Missing Data Research Articles

Linkability measures to assess the data characteristics for record linkage.

Accurate record linkage (RL) enables consolidation and de-duplication of data from disparate datasets, resulting in more comprehensive and complete patient data. However, conducting RL with low quality or unfit data can waste institutional resources on poor linkage results. We aim to evaluate data linkability to enhance the effectiveness of record linkage. We describe a systematic approach using data fitness ("linkability") measures, defined as metrics that characterize the availability, discriminatory power, and distribution of potential variables for RL. We used the isolation forest algorithm to detect abnormal linkability values from 188 sites in Indiana and Colorado, and manually reviewed the data to understand the cause of anomalies. We calculated 10 linkability metrics for 11 potential linkage variables (LVs) across 188 sites for a total of 20680 linkability metrics. Potential LVs such as first name, last name, date of birth, and sex have low missing data rates, while Social Security Number vary widely in completeness among all sites. We investigated anomalous linkability values to identify the cause of many records having identical values in certain LVs, issues with placeholder values disguising data missingness, and orphan records. The fitness of a variable for RL is determined by its availability and its discriminatory power to uniquely identify individuals. These results highlight the need for awareness of placeholder values, which inform the selection of variables and methods to optimize RL performance. Evaluating linkability measures using the isolation forest algorithm to highlight anomalous findings can help identify fitness-for-use issues that must be addressed before initiating the RL process to ensure high-quality linkage outcomes.

Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization

Forecasting urban traffic states is crucial to transportation network monitoring and management, playing an important role in the decision-making process. Despite the substantial progress that has been made in developing accurate, efficient, and reliable algorithms for traffic forecasting, most existing approaches fail to handle sparsity, high-dimensionality, and nonstationarity in traffic time series and seldom consider the temporal dependence between traffic states. To address these issues, this work presents a Hankel temporal matrix factorization (HTMF) model using the Hankel matrix in the lower dimensional spaces under a matrix factorization framework. In particular, we consider an alternating minimization scheme to optimize the factor matrices in matrix factorization and the Hankel matrix in the lower dimensional spaces simultaneously. To perform traffic state forecasting, we introduce two efficient estimation processes on real-time incremental data, including an online imputation (i.e., reconstruct missing values) and an online forecasting (i.e., estimate future data points). Through extensive experiments on the real-world Uber movement speed data set in Seattle, Washington, we empirically demonstrate the superior forecasting performance of HTMF over several baseline models and highlight the advantages of HTMF for addressing sparsity, nonstationarity, and short time series. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research was supported by the Institute for Data Valorisation, the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, the National Natural Science Foundation of China [Grants 12371456, 72101049, 72232001], the Sichuan Science and Technology Program [Grant 2024NSFJQ0038], and the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045].

EFFICIENCY IN ESTIMATION UNDER MONOTONIC ATTRITION

Attrition is monotonic when agents leaving multi-period studies do not return. Under a general missing at random (MAR) assumption, we study efficiency in estimation of parameters defined by moment restrictions on the distributions of the counterfactuals that were unrealized due to monotonic attrition. We discuss novel issues related to overidentification, usability of sample units, and the information content of various MAR assumptions for estimation of such parameters. We propose a standard doubly robust estimator for these parameters by equating to zero the sample analog of their respective efficient influence functions. Our proposed estimator performs well and vastly outperforms other estimators in our simulation experiment and empirical illustration.

PSLBI-22 Identifying plasma metabolites influencing body weight in Salmonella challenged growing pigs: A machine learning approach

