INSTACART - AN EXPLORATORY DATA ANALYSIS (EDA) FOR DATA SCIENTISTS USING ONLINE GROCERY SHOPPING DATASET

Computing nodes in Edge Computing environments share unlimited data. Such data are exploited to locally build Machine Learning (ML) models for applications such as predictive analytics, exploratory analysis, and smart applications. This edge node-centric local learning reduces the need for data transfer and centralization, which is affected by different factors such as data privacy, data size, communication overhead, and computing resource limitations. Therefore, a collaborative learning fashion at the network edge has appeared as a promising paradigm that enables multiple distributed (edge) nodes to train and deploy ML models cooperatively without infringement of data privacy. Nevertheless, the variety, distribution and quality of data vary between edge nodes. Hence, selecting unsuitable edge nodes can have a negative impact on the ML model performances. We have devised (i) an intelligent node selection mechanism per analytics query based on the range of the availability of required training data at the edge and (ii) variants of collaborative learning processes engaging the most suitable nodes for models training and inference. We evaluate the efficiency of our selection mechanism and collaborative learning and provide a comparative assessment with other methods found in the literature using real data. The results showcase that our mechanism significantly outperforms baseline approaches and existing node selection mechanisms in distributed computing environments.

While the rapid development of vaccines during the COVID-19 pandemic helped save billions of lives, a significant percentage of the population reported adverse reactions after vaccination. Post-vaccination surveys were conducted to understand the possible side effects. Analyzing and understanding the side effects enables the relevant stakeholders to gain more understanding of the vaccine and allows the individual to make informed decisions about whether to receive the vaccine. This work aims to identify a robust approach that can be used to predict the possibility of potential adverse symptoms. A dataset comprising 840 participants with 15 Post-Vaccination Symptoms was considered for the study. Synthetic Minority Oversampling TEchnique (SMOTE) was used to handle the class imbalance of the dataset. A combination of SMOTE and ensemble machine learning models was used to predict the adverse reactions to COVID-19 vaccines. The ensemble Machine Learning (ML) models that are considered for this study are Random Forest, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and AdaBoost. The metrics accuracy, precision, recall and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) were used to measure the performance of the models. The dataset was pre-processed to handle missing values and one-hot encoding was applied to convert categorical variables into the numerical format. Insights into the data distribution and relationships between variables were gained through exploratory data and correlation analysis, respectively. Class imbalance in the target variables was addressed using SMOTE, resulting in a significantly improved F1-score and ROC-AUC score. Among the ensemble ML models, XGBoost delivered the best performance metrics. A combined performance score was calculated by averaging the F1-score and accuracy to identify the best model. XGBoost obtained the highest performance score among the ensemble ML models, and its performance is further enhanced by performing the threshold adjustment using the maximum F1- score strategy. The findings suggest that the combination of SMOTE and ensemble learning models with threshold adjustment provides a more efficient prediction of adverse effects after COVID-19 vaccination, aiding in healthcare decision-making.

Background: Despite advances in pharmacologic management, achieving sustained remission in rheumatoid arthritis (RA) remains challenging, particularly among patients who exhibit suboptimal responses to conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) such as methotrexate (MTX). The emergence of targeted synthetic disease-modifying antirheumatic drugs (tsDMARDs), including Janus kinase (JAK) inhibitors, has expanded therapeutic options by directly modulating intracellular signaling pathways central to inflammation and immune activation. Among these, tofacitinib has demonstrated efficacy in clinical trials, yet its real-world performance, safety profile, and predictors of treatment response are less clearly defined. Real-world data, reflecting diverse patient populations and routine clinical practice, are essential for complementing randomized controlled trials (RCTs) and for guiding evidence-based, individualized treatment strategies in RA management.Methods: This retrospective real-world study included 450 RA patients treated with tofacitinib following MTX failure. Demographics, clinical characteristics, laboratory results, treatment history, and comorbidities were assessed. Outcomes included changes in disease activity score 28 (DAS28), Health Assessment Questionnaire (HAQ), pain, and stiffness at six months, along with adverse event monitoring. Exploratory data analysis and machine learning models (logistic regression, random forest, XGBoost, LightGBM, and support vector machine {SVM}) were applied to predict treatment response.Results: The mean age was 51.2±12.4 years, with 236 females (52.4%). At six months, DAS28 decreased significantly (5.4±1.1-3.2±1.0; p<0.001), with 286 patients (63.6%) achieving low disease activity and 142 patients (31.6%) reaching remission. HAQ improved from 1.5±0.6 to 0.9±0.5 (p<0.001). Adverse events occurred in 132 patients (29.3%), mostly mild infections. Machine learning models identified CRP, disease duration, and baseline DAS28 as key predictors; however, the predictive performance for treatment response was generally limited, with most models showing area under the curve (AUC) values below 0.65.Conclusion: Tofacitinib demonstrated significant clinical benefit in RA patients with inadequate MTX response, with acceptable safety. While machine learning highlighted key predictors, future work with larger datasets is needed to optimize predictive accuracy and personalize therapy.

