Latent Semantic Analysis Research Articles

Enhancing integration testing efficiency through AI-driven combined structural and textual class coupling metric

Integration testing, a critical and resource-intensive phase in the software development lifecycle, can account for up to a high percentage of the total testing cost. Identifying classes with high coupling is crucial for efficient integration testing, as these classes are more susceptible to the impact of maintenance-related changes. This research introduces a novel metric called <i>combined structural and textual class coupling</i> (CSTCC), which harnesses the power of artificial intelligence (AI) techniques to predict and rank the most critical classes in an object-oriented software system. CSTCC integrates structural coupling metrics with <i>latent semantic indexing</i> (LSI)-based textual coupling, providing a comprehensive measure of class coupling. LSI, an information retrieval technique, analyses the semantic relationships between classes based on their textual content, enabling CSTCC to capture both structural and conceptual dependencies, resulting in a more accurate identification of high-risk classes. The effectiveness of the proposed approach is rigorously evaluated using mutation testing on four Java open-source projects, and the results demonstrate that test cases developed based on CSTCC achieve high mutation scores, indicating their ability to detect a significant percentage of integration faults. By focusing testing efforts on high-coupling classes identified by CSTCC, developers can potentially save time and cost during integration testing. The results demonstrate that test cases developed based on CSTCC achieve high mutation scores, ranging from 98% to 100%, indicating their ability to detect a significant percentage of integration faults. Additionally, the approach results in substantial efficiency gains, with a notable reduction in the number of test cases needed, saving up to 33.3% of the testing effort in some cases. By focusing testing efforts on high-coupling classes identified by CSTCC, developers can potentially save time and cost during integration testing. The CSTCC metric provides a novel and effective approach to prioritize testing resources and improve the efficiency of integration testing in object-oriented software systems.

An Efficient Archerfish Hunting Optimizer (AHO)-Based Power Quality Improvement in Distribution System Connected UPQC

These days, because power quality (PQ) issues are costing industrial customers a lot of money, both electrical utilities and end-users are paying a lot of attention to them. The most significant and frequent PQ concerns for sensitive loads are voltage sag and swell. This manuscript proposes an optimization technique for enhancing the control system of a unified power quality conditioner (UPQC) connected solar photovoltaic system in the distributed generation system. The proposed strategy is the archerfish hunting optimizer (AHO). The objective of the AHO approach is to optimizing UPQC operation in distribution systems, focusing on targeted power quality enhancement. The optimal control pulses are produced by the series active power filter and the shunt active power filter, depending on the load-side and source circumstances. During the load variation states the AHO technique manages the power loss, total harmonic distortion (THD), and voltage instability issue respectively. The performance of the AHO methodology is carried out on the MATLAB platform and compared with existing methods, like particle swarm optimization (PSO), latent semantic analysis (LSA), and ant-lion optimizer (ALO). The existing methods THD are 6.35%, 7.85%, and 9.19%. The proposed method of THD is 4.22%. From the outcome, it concluded that the proposed method based on the THD is low contracted to present techniques.

Analysis Sentiment & Topic Modelling using Latent Semantic Indexing

The proliferation of digital news platforms has transformed information consumption, making user experience (UX) a key success factor for Detik.com, a leading news application in Indonesia. This research aims to explore the relationship between UX and news through sentiment analysis and topic modelling. The methods include web scraping for data collection, descriptive analysis for sentiment, and Latent Semantic Indexing (LSI) for topic modelling. Out of 14,320 reviews analyzed, 45.6% were positive, focusing on the speed of updates and news quality. Conversely, 41.18% of reviews were negative, highlighting disruptive ads and login issues, while 13.76% were neutral. LSI identified eight main topics, including user experience and ad complaints. Visualizations through heatmaps, distance maps, and word clouds emphasized terms like "news" and "ads." This research recommends that Detik.com developers reduce ad disruptions, fix technical problems, and understand frequently discussed topics to enhance user satisfaction.

