data warehouse (DW) technologies. The proposed framework aims at extracting key featur...

Enhancing adult lifelong learning through data fusion algorithms and quality assurance

Abstract This study presents a Data Fusion + Random Forest (RF) framework to predict adult learners’ educational outcomes, such as dropout risk and course completion. Adult lifelong learning is crucial for personal and professional growth, ...