Human-computer interaction (HCI) investigators must consider the sequential nature of interaction and must often weigh behavioral, cognitive, and social factors when studying and designing today's increasingly complex systems. In many cases, laboratory experimentation is inappropriate and formal modeling intractable; instead, observational data analysis is frequently the only appropriate empirical approach. Diverse approaches to observational data analysis already exist, which we synthesize as instances of exploratory sequential data analysis (ESDA). In this article, we outline fundamental ESDA characteristics that might help HCI investigators using sequential data make better conceptual and methodological choices. ESDA owes a philosophical debt to exploratory data analysis but focuses on exploring sequential data. Important issues for ESDA are finding an appropriate temporal band for analysis, finding an effective semantics for encoding, and completing an analysis in an acceptable time frame. We survey temporal factors and introduce analysis time: sequence time ratios, which describe the time cost of conducting different types of ESDA. We also introduce the "Eight Cs"-different general transformations that can be performed on sequential data. We conjecture that the Eight Cs, and their combinations, are critical for supporting scientific inference in ESDA. Distinctions are made among three principal ESDA traditions that are relevant for HCI-behavioral, cognitive, and social. We indicate how each ESDA tradition has been used in HCI and describe one technique from each tradition. Last, we outline major practical problems for investigators using observational data and, following our framework, suggest ways such problems might be overcome.

The modern economy increasingly relies on ex- ploratory data analysis. Much of this is dependent on data scientists - expert statisticians who process data using statistical tools and programming languages. Our goal is to offer some of this analytical power to end-users who have no statistical training through simple interaction techniques and metaphors. We describe a spreadsheet-based interaction technique that can be used to build and apply sophisticated statistical models such as neural networks, decision trees, support vector machines and linear regression. We present the results of an experiment demon- strating that our prototype can be understood and successfully applied by users having no professional training in statistics or computing, and that the experience of interacting with the system leads them to acquire some understanding of the concepts underlying exploratory statistical modelling. I. INTRODUCTION There are many situations in which end-users need to per- form simple exploratory and interactive data analysis. Typical examples include the analysis of trends in historical data in order to predict future values, estimation of missing data from a data set, or sanity checking of new data by comparison to known information. In this paper, we present a novel tool that supports these kinds of operations within a spreadsheet-like interaction paradigm. Formally, the problem being addressed is mixed-initiative statistical inference or machine learning. Here, the goal is to construct a model that characterises a multivariate training data set, and then apply that model to a test data set to estimate missing values or gauge the correctness of preexisting values. Specialised software packages such as scikit-learn (5), programming languages such as R (6), and deep domain expertise are typically needed in order to build and apply such models. In contrast, our tool only requires the user to be familiar with simple spreadsheet manipulation operations. II. OUR INTERACTION TECHNIQUE

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Systematic Review of Machine Learning in Diagnosis of Myeloproliferative Neoplasia

A common task in data mining is the visualization of multivariate objects on scatterplots, allowing human observers to perceive subtle inter-relations in the dataset such as outliers, groupings or other regularities. Least- squares multidimensional scaling (MDS) is a well known Exploratory Data Analysis family of techniques that produce dissimilarity or distance preserving layouts in a nonlinear way. In this framework, the issue of visualizing large multidimensional datasets through MDS-based methods is addressed. An original scheme providing very accurate layouts of large datasets is introduced. It is a compromise between the computational complexity O(N5/2) and the accuracy of the solution that makes it suitable both for visualization of fairly large datasets and preprocessing in pattern recognition tasks.