Def2Vec: you shall know a word by its definition

AbstractDef2Vec introduces a new perspective on building words embeddings by using dictionary definitions. By leveraging term-document matrices derived from dictionary definitions and employing Latent Semantic Analysis (LSA), our method, Def2Vec, yields embeddings characterized by robust performance and adaptability. Through comprehensive evaluations encompassing token classification, sequence classification and semantic similarity, we show empirically how Def2Vec consistently demonstrates competitiveness with established models like Word2Vec, GloVe, and FastText. Notably, our model’s utilization of all the matrices resulting from LSA factorisation facilitates efficient prediction of embeddings for out-of-vocabulary words, given their definition. By effectively integrating the benefits of dictionary definitions with LSA-based embeddings, Def2Vec builds informative semantic representations, all while minimizing data requirements. In this paper, we run different experiment to assess the quality of our embedding model at word level and at sequence level. Our findings contribute to the ongoing evolution of word embedding methodologies by incorporating structured lexical information and enabling efficient embedding prediction.

Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

BackgroundSocial and behavioral determinants of health (SBDH) are associated with a variety of health and utilization outcomes, yet these factors are not routinely documented in the structured fields of electronic health records (EHR). The objective of this study was to evaluate different machine learning approaches for detection of SBDH from the unstructured clinical notes in the EHR.MethodsLatent Semantic Indexing (LSI) was applied to 2,083,180 clinical notes corresponding to 46,146 patients in the MIMIC-III dataset. Using LSI, patients were ranked based on conceptual relevance to a set of keywords (lexicons) pertaining to 15 different SBDH categories. For Generative Pretrained Transformer (GPT) models, API requests were made with a Python script to connect to the OpenAI services in Azure, using gpt-3.5-turbo-1106 and gpt-4-1106-preview models. Prediction of SBDH categories were performed using a logistic regression model that included age, gender, race and SBDH ICD-9 codes.ResultsLSI retrieved patients according to 15 SBDH domains, with an overall average PPV ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document} 83%. Using manually curated gold standard (GS) sets for nine SBDH categories, the macro-F1 score of LSI (0.74) was better than ICD-9 (0.71) and GPT-3.5 (0.54), but lower than GPT-4 (0.80). Due to document size limitations, only a subset of the GS cases could be processed by GPT-3.5 (55.8%) and GPT-4 (94.2%), compared to LSI (100%). Using common GS subsets for nine different SBDH categories, the macro-F1 of ICD-9 combined with either LSI (mean 0.88, 95% CI 0.82-0.93), GPT-3.5 (0.86, 0.82-0.91) or GPT-4 (0.88, 0.83-0.94) was not significantly different. After including age, gender, race and ICD-9 in a logistic regression model, the AUC for prediction of six out of the nine SBDH categories was higher for LSI compared to GPT-4.0.ConclusionsThese results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems.

Topic selection for text classification using ensemble topic modeling with grouping, scoring, and modeling approach

TextNetTopics (Yousef et al. in Front Genet 13:893378, 2022. https://doi.org/10.3389/fgene.2022.893378) is a recently developed approach that performs text classification-based topics (a topic is a group of terms or words) extracted from a Latent Dirichlet Allocation topic modeling as features rather than individual words. Following this approach enables TextNetTopics to fulfill dimensionality reduction while preserving and embedding more thematic and semantic information into the text document representations. In this article, we introduced a novel approach, the Ensemble Topic Model for Topic Selection (ENTM-TS), an advancement of TextNetTopics. ENTM-TS integrates multiple topic models using the Grouping, Scoring, and Modeling approach, thereby mitigating the performance variability introduced by employing individual topic modeling methods within TextNetTopics. Additionally, we performed a thorough comparative study to evaluate TextNetTopics’ performance using eleven state-of-the-art topic modeling algorithms. We used the extracted topics for each as input to the G component in the TextNetTopics tool to select the most compelling topic model regarding their predictive behavior for text classification. We conducted our comprehensive evaluation utilizing the Drug-Induced Liver Injury textual dataset from the CAMDA community and the WOS-5736 dataset. The experimental results show that the Latent Semantic Indexing provides comparable performance measures with fewer discriminative features when compared with other topic modeling methods. Moreover, our evaluation reveals that the performance of ENTM-TS surpasses or aligns with the optimal outcomes obtained from individual topic models across the two datasets, establishing it as a robust and effective enhancement in text classification tasks.

