Spectrum of interstitial lung disease from a tertiary care hospital in Karachi

Objective: To determine the clinical features and patterns of interstitial lung disease.Methods: This retrospective study was conducted at the Aga Khan University Hospital, Karachi, and comprised record of patients diagnosed with interstitial lung disease from January 2005 to December 2015. All patients aged 16 years and above diagnosed with interstitial lung disease on the basis of clinical features, radiological features on high-resolution computed tomography of the chest, and lung biopsies were included. SPSS 19 was used for data analysis.Results: Of the 537 patients, 324(60.3%) of the participants were females. The overall mean age was 60.5±14.9 years. The most common co-morbid condition was diabetes mellitus in 72(13.4%) patients, followed by hypertension in 48(8.9%) and ischaemic heart disease in 21(3.9%). The most common interstitial lung disease was idiopathic pulmonary fibrosis in 217(40.4%) patients, followed by non-specific interstitial pneumonia in 106(19.7%), sarcoidosis in 82(15.3%) and connective tissue disease-related interstitial lung disease in 56(10.4%) patients.Conclusions: Idiopathic pulmonary fibrosis was found to be the most common interstitial lung disease subtype followed by non-specific interstitial pneumonia, sarcoidosis and connective tissue disease-related-interstitial lung disease

An experimental comparison of unsupervised keyphrase extraction techniques for extracting significant information from scientific research articles

The automatic extraction of key information from an article that expresses all of the document’s main elements is referred to as keyphrase extraction. The number of scientific research articles each year is growing. Finding a research article on relevant topics or summarizing a particular research article using important information has become time-consuming by going through the entire article. Therefore, the textual information processing task involves the automatic keyphrase extraction from a document that expresses all of the document’s main elements. This article aims to make an experimental comparison of different unsupervised keyphrase extraction approaches, namely statistical-based, graph-based, and tree-based. The experiment is conducted upon 120 research articles from different subject areas of the computer science. The comparison between different techniques is made by calculating the precision, recall, and Fl-score. The overall performance of the experimental result shows that KP-Miner, a statistical-based technique, outperforms all the other graph-based and tree-based techniques. Among the other techniques, the tree-based technique TeKET performs better after KPMiner. The statistical-based and tree-based approach performs better than the graph-based approach

Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding

A textual data processing task that involves the automatic extraction of relevant and salient keyphrases from a document that expresses all the important concepts of the document is called keyphrase extraction. Due to technological advancements, the amount of textual information on the Internet is rapidly increasing as a lot of textual information is processed online in various domains such as offices, news portals, or for research purposes. Given the exponential increase of news articles on the Internet, manually searching for similar news articles by reading the entire news content that matches the user’s interests has become a time-consuming and tedious task. Therefore, automatically finding similar news articles can be a significant task in text processing. In this context, keyphrase extraction algorithms can extract information from news articles. However, selecting the most appropriate algorithm is also a problem. Therefore, this study analyzes various supervised and unsupervised keyphrase extraction algorithms, namely KEA, KP-Miner, YAKE, MultipartiteRank, TopicRank, and TeKET, which are used to extract keyphrases from news articles. The extracted keyphrases are used to compute lexical and semantic similarity to find similar news articles. The lexical similarity is calculated using the Cosine and Jaccard similarity techniques. In addition, semantic similarity is calculated using a word embedding technique called Word2Vec in combination with the Cosine similarity measure. The experimental results show that the KP-Miner keyphrase extraction algorithm, together with the Cosine similarity calculation using Word2Vec (Cosine-Word2Vec), outperforms the other combinations of keyphrase extraction algorithms and similarity calculation techniques to find similar news articles. The similar articles identified using KPMiner and the Cosine similarity measure with Word2Vec appear to be relevant to a particular news article and thus show satisfactory performance with a Normalized Discounted Cumulative Gain (NDCG) value of 0.97. This study proposes a method for finding similar news articles that can be used in conjunction with other methods already in use