Improving autocoding performance of rare categories in injury classification: Is more training data or filtering the solution?

Study question Can a machine learning (ML) model, developed using modern neural network architecture produce comparable annotation data; utilisable for algorithmic outcome prediction, to manual time-lapse annotations? Summary answer The model automatically annotated unseen embryos with comparable results to manual methods, generating morphokinetic data to enable comparably predictive outputs from an embryo selection algorithm. What is known already The application of artificial intelligence across healthcare industries, including fertility, is increasing. Several ML models are available that seek to generate or analyse embryo images and morphokinetic data, and to determine embryo viability potential. Along with photographic images, the use of time-lapse in IVF laboratories has amassed numeric data, resulting predominantly from annotated manual assessment of images over time. Embryo annotation practice is variable in quality, can be subjective and is time-consuming; commonly taking several minutes per embryo. The development of rapid, accurate automatic annotation would represent a significant time-saving as well as an increase in reproducibility and accuracy. Study design, size, duration Multicentre quality assured annotation data from 63,383 time-lapse monitored embryos (EmbryoScope®), comprising over 400 million individual images, were used to train a ML model to automatically generate morphokinetic annotations. Data was derived from 8 UK clinics within a cohesive group between 2012-2021. Accuracy was assessed using 900 unseen embryos (with live birth outcome) by comparing the output of an established in-house, prospectively validated embryo selection model when the input was either ML-automated, or manual annotations. Participants/materials, setting, methods Multi-focal plane images were processed on the Azure cloud (Microsoft) and resampled to 300x300 pixels. A Laplacian-based focal stacking algorithm merged frames into a single image. The model consisted of an EfficientNetB4 Convolutional Neural Network classifier to extract features and classify the stage of embryo images. A Temporal Convolutional Network interpreted a time-series of image features; producing annotations from pronuclear fading through to blastocyst. Soft localisation loss function used QA data to integrate annotation subjectivities. Main results and the role of chance The ML model rapidly and automatically generated annotations. Efficacy and comparability of the ML model to automate reliable, utilisable annotations was demonstrated by comparison with manual annotation data and the ML model’s ability to auto-generate annotations which could be used to predict live birth by providing annotation data to an established, validated in house embryo selection model. Live birth-predictive capability was measured, and benchmarked against manual annotation, using the area under the receiver operating characteristic curve (AUC). When tested on time-lapse images, collected from pronuclear fading to full blastulation, representing 900 previously unseen, transferred blastocysts where live birth outcomes were blinded, the in-house developed auto-annotation ML model resulted in an AUC of 0.686 compared with 0.661 for manual annotations, for live birth prediction. Auto annotation using the developed model took only milliseconds to complete per embryo. The developed auto-annotation model, built and tested on large data, is considered suitable for productionisation with the aim of being validated and integrated into an application to support IVF laboratory practice. Limitations, reasons for caution Whilst this model was trained to recognise key morphokinetic events, there are other morphokinetic variables that may be useful in the prediction of live birth and further improve embryo selection, or deselection, ability. Akin to manual interpretation, some embryos may fail to be annotated or need second opinion. Wider implications of the findings There is increasing evidence supporting the application of ML to utilise big data from time-lapse imaging and fertility care generally. Whilst promising benefits to IVF clinics and patients, responsible use of data is required alongside large high-quality datasets, and rigorous validation, to ensure safe and robust applications. Trial registration number N/A

