Unstructured Format Research Articles

Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study.

ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have examined the quality, similarity (abstracts being similar to the original one), and accuracy of the abstracts generated by ChatGPT when researchers provide full-text basic research papers. We aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research. We selected 30 basic research papers from Nature, Genome Biology, and Biological Psychiatry. Excluding abstracts, we inputted the full text into ChatPDF, an application of a language model based on ChatGPT, and we prompted it to generate abstracts with the same style as used in the original papers. A total of 8 experts were invited to evaluate the quality of these abstracts (based on a Likert scale of 0-10) and identify which abstracts were generated by ChatPDF, using a blind approach. These abstracts were also evaluated for their similarity to the original abstracts and the accuracy of the AI content. The quality of ChatGPT-generated abstracts was lower than that of the actual abstracts (10-point Likert scale: mean 4.72, SD 2.09 vs mean 8.09, SD 1.03; P<.001). The difference in quality was significant in the unstructured format (mean difference -4.33; 95% CI -4.79 to -3.86; P<.001) but minimal in the 4-subheading structured format (mean difference -2.33; 95% CI -2.79 to -1.86). Among the 30 ChatGPT-generated abstracts, 3 showed wrong conclusions, and 10 were identified as AI content. The mean percentage of similarity between the original and the generated abstracts was not high (2.10%-4.40%). The blinded reviewers achieved a 93% (224/240) accuracy rate in guessing which abstracts were written using ChatGPT. Using ChatGPT to generate a scientific abstract may not lead to issues of similarity when using real full texts written by humans. However, the quality of the ChatGPT-generated abstracts was suboptimal, and their accuracy was not 100%.

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.

In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

Enhanced abnormal data detection hybrid strategy based on heuristic and stochastic approaches for efficient patients rehabilitation

Over the last few years, substantial research has been conducted towards developing efficient abnormal detection techniques while considering efficiency, accuracy, high-dimensional data, distributed environments, and others. Researchers increasingly deal with “abnormalities” in clinical patient data to derive relevant clinical knowledge for making informed decisions. However, data collection for clinically relevant research is often guided by patient conditions and administrative or clinical requirements rather than a regular schedule. Therefore, clinical data is frequently obtained in an unreliable form, characterized by data outliers and inconsistencies, incomplete information, and an unstructured format that varies based on patient types and data structures. In this research study, an enhanced hybrid AD strategy is developed based on heuristic and stochastic methods to cope with abnormalities in the clinical data of patients. The proposed hybrid strategy employs optimal k-means clustering as a heuristic method to cluster the clinical data based on the patient’s routine exercise characteristics to cope with abnormalities efficiently. Next, an interquartile range-based stochastic approach is employed as a statistical method to detect and eliminate abnormal data points by providing only reliable and effectual data to medical practitioners. The main objective of this research article is to facilitate healthcare and research practitioners by dealing with a high dimensional massive amount of inconsistent and incomplete clinical data of patients to detect and discard anomalous data points for providing only efficacious information. Furthermore, the AutoML paradigm is employed to develop an optimal regression model for analyzing the impact of the proposed hybrid strategy for abnormal pattern detection. In addition, different statistical error estimation measures are used to evaluate the empirical effectiveness of the proposed hybrid strategy using AutoML. The experiment results show a noteworthy improvement in terms of the R2 score for predicting healthcare indicators compared to the existing state-of-the-art regression models. Our optimal regression model performed efficiently regarding the R2 score and MAPE; it achieved an R2 score of 0.9855 and 0.9850 for predicting the Borg RPE and TUG, respectively. Similarly, our model achieved a low prediction error in terms of MAPE for predicting both health functional indicators; it achieved a MAPE of 6.57% and 5.19% for Borg RPE and TUG prediction. Our contribution signifies that the performance of the AutoML improves and outperforms traditional regression models while applying our proposed hybrid abnormal detection model to the patient’s rehabilitation data for accurately dealing with anomalous data.

