Automatic TNM staging of colorectal cancer radiology reports using pre-trained language models.

Background And Objective: Colorectal cancer is one of the major causes of cancer death worldwide. Essential for prognosis and treatment planning, TNM staging offers critical insights into the advancement of colorectal cancer. However, manual TNM staging from colon magnetic resonance imaging (MRI) is a laborious and error prone process.

Automatic text classification of prostate cancer malignancy scores in radiology reports using NLP models.

This paper presents the implementation of two automated text classification systems for prostate cancer findings based on the PI-RADS criteria. Specifically, a traditional machine learning model using XGBoost and a language model-based approach using RoBERTa were employed. The study focused on Spanish-language radiological MRI prostate reports, which has not been explored before.

CARES: A Corpus for classification of Spanish Radiological reports.

This paper presents a new corpus of radiology medical reports written in Spanish and labeled with ICD-10. CARES (Corpus of Anonymised Radiological Evidences in Spanish) is a high-quality corpus manually labeled and reviewed by radiologists that is freely available for the research community on HuggingFace. These types of resources are essential for developing automatic text classification tools as they are necessary for training and tuning computational systems.

Combining word embeddings to extract chemical and drug entities in biomedical literature.

Background: Natural language processing (NLP) and text mining technologies for the extraction and indexing of chemical and drug entities are key to improving the access and integration of information from unstructured data such as biomedical literature.

Methods: In this paper we evaluate two important tasks in NLP: the named entity recognition (NER) and Entity indexing using the SNOMED-CT terminology. For this purpose, we propose a combination of word embeddings in order to improve the results obtained in the PharmaCoNER challenge.

How do sentiments affect virality on Twitter?

Virality on Twitter is catching the attention of researchers, trying to identify factors which increase or decrease the probability of retweeting. We study how terms expressing sentiments affect retweeting frequencies by means of a regression model on the number of retweets, which is specially accurate to deal with virality. We focus on the Spanish political situation during the pseudo-referendum held in Catalonia on 1 October 2017.

Automatic medical protocol classification using machine learning approaches.

Background And Objective: Assignment of medical imaging procedure protocols requires extensive knowledge about patient's data, usually included in radiological request forms and radiological reports. Assignment of protocol is required prior to radiological study acquisition, determining procedure for each patient. The automation of this protocol assignment process could improve the efficiency of patient's diagnosis.

COVID-19 detection in radiological text reports integrating entity recognition.

COVID-19 diagnosis is usually based on PCR test using radiological images, mainly chest Computed Tomography (CT) for the assessment of lung involvement by COVID-19. However, textual radiological reports also contain relevant information for determining the likelihood of presenting radiological signs of COVID-19 involving lungs. The development of COVID-19 automatic detection systems based on Natural Language Processing (NLP) techniques could provide a great help in supporting clinicians and detecting COVID-19 related disorders within radiological reports.

Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology.

Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common imaging tasks, not only for diagnostic purposes but also for image acquisition and postprocessing. AI is now recognized to be a driving initiative in every aspect of radiology.

How do we talk about doctors and drugs? Sentiment analysis in forums expressing opinions for medical domain.

Objective: The main goal of this study is to examine how people express their opinion in medical forums. We analyze the language used in order to determine the best way to tackle sentiment analysis in this domain.

Methods: We have applied supervised learning and lexicon-based sentiment analysis approaches over two different corpora extracted from social web.

Assessing Latin America's Progress Toward Achieving Universal Health Coverage.

Two commonly used metrics for assessing progress toward universal health coverage involve assessing citizens' rights to health care and counting the number of people who are in a financial protection scheme that safeguards them from high health care payments. On these metrics most countries in Latin America have already "reached" universal health coverage. Neither metric indicates, however, whether a country has achieved universal health coverage in the now commonly accepted sense of the term: that everyone--irrespective of their ability to pay--gets the health services they need without suffering undue financial hardship.

Trends in stunting and overweight in Peruvian pre-schoolers from 1991 to 2011: findings from the Demographic and Health Surveys.

Objective: To examine trends in stunting and overweight in Peruvian children, using 2006 WHO Multicentre Growth Reference Study criteria.

Design: Trend analyses using nationally representative cross-sectional surveys from Demographic and Health Surveys (1991-2011). We performed logistic regression analyses of stunting and overweight trends in sociodemographic groups (sex, age, urban-rural residence, region, maternal education and household wealth), adjusted for sampling design effects (strata, clusters and sampling weights).

Equity in health and health care in Peru, 2004-2008.

Objective: This study evaluates whether recent positive economic trends and pro-poor health policies have resulted in more health equity and explores key factors that explain such change.

Methods: This study focuses on the evolution of measures of health status (self-reported morbidity) and use of health care services obtained from the 2004 and 2008 rounds of the Peruvian National Household Survey (Encuesta Nacional de Hogares). It concentrates on health inequalities associated with socioeconomic status and uses interquintile differences (gradient), concentration indices with and without needs-based adjustments, and decomposition analysis.

Query expansion with a medical ontology to improve a multimodal information retrieval system.

Searching biomedical information in a large collection of medical data is a complex task. The use of tools and biomedical resources could ease the retrieval of the information desired. In this paper, we use the medical ontology MeSH to improve a Multimodal Information Retrieval System by expanding the user's query with medical terms.

The learning vector quantization algorithm applied to automatic text classification tasks.

Automatic text classification is an important task for many natural language processing applications. This paper presents a neural approach to develop a text classifier based on the Learning Vector Quantization (LVQ) algorithm. The LVQ model is a classification method that uses a competitive supervised learning algorithm.

Levels of endothelial and platelet microparticles and their interactions with leukocytes negatively correlate with organ dysfunction and predict mortality in severe sepsis.

Objective: Mortality in sepsis is believed to be associated with exaggerated inflammatory responses, but recent evidence suggests that poor outcome is associated with reduced inflammation. To test this hypothesis, we measured several inflammatory markers to determine whether any of them or any combinations are associated with mortality or organ dysfunction.

Design: Clinical study.

Poverty, health infrastructure and the nutrition of Peruvian children.

This paper offers empirical evidence on the impact of the expansion in health infrastructure of the 1990s upon child nutrition in Peru, as measured by the height for age z-score. Using a pooled sample of three rounds of the Peruvian DHS, I have controlled for biases in the allocation of public investments by using a district fixed effects model. The econometric analysis shows a positive effect of the expansion of the last decade in urban areas, but not in rural areas.

Public health infrastructure and equity in the utilization of outpatient health care services in Peru.

This article analyzes the magnitude and nature of socioeconomic differences in the utilization of outpatient health care services in Peru. In particular, it explores the potential equity-enhancing effect of the expansion and improvements in the network of health centres during the 1990s. The Peruvian health reform made relatively little progress in terms of the reform agenda promoted internationally during the 1990s.

Inequalities in health in Latin America and the Caribbean: descriptive and exploratory results for self-reported health problems and health care in twelve countries.

Objective: To explore and describe inequalities in health and use of health care as revealed by self-report in 12 countries of Latin America and the Caribbean.

Methods: A descriptive and exploratory study was performed based on the responses to questions on health and health care utilization that were included in general purpose household surveys. Inequalities are described by quintile of household expenditures (or income) per capita, sex, age group (children, adults, and older adults), and place of residence (urban vs.