Discovering novel, multicomponent crystalline materials is a complex task owing to the large space of feasible structures. Here we demonstrate a method to significantly accelerate materials discovery by using a machine learning (ML) model trained on density functional theory (DFT) data from the Open Quantum Materials Database (OQMD). Our ML model predicts the stability of a material based on its crystal structure and chemical composition, and we illustrate the effectiveness of the method by application to finding new quaternary Heusler (QH) compounds. Our ML-based approach can find new stable materials at a rate 30 times faster than undirected searches and we use it to predict 55 previously unknown, stable QH compounds. We find the accuracy of our ML model is higher when trained using the diversity of crystal structures available in the OQMD than when training on well-curated datasets which contain only a single family of crystal structures (i.e., QHs). The advantage of using diverse training data shows how large datasets, such as OQMD, are particularly valuable for materials discovery and that we need not train separate ML models to predict each different family of crystal structures. Compared to other proposed ML approaches, we find that our method performs best for small $(l{10}^{3})$ and large $(g{10}^{5})$ training set sizes. The excellent flexibility and accuracy of the approach presented here can be easily generalized to other types of crystals.

Introduction: Colon cancer is the fourth most common cancer in the United States and the third leading cause of cancer-related death. High throughput genomic sequencing has led to a number of significant advances in tumor biology and in identifying novel signaling molecules such as long non-coding RNA (lncRNA). The aim of this study was to identify differentially expressed lncRNA in colon cancer from a large RNA-sequencing (RNA-seq) data set. Methods: The raw RNA-seq files of 398 patients with colon cancer were downloaded from The Cancer Genome Atlas (TCGA). Sequencing files were aligned using STAR (Spliced Transcripts Alignment to a Reference) to the most recent genome annotation. Using a subset of patients with paired colon cancer and normal colon epithelium RNA-seq data (n=40), an exploratory binomial regression model was used to calculate differential RNA expression. The most differentially expressed lncRNA were identified from the exploratory analysis and verified by comparing the larger colon cancer RNA-seq data set (n=358) with the normal colon epithelium RNA-seq data set (n=40). Results: 33,514 genes were identified from the comparative analysis, and using differential expression cut off values of &amp;gt; +1.5 or &amp;lt; -1.5 log fold change and a false discovery rate of &amp;lt;0.05; 543 were upregulated and 1822 were downregulated. Within this dysregulated group, 60 lncRNAs were identified. Using the larger data set (n=358 vs n=40), 41/60 lncRNA remained differentially expressed, of which 15 were downregulated and 26 were upregulated. Twenty-four of these lncRNAs have not previously been described in colon cancer, 12 of which are upregulated and 12 of which are downregulated (Table 1). Conclusions: This analysis of RNA-seq data from TCGA has identified dysregulated lncRNAs which have not previously been described in human colon cancer. These lncRNAs may have significant roles in colon cancer tumor signaling. Table 1.Differential expression of lncRNA not described in colon cancerlncRNALog Fold ChangeP-valueDescription in other cancersKRT16P14.1041.36E-21Increased in lung squamous cell carcinomaBOK-AS13.7784.98E-09Increased in oral squamous cell carcinomaCELP3.1151.30E-09Not availableCLDN10-AS12.3944.61E-12Not availablePOU6F2-AS12.2678.25E-09Not availableSLC7A11-AS12.1931.60E-13Decreased in gastric adenocarcinomaCSAG42.1083.59E-10Not availableLUCAT12.0224.21E-11Increased in Ovarian cancer, renal clear cell carcinoma, head and neck squamous cell carcinoma, Non-small cell cancer, glioma, osteosarcoma, esophageal squamous cell carcinomaVPS9D1-AS11.9795.27E-21Decreased in gastric adenocarcinoma, Increased in non-small cell lung cancerLEF1-AS11.8464.48E-13Increased in glioblastomaCASC81.6485.64E-06Single nucleotide polymorphisms in colorectal adenocarcinomaLINC004911.5903.11E-04Not availableLINC00648-1.5021.02E-15Not availableLINC00163-1.6751.03E-22Not availableSATB2-AS1-1.8074.18E-09Increased in osteosarcomaLINC00702-2.1424.81E-18Not availableTRHDE-AS1-2.2623.44E-24Not availableLINC00908-2.3082.76E-103Not availableLINC00483-2.4012.87E-39Increased in gastric adenocarcinomaLINC00461-2.4401.42E-41Increased in glioma, multiple myelomaFENDRR-2.6872.12E-58Decreased in breast cancer, prostate cancer, osteosarcoma, gastric adenocarcinoma,ARHGEF26-AS1-2.7148.55E-32Not availableADAMTS9-AS2-2.9041.78E-74Increased in lung cancer salivary adenoid cystic carcinoma, Decreased in glioma, gastric adenocarcinomaMT1JP-4.4601.08E-114Decreased in retinoblastoma, Decreased in gastric adenocarcinoma Citation Format: Stephen J. O'Brien, Theodore Kalbfleisch, Sudhir Srivastava, Shesh Rai, Susan Galandiuk. Differential expression of long non-coding RNA in colon adenocarcinoma RNA-sequence data set [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1817.