Enhancing pre-trained contextual embeddings with triplet loss as an effective fine-tuning method for extracting clinical features from electronic health record derived mental health clinical notes

The development and application of real-world evidence in the field of mental health trails other therapeutic areas like oncology and cardiovascular diseases, largely because of the lack of frequent, structured outcomes measures in routine clinical care. A wealth of valuable patient-level clinical data resides in an unstructured format in clinical notes documented at each clinical encounter. Manual extraction of this information is not scalable, and heterogeneity in recording patterns and the heavily context-dependent nature of the content renders keyword-based automated searches of little practical value. While state-of-the-art natural language processing (NLP) models based on the transformer architecture have been developed for information extraction tasks in the mental health space, they are not trained on unstructured clinical data that capture the nuances of different dimensions of mental health (e.g., symptomology, social history, etc.). We have developed a novel transformer architecture-based NLP model to capture core clinical features of patients with major depressive disorder (MDD). Initialized on MentalBERT model weights, we pre-trained our model further on clinical notes from routine mental health care and fine-tuned using triplet loss, an effective feature embedding regularizer which boosts classification and extraction of 3 specific features in patients with MDD: anhedonia, suicidal ideation with plan or intent (SP), and suicidal ideation without plan or intent (SI) or where plan or intent are unknown. Training and testing data were annotated by mental health clinicians. Using triplet loss for fine tuning led to improvement in model performance benchmarked against other standard models (MentalBERT and BioClinicalBERT) on the same tasks, achieving F1 scores of 0.99 for anhedonia, 0.94 for SP, and 0.88 for SI. Model robustness was tested by testing sensitivity of model predictions on modifications to test sentences. The application of such an NLP model can be further scaled to capture clinical features of other disorders as well as other domains like social history or history of illness.

Hybrid unstructured text features for meta-heuristic assisted deep CNN-based hierarchical clustering

The text clustering model becomes an essential process to sort the unstructured text data in an appropriate format. But, it does not give the pave for extracting the information to facilitate the document representation. In today’s date, it becomes crucial to retrieve the relevant text data. Mostly, the data comprises an unstructured text format that it is difficult to categorize the data. The major intention of this work is to implement a new text clustering model of unstructured data using classifier approaches. At first, the unstructured data is taken from standard benchmark datasets focusing on both English and Telugu languages. The collected text data is then given to the pre-processing stage. The pre-processed data is fed into the model of the feature extraction stage 1, in which the GloVe embedding technique is used for extracting text features. Similarly, in the feature extraction stage 2, the pre-processed data is used to extract the deep text features using Text Convolutional Neural Network (Text CNN). Then, the text features from Stage 1 and deep features from Stage 2 are all together and employed for optimal feature selection using the Hybrid Sea Lion Grasshopper Optimization (HSLnGO), where the traditional SLnO is superimposed with GOA. Finally, the text clustering is processed with the help of Deep CNN-assisted hierarchical clustering, where the parameter optimization is done to improve the clustering performance using HSLnGO. Thus, the simulation findings illustrate that the framework yields impressive performance of text classification in contrast with other techniques while implementing the unstructured text data using different quantitative measures.

Evaluation of Responsive With Big Data Technologies

The volume of produced digital data &amp; everyday usage is increasing as a result of rising man-machine interaction, process automation, and falling hardware and software costs.&#x0D; The vast amount of digital information produced per second worldwide in unstructured, structured, and semi-structured formats is referred to as big data in this context.&#x0D; Big data analytics is a new topic that has inspired researchers all over the world to create, develop, and implement a variety of mechanisms, technologies, architectures, and areas for evaluating the enormous amount of data created every day. Traditional database management systems struggle to make sense of the massive amounts of data that make up "big data." The paper describes a few analyses, including word count, sentiment analysis, and responsive analysis.&#x0D; Responsiveness is crucial for improving the system, identifying vulnerabilities, and ensuring that tasks are distributed fairly. Responsive is important data that can be used for making wise decisions.&#x0D; Responsive is significant for both strengths and weaknesses, not just when it identifies flaws. If the responsive analysis is performed incorrectly, the analysis's conclusion will likewise be incorrect. As a result, the system as a whole will be inaccurate because the pattern that was discovered will also be incorrect. We will utilize the Map-Reduce framework to create this suggested system for responsive analysis, and Hadoop will be used for storage.

