Analyzing transfer learning impact in biomedical cross lingual named entity recognition and normalization

Background The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain. Methods In this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture. Results Several Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset. Conclusion These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages.This work was partially supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)

The impact of pretrained language models on negation and speculation detection in cross-lingual medical text: Comparative study

Background: Negation and speculation are critical elements in natural language processing (NLP)-related tasks, such as information extraction, as these phenomena change the truth value of a proposition. In the clinical narrative that is informal, these linguistic facts are used extensively with the objective of indicating hypotheses, impressions, or negative findings. Previous state-of-the-art approaches addressed negation and speculation detection tasks using rule-based methods, but in the last few years, models based on machine learning and deep learning exploiting morphological, syntactic, and semantic features represented as spare and dense vectors have emerged. However, although such methods of named entity recognition (NER) employ a broad set of features, they are limited to existing pretrained models for a specific domain or language. Objective: As a fundamental subsystem of any information extraction pipeline, a system for cross-lingual and domain-independent negation and speculation detection was introduced with special focus on the biomedical scientific literature and clinical narrative. In this work, detection of negation and speculation was considered as a sequence-labeling task where cues and the scopes of both phenomena are recognized as a sequence of nested labels recognized in a single step. Methods: We proposed the following two approaches for negation and speculation detection: (1) bidirectional long short-term memory (Bi-LSTM) and conditional random field using character, word, and sense embeddings to deal with the extraction of semantic, syntactic, and contextual patterns and (2) bidirectional encoder representations for transformers (BERT) with fine tuning for NER. Results: The approach was evaluated for English and Spanish languages on biomedical and review text, particularly with the BioScope corpus, IULA corpus, and SFU Spanish Review corpus, with F-measures of 86.6%, 85.0%, and 88.1%, respectively, for NeuroNER and 86.4%, 80.8%, and 91.7%, respectively, for BERT. Conclusions: These results show that these architectures perform considerably better than the previous rule-based and conventional machine learning-based systems. Moreover, our analysis results show that pretrained word embedding and particularly contextualized embedding for biomedical corpora help to understand complexities inherent to biomedical text.This work was supported by the Research Program of the Ministry of Economy and Competitiveness, Government of Spain (DeepEMR Project TIN2017-87548-C2-1-R)

A Systematic Approach for the Paper Review on the Utilization of Citrus Fruit Waste in the Philippines

One of the main contributors to the waste problem in the Philippines is citrus fruits because of their high yield. Various studies have investigated the utilization of citrus fruit waste for different applications. However, there is a lack of a systematic mapping study that can bring these studies together. Thus, this study employed a systematic approach to determine the utilization of citrus fruit wastes which will be beneficial to reduce waste in the landfill. This study sought to: 1) investigate the trends in current research on citrus fruit waste utilization, 2) identify the processes undergone by citrus fruit waste to achieve their respective applications, and 3) observe the techniques that have been utilized to evaluate the efficiency and performance of citrus fruit waste products. The study performed a general search for papers related to citrus fruit waste utilization in Scopus search engine. The documents were organized into specific categories, and data extraction was performed. After the data was analyzed and the following results were obtained: there is a continuous increase in the amount of research on citrus fruit waste utilization, citrus fruit peels are the most commonly used type of waste, citrus fruit wastes undergo rigorous processes that mostly involve heat to reach their applications, and most studies utilize pore size and BET surface area to evaluate fruit waste products. In conclusion, citrus fruit waste utilization is a topic with great potential, and will contribute to solving the waste management problem in the country

Optimización de la Lógica de Control del Sistema de Refrigeración de Agua de Turbinas de Gas en Central Termoeléctrica Puerto Bravo