An embedded computational framework of memory: Accounting for the influence of semantic information in verbal short-term memory

We introduce the Embedded Computational Framework of Memory (eCFM), a model that integrates structured semantic word representations with an instance-based memory model to account for the influence of semantic information in verbal short-term memory. The eCFM combines principles from the episodic MINERVA 2 model and the semantic Latent Semantic Analysis model. After reviewing how semantic information impacts verbal short-term memory performance, we demonstrate eCFM’s ability to reconcile various phenomena within a common computational framework. Our model captures key findings, such as the influence of semantic information in serial recall, its reduction in serial reconstruction, and the impact of task difficulty on semantic information. In five experiments, we tested predictions derived from the eCFM. Experiments 1 and 2 manipulated list organization, with Experiment 1 using alternating lists of related or unrelated words and Experiment 2 using blocked lists. Experiment 3 varied presentation rates, Experiment 4 revisited the detrimental effect of semantic information on order information, and Experiment 5 explored false recall. We found that semantic information interacts with list composition, presentation rate affects the magnitude of its influence, and semantic information impacts order information contrary to the dominant view. Additionally, increasing the number of related study words to a non-studied semantic lure boosts false recall. The eCFM captured these findings as well as memory at the item level. Our demonstration provides insight into the cognitive mechanisms underlying verbal short-term memory and the interplay of semantic and episodic memory processes in recall.

Comparison of Latent Semantic Analysis (LSA) and Doc2Vec Algorithms of Thesis Similarity Detection

This study aims to develop a system for detecting similarities in thesis titles and content to prevent plagiarism and support student originality. The high level of similarity in final projects is a significant concern in academic environments. Two text vectorization methods, Latent Semantic Analysis (LSA) and Doc2Vec, were compared to measure document similarity. Results showed that LSA achieved a very high cosine similarity (99.94%) due to dimensionality reduction that preserved semantic correlations. In contrast, Doc2Vec produced lower similarity scores, with 7.17% for PV-DM and 39.07% for PV-DBOW, indicating richer text representations. This study adopted the CRISP-DM model, which includes Business Understanding, Data Understanding, Data Preparation, Modelling, and Evaluation. The model is expected to strengthen academic integrity and encourage valuable scientific contributions.

Evaluating the Effectiveness of the LexRank and LSA Algorithm in Automatic Text Summarization for Indonesian Language

The aim of this study is to evaluate how effective the Lexrank algorithm and Latent semantic analysis (LSA) are in automatic text summarization for the Indonesian language. This research focuses on natural language processing and handling of excessive data. We applied both algorithms to generate text summaries using the INDOSUM dataset, which contains about 20,000 news articles in Indonesian with manual summaries. To assess performance, the ROUGE metric was used, which includes aspects of precision, recall, and F1 score. In all tested metrics, LSA outperformed Lexrank. LSA had a precision of 0.57, recall of 0.67, and an F1 score of 0.59, whereas Lexrank had a precision of 0.46, recall of 0.52, and an F1 score of 0.48. These result indicate that LSA is better at gathering important information from the original text than Lexrank.

Identify Issues That Influence Customer Satisfaction Using Sentiment Analysis and Topic Modeling

The development of internet users in Indonesia continues to increase over time, this makes customers comfortable with digital transactions. Trinusa Travelindo Company or better known as Traveloka, which is engaged in the online travel agent sector, has proven its extraordinary achievement, namely becoming the E-tourism application that is ranked 1st most visited by the Indonesian people in 2022. However, these results are also directly proportional to the number of negative reviews created in the application review itself. This study aims to identify what issues affect customer satisfaction with topic modelling. This study uses the text mining method derived from the results of Traveloka application user reviews. The data source obtained in this study is secondary data by using the Traveloka application review data crawling technique with samples from January to May. Based on sentiment analysis, positive sentiment is more dominant (50.38%) compared to negative sentiment (49.62%). User reviews are grouped based on the results of sentiment analysis, then Topic Modelling is carried out to find out what issues affect customer satisfaction. The topic modelling of the data used is Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) with the help of phyton 6.3. to find out what words and topics often appear or as factors that influence customer satisfaction. The most influential negative issues include the refund and payment process, pay later services, and the ordering experience which is considered complicated. On the other hand, issues that contribute positively to customer satisfaction include product quality, service quality, and promos and discounts. This is reinforced by the results of topic modelling which show that these aspects are often the main concerns in user reviews on the Google Play store. Thus, this study suggests that in order to increase customer satisfaction, Traveloka needs to focus on improving the quality of service, products, and offering more attractive promos, while simplifying the refund process and ordering experience in the application, some still say it is complicated.

