Artificial intelligence based Chinese clinical trials eligibility criteria classification / 生物医学工程学杂志

Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro

Research on manufacturing text classification based on improved genetic algorithm

ABSTRACT According to the features of texts, a text classification model is proposed. Base on this model, an optimized objective function is designed by utilizing the occurrence frequency of each feature in each category. According to the relation matrix oftext resource and features, an improved genetic algorithm is adopted for solution with integral matrix crossover, transposition and recombination of entire population. At last the sample date of manufacturing text information from professional resources database system is taken as an example to illustrate the proposed model and solution for feature dimension reduction and text classification. The crossover and mutation probabilities of algorithm are compared vertically and horizontally to determine a group of better parameters. The experiment results show that the proposed method is fast and effective.

Procesamiento de lenguaje natural para el análisis de lenguaje subjetivo / Natural language procesing for the analysis of subjective language

Describimos la aplicación de la tecnología de procesamiento de lenguaje natural (NLP) al análisis del lenguaje subjetivo. En particular, nos concentramos en la problemática de la clasificación de opinión de material textual extraído de fuentes de datos relacionados con negocios. Estudiamos la derivación de los valores de opiniones de palabras a partir del recurso léxico SentiWordNet y utilizamos estos valores para la interpretación de texto con el objetivo de obtener la valoración de una opinión a partir de sus palabras y frases. Utilizamos características de las palabras para inducir un clasificador basado en el uso de Máquinas de Vectores de Soporte que alcanzan resultados acordes con el estado del arte. También mostramos experimentos preliminares en los que el uso de resúmenes de opiniones ofrece ventaja competitiva para el problema de clasificación respecto del uso de documentos completos cuando los documentos son extensos y contienen material tanto subjetivo como no-subjetivo.

We describe the application of natural language processing (NLP) technology to the analysis of subjective language. In particular we concentrate on the problem of opinion classification of textual material extracted from business-related data-sources. We study the derivation of sentiment values for words from the SentiWordNet lexicalresource and use them for text interpretation to produce word, sentence, and text based sentiment features for opinion classification. We use word-based and sentiment basedfeatures to induce a classifier based on the use of Support Vector Machinesachieving state of the art results. We also show preliminary experiments where the use of summaries before opinion classification provides competitive advantage over the use of full documents when the documents are long and contain both subjective andnon-subjective material.

Classification of Human Papillomavirus (HPV) Risk Type via Text Mining

Human Papillomavirus (HPV) infection is known as the main factor for cervical cancer which is a leading cause of cancer deaths in women worldwide. Because there are more than 100 types in HPV, it is critical to discriminate the HPVs related with cervical cancer from those not related with it. In this paper, the risk type of HPVs using their textual explanation. The important issue in this problem is to distinguish false negatives from false positives. That is, we must find high-risk HPVs as many as possible though we may miss some low-risk HPVs. For this purpose, the AdaCost, a cost-sensitive learner is adopted to consider different costs between training examples. The experimental results on the HPV sequence database show that the consideration of costs gives higher performance. The improvement in F-score is higher than that of the accuracy, which implies that the number of high-risk HPVs found is increased.