Abstract Body weight (BW) is an important component of pig productivity. Understanding the metabolic factors affecting growth rate is key to guiding nutritional strategies and bringing economic benefits to pig farms. Machine learning (ML) algorithms have been used to capture patterns in large datasets and predict animal production traits based on integrated information of performance data and blood records. However, utilizing ML models and metabolomic profiling to predict BW of pigs in small datasets with challenged animals has yet to be reported. Therefore, this study investigated use of plasma metabolites in predicting BW of pigs at the growing stage. This simulation used data from gilts (n = 40; BW = 25.6 ± 2.3 kg) raised over a 28-d trial. Plasma samples and BW were collected on d 0, 14, and 28 after 8 h of fasting. Ninety plasma metabolites were identified and quantified from an untargeted metabolomics assay. The development dataset included 30, and the validation dataset contained 10 unique animals with repeated measures in time. The metabolites were log-transformed, Pareto-scaled and missing values were imputed using the minimum nonzero value method. The BW prediction model was performed in R with the caret package. The optimal hyper-parameter configuration for the random forest and neural network algorithms was evaluated based on repeated cross-validation. The final model was the random forest composed of 400 trees with 45 randomly selected predictors. Model accuracy was estimated on repeated cross-validation with 10-folds and 5 repeats and compared with the validation set based on the coefficient of determination (R2), the root mean squared error (RMSE), and Pearson correlation (r). The metabolites with greater contribution with variable importance score above 10 to the model development were extracted. Aiming to understand differences in BW categories, the model response was visualized with an alluvial diagram for the 3 variables with the greatest metabolite importance, and BW was classified as high, medium, and low. The main metabolites driving BW were lysine, oleic acid, aminomalonic acid, myo-inositol, and linoleic acid. The RMSE after cross-validation was 7.22 kg of BW (r = 0.70), and R2 = 0.39. For pigs with a high BW, the predicted BW was explained by generally less plasmatic concentrations of lysine, oleic acid, and aminomalonic acid. Lysine is the main amino acid for muscle protein accretion, whereas oleic acid is for energy substrate. Aminomalonic acid is a metabolite resulting from glycine metabolism and is important in reversing oxidative damage caused by pathogens. Therefore, decreased plasma lysine, amino malonic acid, and oleic acid may indicate faster growth and lean deposition, leading to heavier BW in pigs. Using random forests helped to connect the plasma dynamics to BW and is a promising avenue to understand the main drivers in BW of group-housed pigs.

NeRF-OR: neural radiance fields for operating room scene reconstruction from sparse-view RGB-D videos.

RGB-D cameras in the operating room (OR) provide synchronized views of complex surgical scenes. Assimilation of this multi-view data into a unified representation allows for downstream tasks such as object detection and tracking, pose estimation, and action recognition. Neural radiance fields (NeRFs) can provide continuous representations of complex scenes with limited memory footprint. However, existing NeRF methods perform poorly in real-world OR settings, where a small set of cameras capture the room from entirely different vantage points. In this work, we propose NeRF-OR, a method for 3D reconstruction of dynamic surgical scenes in the OR. Where other methods for sparse-view datasets use either time-of-flight sensor depth or dense depth estimated from color images, NeRF-OR uses a combination of both. The depth estimations mitigate the missing values that occur in sensor depth images due to reflective materials and object boundaries. We propose to supervise with surface normals calculated from the estimated depths, because these are largely scale invariant. We fit NeRF-OR to static surgical scenes in the 4D-OR dataset and show that its representations are geometrically accurate, where state of the art collapses to sub-optimal solutions. Compared to earlier work, NeRF-OR grasps fine scene details while training 30 faster. Additionally, NeRF-OR can capture whole-surgery videos while synthesizing views at intermediate time values with an average PSNR of 24.86dB. Last, we find that our approach has merit in sparse-view settings beyond those in the OR, by benchmarking on the NVS-RGBD dataset that contains as few as three training views. NeRF-OR synthesizes images with a PSNR of 26.72dB, a 1.7% improvement over state of the art. Our results show that NeRF-OR allows for novel view synthesis with videos captured by a small number of cameras with entirely different vantage points, which is the typical camera setting in the OR. Code is available via: github.com/Beerend/NeRF-OR .

A multiple imputation method using population information

Multiple imputation (MI) is effectively used to deal with missing data when the missing mechanism is missing at random. However, MI may not be effective when the missing mechanism is not missing at random (NMAR). In such cases, additional information is required to obtain an appropriate imputation. Pham et al. (2019) proposed the calibrated-δ adjustment method, which is a multiple imputation method using population information. It provides appropriate imputation in two NMAR settings. However, the calibrated-δ adjustment method has two problems. First, it can be used only when one variable has missing values. Second, the theoretical properties of the variance estimator have not been provided. This article proposes a multiple imputation method using population information that can be applied when several variables have missing values. The proposed method is proven to include the calibrated-δ adjustment method. It is shown that the proposed method provides a consistent estimator for the parameter of the imputation model in an NMAR situation. The asymptotic variance of the estimator obtained by the proposed method and its estimator are also given.

The condition monitoring scheme for industrial IoT scenario: A distributed modeling for high-dimensional nonstationary data

Based on large-scale data collection and high-speed transmission, Industrial Internet of Things (IIoT) promotes the rapid development of intelligent manufacturing. IIoT systems are usually disturbed by complex external factors, which lead to high-dimensional nonstationary operating data. Besides, unexpected data transmission interruptions, sensor failures, and network delays lead to data loss. This paper proposes a distribution & communication strategy for monitoring high-dimensional nonstationary processes with missing values in IIoT scenarios. First, a deep learning-based imputation network is proposed to impute the missing values. Then a decomposition strategy based on degree of cointegration is proposed, which decomposes a high-dimensional nonstationary process into multiple blocks. And a communication strategy is proposed to mine the internal relationship between different blocks. Finally, faulty information is detected by a distributed framework. Two real cases from IIoT are applied to illustrate the monitoring performance of the proposed method. The results show that the proposed method outperforms existing benchmarks in data imputation and monitoring performance.