Introduction: Decision-making for orchiectomy following testicular torsion often relies on subjective clinical evaluations.This study investigated the efficacy of machine-learning (ML) models in objectively predicting post-torsion testicular viability, aiming to maintain a parenchymal ratio over 80% compared to the contralateral testicle, irrespective of initial appearance and surgical timing.Methods: A prospective database from a single surgeon (2020-2024) covering all patients who underwent detorsion and subsequent bilateral orchidopexy was used.Followup ultrasounds were conducted at 6-12 months post-procedure.Variables included patient age (neonatal, prepubertal, post-pubertal), time from presentation to surgery, and history of torsion-detorsion events.Various ML models -regression, k-nearest neighbors, and decision trees -were assessed for accuracy and precision. Results:The decision tree model demonstrated the highest accuracy at 90.5%, followed by the regression model at 82.7% accuracy, and the k-nearest neighbors model at 81.4% accuracy.The area under the curve (AUC) for the regression model indicated adequate predictability for testicular viability.A crucial finding was the impact of the timing of surgical intervention; surgeries conducted within four hours showed a 100% viability rate (p=0.001).Age also significantly affected outcomes, with post-pubertal patients showing a higher viability rate of 57.1% (p=0.041).Detailed performance metrics, such as precision, recall, and F1-scores for each model, further validate the predictive capacity of these ML models (Figure 1, Tables 1,2).Conclusions: Preliminary results suggest that ML models are viable tools for predicting testicular viability post-torsion, significantly enhancing clinical decisionmaking by providing an objective basis for potentially preserving testicles.Further studies with larger datasets are necessary to confirm and refine these predictions.

Cholera remains a critical public health concern in Nigeria, causing significant mortality and straining healthcare resources. Despite efforts to control the disease, recurrent outbreaks persist, especially in areas with inadequate sanitation and limited access to clean water. Traditional models for predicting cholera outbreaks often fall short due to their inability to capture the multifactorial nature of the disease's spread. This study aims to address this gap by employing regression machine learning (ML) models that integrate various socio-economic, environmental, and demographic factors to optimize the prediction of total cholera outbreak in Nigeria. The research utilized a comprehensive dataset, including variables such as population size, health facilities, water sources, poverty rates, financial inclusion, and literacy levels, spanning across 76 local government areas (LGAs) in 21 states of Nigeria. Six machine learning models Linear Regression, Decision Trees, Random Forests, XGBoost, LightGBM, and Neural Networks were developed and compared to identify the most effective predictive approach. Data preprocessing, feature engineering, and exploratory data analysis (EDA) were conducted to ensure data quality and model readiness. The results indicate that LightGBM outperforms other models, with the lowest Mean Absolute Error (MAE) and the highest R-squared value, explaining 69% of the variance in cholera cases. This model's ability to handle complex interactions between variables suggests its potential utility in real-time public health interventions. The study concludes that integrating advanced machine learning techniques into public health surveillance can significantly enhance cholera outbreak prediction and support proactive strategies to mitigate the disease's impact. These findings offer actionable insights for policymakers, public health authorities, and researchers aiming to improve disease surveillance and response in Nigeria and other developing regions.

Machine learning for Big Data analytics in plants.