Bigram feature extraction and conditional random fields model to improve text classification clinical trial document

In the field of health and medicine, there is a very important term known as clinical trials. Clinical trials are a type of activity that studies how the safest way to treat patients is. These clinical trials are usually written in unstructured free text which requires translation from a computer. The aim of this paper is to classify the texts of cancer clinical trial documents consisting of unstructured free texts taken from cancer clinical trial protocols. The proposed algorithm is conditional random Fields and bigram features. A new classification model from the cancer clinical trial document text is proposed to compete with other methods in terms of precision, recall, and f-1 score. The results of this study are better than the previous results, namely 88.07 precision, 88.05 recall and f-1 score 88.06

Classifying Eligibility Criteria in Clinical Trials Using Active Deep Learning

In this paper we propose an active deep learning approach to automatically classify eligibility criteria of clinical trials, an application that has not been explored in machine learning. We collected all clinical trial data from the National Cancer Institute website, and applied word2vec to learn word embeddings for eligibility criteria. Criteria encoded with word embeddings were then fed into a multi-layer convolution neural network (CNN) for classification. To overcome the challenge of non-existing class labels, we designed an active learning algorithm that uses uncertainty cluster sampling to navigate the dataset and strategically propagate obtained labels to expand the training set for CNN. Experimental results show that word2vec successfully learns meaningful embeddings in criteria data, and the active deep learning approach reports a significant lower error rate in classification than the baseline k-nearest neighbor method

Successful Marketing Strategies for Sustaining Nigerian Small and Medium Enterprises

Owners of small and medium enterprises (SMEs) constantly face challenges to sustain their businesses. More than 80% of SMEs in Nigeria fail within the first 5 years of operation. SME owners are concerned with the high failure rate of businesses within the first years of operation, which could negatively impact business sustainability, employment, and economic growth. Grounded in the resource-advantage theory of competition and the social marketing strategic model, the purpose of this qualitative multiple case study was to explore the marketing strategies five chief executive officers from five Nigerian SMEs used to sustain their businesses beyond 5 years. Data were collected using semistructured interviews with open-ended questions, interview notes, organization marketing strategies, marketing plans, websites, and social media sites. Through methodological triangulation and Yin’s five-stage data analysis process, four themes emerged: market segmentation and social media marketing, market readiness of new products and services, agility and flexibility through business process modification to suit the market, and arranging targeted visits to establish a long-term relationship with customers and stakeholders. A key recommendation is for SME owners to adopt a strategy for market readiness, continuous market research, customer data gathering, and business intelligence to ascertain market readiness before launching new products and services to target markets. The implication for positive social change includes increased SME sustainability, business profitability, and employment rate, which could result in an improved standard of living and economic growth in Nigeria