Automated invoice processing: Machine learning-based information extraction for long tail suppliers

Automation of incoming invoices processing promises to yield vast efficiency improvements in accounting. Until a universal adoption of fully electronic invoice exchange formats has been achieved, machine learning can help bridge the adoption gaps in electronic invoicing by extracting structured information from unstructured invoice formats. Machine learning especially helps the processing of invoices of suppliers who only send invoices infrequently, as the models are able to capture the semantic and visual cues of invoices and generalize them to previously unknown invoice layouts. Since the population of invoices in many companies is skewed toward a few frequent suppliers and their layouts, this research examines the effects of training data taken from such populations on the predictive quality of different machine-learning approaches for the extraction of information from invoices. Comparing the different approaches, we find that they are affected to varying degrees by skewed layout populations: The accuracy gap between in-sample and out-of-sample layouts is much higher in the Chargrid and random forest models than in the LayoutLM transformer model, which also exhibits the best overall predictive quality. To arrive at this finding, we designed and implemented a research pipeline that pays special attention to the distribution of layouts in the splitting of data and the evaluation of the models.

Crop Information Retrieval Framework Based on LDW-Ontology and SNM-BERT Techniques

Currently, on the Internet, the information about agriculture is augmenting extremely; thus, searching for precise, relevant data of various details is highly complicated. To deal with particular difficulties like lower relevancy rate, false detection of retrieval resources, poor similarity rate, unstructured data format, multivariate data, irrelevant spelling, and higher computation time, an intelligent Information Retrieval (IR) system is required. An IR Framework centered on Levenshtein Distance Weight-centric Ontology (LDW-Ontology) and Sutskever Nesterov Momentum-centred Bidirectional Encoder Representation from Transformer (SNM-BERT) methodologies is presented here to overcome the complications as mentioned earlier. Firstly, the data is pre-processed, transmuting the unstructured data into a structured format, thus mitigating the error probabilities. Then, the LDW-Crop Ontology construction is done regarding the structured data. In the methodology presented, significance, frequency,and the suggestion of word in mind are considered to build Crop ontology. In the MongoDB database, the data being constructed are amassed. Then, by utilizing SNM-BERT, the data is trained for IR regarding clustered input produced by Inter Quartile Pruning Range-centred Hierarchical Divisive Clustering (IQPR-HDC) model. The LDW is computed for the provided user query; subsequently, the similarity evaluation outcomes are obtained from the database. The experiential evaluation displays that when analogized with the prevailing methodologies, a better accuracy of 94 % for simple queries and 92% for complex queries is achieved. Along with retrieval rate with lower computation time is achieved by the proposed methodology.

Deep Learning and Text Mining: Classifying and Extracting Key Information from Construction Accident Narratives

Construction accidents can lead to serious consequences. To reduce the occurrence of such accidents and strengthen the execution capabilities in on-site safety management, managers must analyze accident report texts in depth and extract valuable information from them. However, accident report texts are usually presented in unstructured or semi-structured forms; analyzing these texts manually requires a lot of time and effort, it is difficult to cope with the demand of analyzing a large number of accident texts, and the quality of key information extracted manually may be poor. Therefore, this study proposes a classification method based on natural language processing (NLP) technology. First, we developed a text classification model based on a convolutional neural network (CNN) that can automatically classify accident categories based on accident text features. Next, taking the classified fall accidents as an example, we extracted key information from accident narratives using the term frequency-inverse document frequency (TF-IDF) method and presented it visually using word clouds. The results show that the overall accuracy of the CNN model reaches 84%, which is better than the other three shallow machine-learning models. Then, eight key accident areas and three accident-prone operations were identified using the TF-IDF algorithm. This study can provide important guidance for project managers and can be used for on-site safety management to help prevent production safety accidents.

OIE4PA: open information extraction for the public administration

Tenders are powerful means of investment of public funds and represent a strategic development resource. Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration. Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. Nevertheless, OIE4PA adopts a multilingual approach so it can be applied to several languages by providing appropriate training data. Rather than purely training a classifier on a portion of the extracted relations, the backbone idea of our learning strategy is to put a supervised method based on self-training to the proof and to assess whether or not it improves the performance of the classifier. For evaluation purposes, we built a dataset composed of 2,000 triples which have been manually annotated by two human experts. The in-vitro evaluation shows that OIE4PA achieves a MacroF1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_1$$\\end{document} equal to 0.89 and a 91%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} accuracy. In addition, OIE4PA was used as the pillar of a prototype search engine, which has been evaluated through an in-vivo experiment with positive feedback from 32 final users, obtaining a SUS score equal to 83.98.