National registry of interstitial lung disease from Pakistan

Introduction: Interstitial lung disease (ILD) is a heterogeneous group of over 200 parenchymal lung diseases with a myriad of etiologies. Interstitial lung disease registries from around the world show varying prevalence and incidence of these diseases. The aim of this study was to determine the epidemiology and characteristics of ILD in Pakistan.Methods: This web-based registry, which is the first multicenter registry of ILD from Pakistan, recruited patients from 10 centers of five major cities between January 2016 and March 2019.Results: A total of 744 patients were enrolled in the registry. The five most frequent ILDs were idiopathic pulmonary fibrosis (IPF) 34.4%, hypersensitivity pneumonitis (HP) - 17.7%, idiopathic nonspecific interstitial pneumonitis (iNSIP) - 16.8%, connective tissue disease-associated ILD (CTD-ILD) - 16.3%, and sarcoidosis - 9.1%.Conclusion: Idiopathic pulmonary fibrosis is the most prevalent ILD in Pakistan, followed by HP and iNSIP. An ongoing prospective registry with longitudinal follow-up will help us further elaborate on the clinical characteristics, treatment, and survival outcome of patients with ILD

Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis

Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert-provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert-provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques

An automated materials and processes identification tool for material informatics using deep learning approach

This article reports a tool that enables Materials Informatics, termed as MatRec, via a deep learning approach. The tool captures data, makes appropriate domain suggestions, extracts various entities such as materials and processes, and helps to establish entity-value relationships. This tool uses keyword extraction, a document similarity index to suggest relevant documents, and a deep learning approach employing Bi-LSTM for entity extraction. For example, materials and processes for electrical charge storage under an electric double layer capacitor (EDLC) mechanism are demonstrated herewith. A knowledge graph approach finds and visualizes different latent knowledge sets from the processed information. The MatRec received an F1 score of 9̃6% for entity extraction, 8̃3% for material-value relationship extraction, and 8̃7% for process-value relationship extraction, respectively. The proposed MatRec could be extended to solve material selection issues for various applications and could be an excellent tool for academia and industry

Predicting young imposter syndrome using ensemble learning

Background. Imposter syndrome (IS), associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students' IS using the machine learning ensemble approach. Methods. This study was a cross-sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction

An efficientnet to classify monkeypox-comparable skin lesions using transfer learning

Monkeypox is an infectious illness caused by the DNA-based monkeypox virus, which has raised public health concerns due to its rapid transmission to over 50 countries. Direct physical interaction with infected humans or infected animals is the main reason behind the spread of this virus. The appearance of skin problems such as smallpox and rashes are the most frequently reported symptoms of this virus. Since cases of monkeypox are increasing rapidly around the world, stopping the spread of this zoonosis by providing early diagnosis and treatment is crucial before the emergence of a pandemic similar to COVID-19. In this study, we aim to propose a transfer learning-based approach using the EfficientNet-B0 architecture to identify monkeypox subjects by using skin lesion image samples. However, distinguishing monkeypox from other comparable infectious skin illnesses like chickenpox and measles is challenging. Therefore, additionally, this study identifies other diseases that also cause blisters and rashes on the skin, like chickenpox, and measles. During the data distribution phase, 5-fold cross-validation is used to validate the work's reliability by assuring that every sample is utilized for training and validation. For the evaluation of the model's classification performance, accuracy and loss are recorded for each training epoch. Moreover, precision, recall, F1-score, and confusion matrix are generated upon completion of the model training. This proposed approach is experimented on a public dataset and has shown remarkable performance by providing an overall 96.53% classification accuracy, 96.57% precision, 96.53% recall, and 96.52% F1-score

An adaptive medical cyber-physical system for post diagnosis patient care using cloud computing and machine learning approach

Medical care is one of the most basic human needs. Due to the global shortage of doctors, nurses, and other healthcare personnel, medical cyber-physical systems are quickly becoming a viable option. Post-diagnosis surveillance is an essential application of these systems, which can be performed more successfully using various monitoring devices rather than active observation by nurses in their physical presence. However, most existing solutions for this application are rigid and do not consider current difficulties. Intelligent and adaptive systems can overcome the challenges because of the advances in relevant technology, especially healthcare 4.0. Therefore, this work presents an adaptive system based on cloud and edge computing architecture and machine learning approaches to perform post-diagnosis medical tasks on patients, thus reducing the need for nurses, especially in the post-diagnosis phase