La generación de energía eléctrica industrial se realiza en centrales eléctricas, las cuales transforman energía (química, cinética, térmica, nuclear, solar, entre otras) en energía eléctrica. La central termoeléctrica Puerto Bravo opera con turbinas de gas, fuentes de poder ampliamente usadas en la generación de electricidad a nivel mundial. Estos equipos han demostrado ser capaces de producir potencias muy elevadas, por lo que los trabajos de planta están orientados a mejorar el rendimiento y las condiciones de operación de las mismas, ya que de su correcto funcionamiento dependen la vida de muchas personas y una inversión económica considerable. En el desarrollo de esta tesis, se detalla las condiciones de operación, la creación de nuevas variables de control, la modificación de hojas de programación, la simulación y pruebas en tiempo real, parte del proceso de optimización de la lógica de control que se realizó en el sistemas de refrigeración de agua de las turbina de gas. Para verificar la efectividad del trabajo realizado, se evaluó el comportamiento del sistema frente a situaciones reales de operación de las turbinas, antes y después de la optimización, comprobando exitosamente la capacidad del sistema al controlar las condiciones críticas de manera automatizada.Tesi

Sustentación de caso: propuesta de un plan estratégico del ingreso al mercado peruano de una empresa del sector fast fashion

El presente trabajo analiza la situación de Fast Retailing, una empresa japonesa de la industria indumentaria dentro del sector retail, y se propone un plan estratégico para el ingreso de la compañía a Perú en el 2019. Con el fin de poder dar solución a este caso, se realizó un diagnóstico que incluye el análisis interno de la empresa, mostrando su propuesta de valor, actividades claves, ventaja competitiva, fortalezas y debilidades; así como, el análisis del macroentorno y de la industria en la medida de que ello sirva para identificar oportunidades y amenazas en el plan de expansión de la compañía. Como resultado del análisis se concluye que el Perú presenta grandes oportunidades para el ingreso de la compañía por el constante crecimiento de su economía y la proyección de venta de los retailers de ropa. Adicionalmente, la solidez de Fast Retailing le permitirá posicionarse rápidamente en el mercado peruano, el cual también sería una puerta de entrada para un futuro plan de expansión en Latinoamérica. Fundamentalmente, se recomienda enviar el mejor talento de la compañía para iniciar operaciones en el mercado peruano y a la par asesorarse con ejecutivos peruanos con experiencia.This paper analyzes the situation of Fast Retailing, a Japanese company in the clothing industry within the retail sector, and it proposes a strategic plan for the company's entry into Peru in 2019. In order to be able to solve this case , a diagnosis was made that includes the internal analysis of the company, showing its value proposition, key activities, competitive advantage, strengths and weaknesses; as well as the analysis of the macro environment and the industry to the extent that it serves to identify opportunities and threats in the company's expansion plan. As a result of the analysis, it is concluded that Peru presents great opportunities for the company to enter due to the constant growth of its economy and the sale forecast of the retail clothing sector. Additionally, the strength of Fast Retailing will allow position itself in the Peruvian market quickly, at the same time it would be a gateway for a future expansion plan in Latin America. Fundamentally, it is recommended to send the best talent of the company to start operations in the Peruvian market and on the other hand, be advised for the experience of Peruvian executives

Planeamiento estratégico para la empresa Mission Produce 2016-2020

Mission Produce es una empresa norteamericana dedicada a la producción, comercialización y distribución de palta tipo Hass, ubicada en el estado de California, Estados Unidos. Su amplia experiencia de 32 años le ha permitido suministrar las paltas desde California, México, Chile, Nueva Zelanda y Perú, logrando una participación del 25% del mercado norteamericano, ventas por USD 461 millones, un ROE 17,82% y un alto posicionamiento con todos sus clientes, gracias a la confianza ganada durante todos estos años por brindar paltas de alta calidad. En año el 2011, como parte de su estrategia corporativa de integración vertical hacia atrás, Mission Produce adquirió 2.000 hectáreas de terreno en el departamento de Trujillo, Perú. El presente planeamiento estratégico es elaborado para el periodo del 2016-2020, determinando que la adquisición de tierras en Perú es la mejor alternativa; la industria es medianamente atractiva, por el alto costo de inversión y años de experiencia que deben contar las empresas. Mission Produce cuenta con los recursos y capacidades que permiten sostener su ventaja competitiva en enfoque en costos y satisfacer el incremento de la demanda mundial, para lo cual se plantean cinco objetivos estratégicos: aumento de participación anual del 1% en cada región; disminución en mermas al 0,5% de la producción; alcanzar un ROE del 34,76% para el año 2020; consolidar su ventaja competitiva, reduciendo anualmente en 1,5% el costo de la venta, y generar valor económico y social sostenible. Todos los objetivos planteados están alineados con la visión y misión de la empresa. Cada área funcional de Mission Produce aporta al éxito de los objetivos trazados por la organización