Intelligent information extraction from government on-site inspection reports of construction projects: A graph-based text mining approach

Government inspection reports detail unsafe acts and conditions that arise on construction sites, especially front-line managers’ non-compliance practices, which are hardly identified during self-inspections. Such information serves as a valuable learning source for better construction management. However, non-compliance issue records in inspection reports are typically stored in unstructured text formats, making data analysis challenging. In response, an intelligent text mining framework integrating graph analysis and visualization is presented. The proposed framework comprises data collection and preprocessing and three levels of text analysis: word, sentence, and document. The main tasks of the word-level analysis include (1) extracting keywords using KeyBERT and (2) identifying non-compliance issue types based on community detection in a keyword co-occurrence graph. The sentence-level analysis is performed to automatically classify text data from inspection reports by determining the degree of similarity between texts and communities and assigning the most similar community to each text. The document-level analysis aims to identify the interrelations between various non-compliance issues through association rule mining and a community interaction network. The framework is validated by a total of 6,153 text data featuring non-compliance issues from 322 government on-site inspection reports in Shanghai, China. The results demonstrate that the critical word-level features of non-compliance issues can be accurately identified using KeyBert, which outperforms other state-of-the-art methods. Our approach can also automate the development of a data-driven taxonomy for non-compliance issues and the classification of the corresponding records, requiring less manual intervention than conventional text classification models.

How could forensic psychiatry be made more attractive?

Decrease in the number of forensic psychiatric experts recently reached a critical level. Shortage of forensic experts caused difficulties in the health care as well as in the justice system. Surveying of how the field of forensic psychiatry and the forensic psychiatric expert work can be made more attractive. We performed an online survey among forensic psychiatric experts and specialists in psychiatry. We complied a questionnaire that beside demographic data contained questions in 4 areas. The first 2 areas of the questionnaire - simplifying the forensic psychiatry training (10 items), decreasing the cost of the forensic psychiatry training (5 items) - consisted of simple tatements. Responders had to indicate on a 10-point Likert scale their level of agreement. In the last 2 areas, participants were asked to describe in unstructured format whether they see any circumstance that makes forensic psychiatry attractive or unattractive. The questionnaire was sent out in electronic form to the forensic psychiatric experts on the mailing list of the Hungarian Forensic Expert Chamber, and all specialists in psychiatry on the mailing list of the Hungarian Medical Chamber. Altogether 171 persons filled in the questionnaire with a mean age of 57.26 ± 11.57 years. There were 122 (71.3%) females among the participants. The following proposals received the highest ratings from the forensic psychiatric experts as well as from the specialists in psychiatry: increasing the number of the training institutes; decreasing the costs of the training; making the Hungarian Forensic Expert Chamber course free of charge; and introducing a stipend to cover the costs of the training. Results of the survey indicate that active forensic psychiatric experts and specialists in psychiatry representing a potential professional resource, concur that forensic psychiatry training can be made more attractive mainly with increasing the number of training institutes and decreasing the total costs of the training which is compiled from different constituents. Due to the critical lack of necessary professionals, urgent action is needed to make forensic psychiatry more popular and the forensic psychiatry qualification easier to obtain. Orv Hetil. 2023; 164(35): 1373-1380.

An extension of the BioAssay Ontology to include pharmacokinetic/pharmacodynamic terminology for the enrichment of scientific workflows

With the capacity to produce and record data electronically, Scientific research and the data associated with it have grown at an unprecedented rate. However, despite a decent amount of data now existing in an electronic form, it is still common for scientific research to be recorded in an unstructured text format with inconsistent context (vocabularies) which vastly reduces the potential for direct intelligent analysis. Research has demonstrated that the use of semantic technologies such as ontologies to structure and enrich scientific data can greatly improve this potential. However, whilst there are many ontologies that can be used for this purpose, there is still a vast quantity of scientific terminology that does not have adequate semantic representation. A key area for expansion identified by the authors was the pharmacokinetic/pharmacodynamic (PK/PD) domain due to its high usage across many areas of Pharma. As such we have produced a set of these terms and other bioassay related terms to be incorporated into the BioAssay Ontology (BAO), which was identified as the most relevant ontology for this work. A number of use cases developed by experts in the field were used to demonstrate how these new ontology terms can be used, and to set the scene for the continuation of this work with a look to expanding this work out into further relevant domains. The work done in this paper was part of Phase 1 of the SEED project (Semantically Enriching electronic laboratory notebook (eLN) Data).