Medical device similarity analysis: a promising approach to medical device equivalence regulation

ABSTRACT Background This study aims to facilitate the identification of similar devices for both, the European Medical Device Regulation (MDR) and the US 510(k) equivalence pathway by leveraging existing data. Both are related to the regulatory pathway of read across for chemicals, where toxicological data from a known substance is transferred to one under investigation, as they aim to streamline the accreditation process for new devices and chemicals. Research design and methods This study employs latent semantic analysis to generate similarity values, harnessing the US Food and Drug Administration 510k-database, utilizing their ‘Device Descriptions’ and ‘Intended Use’ statements. Results For the representative inhaler cluster, similarity values up to 0.999 were generated for devices within a 510(k)-predicate tree, whereas values up to 0.124 were gathered for devices outside this group. Conclusion Traditionally, MDR equivalence involves manual review of many devices, which is laborious. However, our results suggest that the automated calculation of similarity coefficients streamlines this process, thus reducing regulatory effort, which can be beneficial for patients needing medical devices. Although this study is focused on the European perspective, it can find application within 510(k) equivalence regulation. The conceptual approach is reminiscent of chemical fingerprint similarity analysis employed in read-across.

Latent Relations at Steady-state with Associative Nets.

Models of word meaning that exploit patterns of word usage across large text corpora to capture semantic relations, like the topic model and word2vec, condense word-by-context co-occurrence statistics to induce representations that organize words along semantically relevant dimensions (e.g., synonymy, antonymy, hyponymy, etc.). However, their reliance on latent representations leaves them vulnerable to interference, makes them slow learners, and commits to a dual-systems account of episodic and semantic memory. We show how it is possible to construct the meaning of words online during retrieval to avoid these limitations. We implement a spreading activation account of word meaning in an associative net, a one-layer highly recurrent network of associations, called a Dynamic-Eigen-Net, that we developed to address the limitations of earlier variants of associative nets when scaling up to deal with unstructured input domains like natural language text. We show that spreading activation using a one-hot coded Dynamic-Eigen-Net outperforms the topic model and reaches similar levels of performance as word2vec when predicting human free associations and word similarity ratings. Latent Semantic Analysis vectors reached similar levels of performance when constructed by applying dimensionality reduction to the Shifted Positive Pointwise Mutual Information but showed poorer predictability for free associations when using an entropy-based normalization. An analysis of the rate at which the Dynamic-Eigen-Net reaches asymptotic performance shows that it learns faster than word2vec. We argue in favor of the Dynamic-Eigen-Net as a fast learner, with a single-store, that is not subject to catastrophic interference. We present it as an alternative to instance models when delegating the induction of latent relationships to process assumptions instead of assumptions about representation.

A Computing System for Complex Cases of Major Recurrent Depression Based on Latent Semantic Analysis: Relationship between Life Themes and Symptoms

The paper presents a computing procedure with the goal of suggesting applicable solutions to improve complex cases of major recurrent depression. The focus is on identifying the patients’ illness patterns and on finding solutions for alleviating problematic symptoms. The illness patterns synthesize the outcomes of the relationship between the patients’ life themes and symptoms. The testing of the effectiveness of illness improvement solutions was conducted by evaluating and comparing the Beck scores of patients after each psychotherapy session. In addition to latent semantic analysis used to identify semantic relationships between life themes and symptoms, the research also employed the correlation method to find life themes/symptoms that are experienced undistortedly and associations between life themes that amplify latent symptoms. The computing system was applied to eleven patients with severe forms of depression and their progress was monitored for six months. The results obtained following the application of the computing system demonstrated its ability to describe personalized illness patterns and to significantly improve, through its suggestions, the illness of all patients. These findings recommend the use of the computing system in severe cases of major recurrent depression.

