AIMC Topic: Natural Language Processing

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Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.

PloS one
CONTEXT AND BACKGROUND: Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Ad...

SCRQE: Subjective comparative relation quintuple extraction from questions in product domain.

PloS one
The extraction of subjective comparative relations is essential in the field of question answering systems, playing a crucial role in accurately interpreting and addressing complex questions. To tackle this challenge, we propose the SCQRE model, spec...

From the -Factor to Cognitive Content: Detection and Discrimination of Psychopathologies Based on Explainable Artificial Intelligence.

Depression and anxiety
Differentiating psychopathologies is challenging due to shared underlying mechanisms, such as the -factor. Nevertheless, recent methodological advances suggest that distinct linguistic markers can help detect and differentiate these conditions. This...

Zipf's law in China's local government work reports: A 21-year study using natural language processing and regression analysis.

PloS one
The examination and application of Zipf's law is a significant topic in quantitative linguistics. This study presents an in-depth empirical investigation of this law in 651 Chinese provincial government work reports (2003-2023). Employing natural lan...

Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.

PloS one
BACKGROUND: Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the level of variation in PCC diagnoses observed across clinicians from a number of methodological angles a...

Careful design of Large Language Model pipelines enables expert-level retrieval of evidence-based information from syntheses and databases.

PloS one
Wise use of evidence to support efficient conservation action is key to tackling biodiversity loss with limited time and resources. Evidence syntheses provide key recommendations for conservation decision-makers by assessing and summarising evidence,...

A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2.

PloS one
BACKGROUND: Patient symptoms, crucial for disease progression and diagnosis, are often captured in unstructured clinical notes. Large language models (LLMs) offer potential advantages in extracting patient symptoms compared to traditional rule-based ...

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

PloS one
INTRODUCTION: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to inconsistent and not mandatory International Classification of Diseases (ICD) codes. We developed an...

Artificial Intelligence (AI) and academic publishing in psychiatry.

International journal of law and psychiatry
The current and potential impact of various applications of artificial intelligence (AI) to the field of academic publishing in psychiatry is the subject of increasing attention. At present, AI algorithms assist in data analysis, allowing researchers...

Artificial Intelligence-Assisted Matching of Human Postmortem Donors to Ocular Research Projects.

Advances in experimental medicine and biology
The scarcity of human ocular samples with short postmortem intervals (PMIs) is a significant issue in ophthalmic research and drug discovery. A contributing factor is that eye banks must manually match donor data to prospective research project crite...