ONTOLOGY-DRIVEN MEANS FOR PROCESSING AND PRESENTATION OF LARGE ARRAYS OF UNSTRUCTURED TEXTS

The subject of the article's research is methods of ontology-driven processing and presentation of unstructured texts in a global environment. The goal of the work is to improve the efficiency of automatic search, classification and selection of the necessary information contained in electronic publications in an unstructured form by developing a model, method and means of automated processing and transdisciplinary presentation of text documents created in Ukrainian, Russian and English languages. In accordance with the goal, the following tasks were set: to conduct an overview of models of processing and presentation of unstructured texts, to identify the advantages of information systems with an ontology-driven architecture, to develop a conceptual model and means for automated processing and transdisciplinary presentation of text documents, improve the method of recursive reduction, with the help of developed method and means to process a large array of unstructured texts (in particular information base of knowledge of scientific products (IBK SP), educational programs, as well as educational and research projects of the National Center "Junior Academy of Sciences of Ukraine"), to present them in a form that allows to reveal the quality and completeness of the knowledge system available in the databases and, thus, experimentally to confirm the effectiveness of the developed means. The conducted research is based on the following methods: systemological classification, algebraic-logical and axiomatic methods, the method of recursive reduction. The following results were obtained: the existing models of processing and presentation of unstructured texts were described, the technological aspects of the ontology-driven approach to the processing and presentation of large arrays of network documents were improved, the technology of forming ontologies of documents based on the representation of their taxonomies, connections between their contexts and multiple representations was developed, which provides effective search for information in unstructured texts. Conclusions: The advantages of ontology-driven means for processing and presentation unstructured texts are substantiated. The method of recursive reduction by constructing a transformation, which leads to the predicative form of ontologically given descriptions of information, gained further development. The idea of effective search, processing, classification and selection of the necessary information has gained further development.

Leveraging pre-trained language models for mining microbiome-disease relationships

BackgroundThe growing recognition of the microbiome’s impact on human health and well-being has prompted extensive research into discovering the links between microbiome dysbiosis and disease (healthy) states. However, this valuable information is scattered in unstructured form within biomedical literature. The structured extraction and qualification of microbe-disease interactions are important. In parallel, recent advancements in deep-learning-based natural language processing algorithms have revolutionized language-related tasks such as ours. This study aims to leverage state-of-the-art deep-learning language models to extract microbe-disease relationships from biomedical literature.ResultsIn this study, we first evaluate multiple pre-trained large language models within a zero-shot or few-shot learning context. In this setting, the models performed poorly out of the box, emphasizing the need for domain-specific fine-tuning of these language models. Subsequently, we fine-tune multiple language models (specifically, GPT-3, BioGPT, BioMedLM, BERT, BioMegatron, PubMedBERT, BioClinicalBERT, and BioLinkBERT) using labeled training data and evaluate their performance. Our experimental results demonstrate the state-of-the-art performance of these fine-tuned models ( specifically GPT-3, BioMedLM, and BioLinkBERT), achieving an average F1 score, precision, and recall of over >0.8 compared to the previous best of 0.74.ConclusionOverall, this study establishes that pre-trained language models excel as transfer learners when fine-tuned with domain and problem-specific data, enabling them to achieve state-of-the-art results even with limited training data for extracting microbiome-disease interactions from scientific publications.

RegEMR: a natural language processing system to automatically identify premature ovarian decline from Chinese electronic medical records