Explainable paper classification system using topic modeling and SHAP

The exponential growth of academic papers necessitates sophisticated classification systems to effectively manage and navigate vast information repositories. Despite the proliferation of such systems, traditional approaches often rely on embeddings that do not allow for easy interpretation of classification decisions, creating a gap in transparency and understanding. To address these challenges, we propose an innovative explainable paper classification system that combines Latent Semantic Analysis (LSA) for topic modeling with explainable artificial intelligence (XAI) techniques. Our objective is to identify which topics significantly influence the classification outcomes, incorporating Shapley additive explanations (SHAP) as a key XAI technique. Our system extracts topic assignments and word assignments from paper abstracts using latent semantic analysis (LSA) topic modeling. Topic assignments are then employed as embeddings in a multilayer perceptron (MLP) classification model, with the word assignments further utilized alongside SHAP for interpreting the classification results at the corpus, document, and word levels, enhancing interpretability and providing a clear rationale for each classification decision. We applied our model to a dataset from the Web of Science, specifically focusing on the field of nanomaterials. Our model demonstrates superior classification performance compared to several baseline models. Ultimately, our proposed model offers a significant advancement in both the performance and explainability of the system, validated by case studies that illustrate its effectiveness in real-world applications.

Optimized automated testing: test case generation and maintenance using latent semantic analysis-based TextRank and particle swarm optimization algorithms

Software development would have to include automated testing to ensure the finished product and performs as intended. However, the process of Test Case Generation and Maintenance can be time-consuming and error-prone, especially when manual methods are used. This research proposes a new approach to improve the efficiency and accuracy of automated testing using latent semantic analysis (LSA)-based TextRank (TR) and particle swarm optimization (PSO) algorithms. The study aims to evaluate the effectiveness of these algorithms in generating and optimizing test cases based on requirements analysis. To retrieve key information from the criteria, methods including text classification (TC), named entity recognition (NER), and sentiment analysis (SA) are used to evaluate the text. Test cases are then generated using LSA-based TR for text summarization and PSO for optimization. The aim of this work is to identify any limitations that need to be addressed and to evaluate the overall efficiency and accuracy of automated testing (AT) using proposed algorithms. The results of this research are expected to have important implications for the software industry, helps to improve the overall efficiency and accuracy of AT. The findings could guide future research that led to the creation of more advanced and effective tools for AT.

FedExLSA

Every government in the world has multiple departments that must function and operate to address the various inquiries raised by the population. The government's diverse range of websites offers citizens a platform to submit inquiries, thereby facilitating the fulfilling of their requirements. Comprehending the subjects addressed in People Query is essential for government services. Unstructured query data is analyzed using extracting information from text techniques such as allocation of Latent diffuser (LDA) and analysis of hidden semantics (LSA). LSA outperforms other methods in terms of performance because of its minimal complexity and quick installation process. Research on decentralized learning techniques for natural language processing (NLP) is necessary due to concerns about limited data availability and privacy. Federated learning (FL) employs methods that enable different users to collectively train an integrated broad model while maintaining their information regionally stored and accessible. Nevertheless, the current body of literature lacks a thorough examination and evaluation of FL techniques. Data federation is an approach to data integration that allows the government to access and query data from multiple diverse sources as if they were a single, unified repository. Functioning as a form of data virtualization, it facilitates the creation of a comprehensive representation of data, thereby enhancing operational efficiency and the accuracy of decision-making. FedEx utilizes Federated Learning to apply topic modelling techniques to common NLP tasks. The proposed structure integrates the FL Methodology with Latent Semantic Analysis to deliver outcomes for intelligent data analysis and management.