Influencia de la familia sobre las conductas antisociales en adolescentes de Arequipa-Perú

El objetivo fue determinar la influencia de la familia sobre las conductas antisociales en adolescentes no institucionalizados. La muestra consistió en 929 alumnos de secundaria entre 13 y 17 años de edad. Se aplicó una batería de instrumentos sobre datos sociodemográficos, conductas antisociales y funcionamiento familiar: relación, satisfacción, cohesión,  adaptabilidad y comunicación familiar. Se analizaron las variables por medio de modelos de ecuaciones estructurales diferenciados por sexo. Los resultados mostraron que el funcionamiento familiar, en ambos sexos, y el número de hermanos, en los varones, son factores protectores frente a las conductas antisociales. Los factores de riesgo son: maltrato infantil y violencia entre los padres, en las mujeres; además del consumo de alcohol en los padres, para ambos sexos

Análisis exploratorio de la Escala de Timidez Revisada de Cheek y Buss en estudiantes de Psicología de una universidad privada de Arequipa

En esta investigación psicométrica se analiza la estructura interna y la confiabilidad de la Escala de Timidez revisada de Cheek y Buss (1981), en un amuestra de 104 estudiantes universitarios de la carrea de Psicología de una universidad privada de la ciudad de Arequipa, de los cuales el 74% fueron mujeres y el 26% varones con una edad media de 19 años. Se trata de un estudio preliminar y exploratorio que sigue los criterios de la teoría clásica de los tests y la teoría factorial. Los resultados indican que la prueba tiene una estructura de dos factores que explica el 70.5% de la varianza total. Los factores encontrados mediante AFE verifican la estructura interna reportada por Caycho et al. (2013), con óptimos índices de confiabilidad mediante la prueba Alfa de Cronbach (α= .805; α= .914), pero se tuvo que eliminar el ítem 7 debido a que saturaba en ambos factores

A two-stage deep learning approach for extracting entities and relationships from medical texts

This Work Presents A Two-Stage Deep Learning System For Named Entity Recognition (Ner) And Relation Extraction (Re) From Medical Texts. These Tasks Are A Crucial Step To Many Natural Language Understanding Applications In The Biomedical Domain. Automatic Medical Coding Of Electronic Medical Records, Automated Summarizing Of Patient Records, Automatic Cohort Identification For Clinical Studies, Text Simplification Of Health Documents For Patients, Early Detection Of Adverse Drug Reactions Or Automatic Identification Of Risk Factors Are Only A Few Examples Of The Many Possible Opportunities That The Text Analysis Can Offer In The Clinical Domain. In This Work, Our Efforts Are Primarily Directed Towards The Improvement Of The Pharmacovigilance Process By The Automatic Detection Of Drug-Drug Interactions (Ddi) From Texts. Moreover, We Deal With The Semantic Analysis Of Texts Containing Health Information For Patients. Our Two-Stage Approach Is Based On Deep Learning Architectures. Concretely, Ner Is Performed Combining A Bidirectional Long Short-Term Memory (Bi-Lstm) And A Conditional Random Field (Crf), While Re Applies A Convolutional Neural Network (Cnn). Since Our Approach Uses Very Few Language Resources, Only The Pre-Trained Word Embeddings, And Does Not Exploit Any Domain Resources (Such As Dictionaries Or Ontologies), This Can Be Easily Expandable To Support Other Languages And Clinical Applications That Require The Exploitation Of Semantic Information (Concepts And Relationships) From Texts...This work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)