BackgroundThe ovarian reserve is a reservoir for reproductive potential. In clinical practice, early detection and treatment of premature ovarian decline characterized by abnormal ovarian reserve tests is regarded as a critical measure to prevent infertility. However, the relevant data are typically stored in an unstructured format in a hospital’s electronic medical record (EMR) system, and their retrieval requires tedious manual abstraction by domain experts. Computational tools are therefore needed to reduce the workload.MethodsWe presented RegEMR, an artificial intelligence tool composed of a rule-based natural language processing (NLP) extractor and a knowledge-based disease scoring model, to automatize the screening procedure of premature ovarian decline using Chinese reproductive EMRs. We used regular expressions (REs) as a text mining method and explored whether REs automatically synthesized by the genetic programming-based online platform RegexGenerator + + could be as effective as manually formulated REs. We also investigated how the representativeness of the learning corpus affected the performance of machine-generated REs. Additionally, we translated the clinical diagnostic criteria into a programmable disease diagnostic model for disease scoring and risk stratification. Four hundred outpatient medical records were collected from a Chinese fertility center. Manual review served as the gold standard, and fivefold cross-validation was used for evaluation.ResultsThe overall F-score of manually built REs was 0.9444 (95% CI 0.9373 to 0.9515), with no significant difference (paired t test p > 0.05) compared with machine-generated REs that could be affected by training set sizes and annotation portions. The extractor performed effectively in automatically tracing the dynamic changes in hormone levels (F-score 0.9518–0.9884) and ultrasonographic measures (F-score 0.9472–0.9822). Applying the extracted information to the proposed diagnostic model, the program obtained an accuracy of 0.98 and a sensitivity of 0.93 in risk screening. For each specific disease, the automatic diagnosis in 76% of patients was consistent with that of the clinical diagnosis, and the kappa coefficient was 0.63.ConclusionA Chinese NLP system named RegEMR was developed to automatically identify high risk of early ovarian aging and diagnose related diseases from Chinese reproductive EMRs. We hope that this system can aid EMR-based data collection and clinical decision support in fertility centers.

Artificial Intelligence-Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study.

The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources. Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA. We developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype's features and functionalities. Using the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users' individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries. The novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.

Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data.

The past decade has seen a number of impressive developments in predictive chemistry and reaction informatics driven by machine learning applications to computer-aided synthesis planning. While many of these developments have been made even with relatively small, bespoke data sets, in order to advance the role of AI in the field at scale, there must be significant improvements in the reporting of reaction data. Currently, the majority of publicly available data is reported in an unstructured format and heavily imbalanced toward high-yielding reactions, which influences the types of models that can be successfully trained. In this Perspective, we analyze several data curation and sharing initiatives that have seen success in chemistry and molecular biology. We discuss several factors that have contributed to their success and how we can take lessons from these case studies and apply them to reaction data. Finally, we spotlight the Open Reaction Database and summarize key actions the community can take toward making reaction data more findable, accessible, interoperable, and reusable (FAIR), including the use of mandates from funding agencies and publishers.

Preparing an Abstract for Scientific Conferences and Publications: A Beginner's Guide

AbstractAn abstract is a window to a research work. It describes the work in 250 to 500 words in either a structured or an unstructured format. The aim, methods, results, and conclusion are the core components. The integral aspect of writing an abstract is summarizing the entire work in limited words that not only gives away important data but also gives a clear message to its readers. In the fast-rising world of publications, abstracts are often the decisive factor in initial editorial decisions of acceptance or rejection. Good abstracts may improve citations of a work because these are often the only freely available part of the article. Graphical abstracts are becoming an integral part of submission requirements in most journals and tend to depict content figuratively. Although a book can never be judged by its cover, a well-written abstract will always draw attention to the work of the author. This article will act as guidance for writing a good abstract and familiarize readers with all components of the abstract.

A Study on the Situations Determining the Classroom Decisions of Preservice Teachers and the Criteria for Evaluating Their Decision

This study aims to investigate the situations that determine the classroom decisions of preservice teachers and the criteria they use to evaluate their decisions during the teaching practice process. The study utilized a basic qualitative research model and employed the typical case sampling method to select the study group, consisting of ten pre-service teachers in their final year of primary education program. Data collection involved the use of reflective evaluation forms and unstructured interview forms. The pre-service teachers completed reflective evaluation forms after each implementation process for a ten-week period, and unstructured interviews based on the reflective evaluation forms were conducted with the participants in pairs every week. The analysis revealed that the situations of the candidates' classroom decisions can be analyzed under five themes: self-efficacy, students, environment, classroom management, and teaching. Furthermore, the criteria used by the candidates to evaluate their decisions can be analyzed under five themes: content knowledge, classroom management skills, student recognition, teaching-learning process, and monitoring and evaluation of learning. The findings suggest that teacher education programs should provide support to candidates to make effective decisions under the situations that are decisive on classroom decisions. Additionally, it is important to emphasize that candidates evaluate the decisions they make in the classroom on the basis of criteria that cover all components of the teaching-learning process.