Ensemble-based Disease Outbreak Detection: Comparative Analysis of Health News Information Retrieval Techniques

In India, Kerala is the first state to report a COVID-19 infection case, in January 2020, in a medical student, who returned from Wuhan, China. More recently, in June 2022, Kerala also reported India's first case of monkeypox disease. News websites often publish articles dedicated to reporting disease occurrences and live updates of outbreaks. Through the utilization of data gathered from online digital resources, early detection of outbreaks is possible, and this potential is already identified by the research community. As webpages give a comprehensive collection of reports covering a wide range of themes through hyperlinks, precisely categorizing news articles based on their headlines and retrieving health news is a tedious operation. Hence, this paper proposes a novel and efficient news retrieval technique grounded on an ML-based classification method with an ensemble learning approach to identify reports of disease occurrences from web pages by focusing specifically on the health context of Kerala and a comparison with baseline methods for information retrieval such as keyword-based, phrase-based, and content-based latent semantic analysis method is made.

Markov Chains for User Queries Mining: Image Retrieval Application using Internet

Abstract: We suggest a brand-new technique for automatically annotating, indexing, and retrieving photos based on annotations. We introduce the novel technique, which we term Markovian Semantic Indexing (MSI), within the framework of an image retrieval system in online mode. In the event that such a system exists, the queries entered by the users are utilised to build an Aggregate Markov Chain (AMC), which defines the significance between the terms the system sees. The photographs are automatically annotated based on the queries entered by the users. Next, depending on the annotation of each image and the keyword relevance recorded in the AMC, a stochastic distance between them is presented. The suggested distance is given geometric meanings, and its relationship to a grouping in the keyword space is examined. The optimality qualities of the suggested distance are demonstrated by the use of a novel Markovian state similarity measure, the mean first cross passage time (CPT). Images are represented as points in a vector space, and MSI is used to calculate how similar two images are. In Annotation-Based Image Retrieval (ABIR) tasks, it is demonstrated that the novel method outperforms Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in terms of Precision against Recall and has some theoretical advantages.

(Dis)similarities between non-symbolic and symbolic number representations: Insights from vector space models

Empirical evidence in support of a shared system for non-symbolic and symbolic number processing has been inconclusive. The current study aims to address this question in a novel way, specifically by testing whether the efficient coding principle based on co-occurrence of number symbols in natural language holds for both non-symbolic and symbolic number processing. The efficient coding principle postulates that perception is optimized when stimuli frequently co-occur in a natural environment. We hypothesized that both numerical ratios and co-occurrence frequencies of symbolic numbers would significantly influence participants' performance on a non-symbolic and symbolic number comparison task. To test this hypothesis, we employed latent semantic analysis on a TASA corpus to quantify number co-occurrence in natural language and calculate language similarity estimates. We engaged 73 native English speakers (mean age = 19.36, standard deviation = 1.83) with normal or corrected vision and no learning disorders in a number comparison task involving non-symbolic (dot arrays) and symbolic stimuli (Arabic numerals and English number words). Results showed that numerical ratios significantly predicted participants' performances across all number formats (ps < 0.001). Language similarity estimates derived from everyday language also predicted performance on the non-symbolic task and the symbolic task involving number words (ps < 0.007). Our results highlight the complex nature of numerical processing, pointing to the co-occurrence of number symbols in natural language as an auxiliary factor in understanding the shared characteristics between non-symbolic and symbolic number representations. Given that our study focused on a limited number range (5 to 16) and a specific task type, future studies should explore a wider range of tasks and numbers to further test the role of the efficient coding principle in number processing.

Use of Real-World Data and Machine Learning to Screen for Maternal and Paternal Characteristics Associated with Cardiac Malformations.

Effective prevention of cardiac malformations, a leading cause of infant morbidity, is constrained by limited understanding of etiology. The study objective was to screen for associations between maternal and paternal characteristics and cardiac malformations. We selected 720,381 pregnancies linked to live-born infants (n=9,076 cardiac malformations) in 2011-2021 MarketScan US insurance claims data. Odds ratios were estimated with clinical diagnostic and medication codes using logistic regression. Screening of 2,000 associations selected 81 associated codes at the 5% false discovery rate. Grouping of selected codes, using latent semantic analysis and the Apriori-SD algorithm, identified elevated risk with known risk factors, including maternal diabetes and chronic hypertension. Less recognized potential signals included maternal fingolimod or azathioprine use. Signals identified might be explained by confounding, measurement error, and selection bias and warrant further investigation. The screening methods employed identified known risk factors, suggesting potential utility for identifying novel risk factors for other pregnancy outcomes.