CryoTEN: Efficiently Enhancing Cryo-EM Density Maps Using Transformers.

Cryogenic Electron Microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them. In this study, we introduce CryoTEN - a three-dimensional U-Net style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1,295 cryo-EM maps as inputs and their corresponding simulated maps generated from known protein structures as targets. An independent test set containing 150 maps is used to evaluate CryoTEN, and the results demonstrate that it can robustly enhance the quality of cryo-EM density maps. In addition, the automatic de novo protein structure modeling shows that the protein structures built from the density maps processed by CryoTEN have substantially better quality than those built from the original maps. Compared to the existing state-of-the-art deep learning methods for enhancing cryo-EM density maps, CryoTEN ranks second in improving the quality of density maps, while running > 10 times faster and requiring much less GPU memory than them. The source code and data is freely available at https://github.com/jianlin-cheng/cryoten.

Generating synthetic identifiers to support development and evaluation of data linkage methods

We aimed to develop a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error. We determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We generated synthetic versions of a large United Kingdom cohort study (the Avon Longitudinal Study of Parents and Children), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches). Comparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility. Replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods. Our framework provides a novel and generalisable mechanism for developing and benchmarking record linkage algorithms.

Leveraging Machine Learning to Combat Missingness and Error in Data

ObjectivePoor quality data confounds efforts to link clients across datasets. To combat this, we have trialed an approach which seeks to identify linkage candidates using associations with related services. We utilised a machine learning (ML) approach to query linkage candidates’ dataset associations and make predictions about whether candidates are likely to be a client of the service being integrated for linkage. ApproachUtilizing a K-nearest neighbors algorithm, we trained a model using 15 variables to predict whether a linkage candidate is a client of a family service dataset. We then evaluated the model's success, and modified its native performance to minimise false positive matches. Subsequently, we tested the validity of these predictions as linkage criteria by employing blocking strategies and linking the service dataset to our linkage spine. The trained model was then used to identify correct links against the spine. ResultsThe evaluation of the machine learning model yielded promising results, with high accuracy (88.5%) and precision (95.5%). Testing the predictions as linkage criteria resulted in highly accurate links ranging from 96.0% to 98.7% across different blocking strategies. Despite some records failing to establish any links to the spine, rates of false positive matches remained low (0.7% to 3.1%). ConclusionMissingness and inaccuracy in data remains a key problem for data linkage, and a robust approach is required to resolve complex linkage cases. However, these findings suggest that machine learning can present novel options for a toolbox of many approaches to link problem records to a linkage spine.

Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst.

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencoder, an unsupervised machine learning model consisting of one encoder and one decoder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements. We also introduce the denoising autoencoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California. It turns out that our approach can achieve comparable performance to the-state-of-the-art calibration methods given normal data and outperform them given corrupted data with missing values. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This study was supported by the National Science Foundation [Grant CMMI-1949710] and the C2SMART Research Center, a Tier 1 University Transportation Center.

Comparing the Efficacy of 2 WeChat Mini Programs in Reducing Nonmarital Heterosexual Contact by Male Factory Workers: Randomized Controlled Trial.

Male factory workers in China are vulnerable to HIV transmission. Commercial and nonmarital noncommercial contacts are the driving forces of heterosexual HIV transmission among male factory workers in China. There is a lack of effective HIV interventions for male factory workers in China. The primary objective of this randomized controlled trial was to compare the efficacy of an enhanced versus the standard version of a WeChat mini program in reducing sexual intercourse with nonregular female sex partners and female sex workers among male factory workers in Shenzhen, China. A nonblinded 2-arm parallel randomized controlled trial was conducted between December 2021 and April 2023. Participants were adult male factory workers in Shenzhen who had access to a smartphone and WeChat. Those who had oral or anal sex with a man or self-reported as HIV positive were excluded. A total of 247 participants were randomly assigned to the intervention group (n=125, 50.6%) or the control group (n=122, 49.4%); 221 (89.5%) and 220 (89.1%) completed follow-up surveys at T1 (6 months after completion of the interventions) and T2 (6 months after T1). Participants in the control group had access to the standard WeChat mini program that provided basic HIV-related knowledge and information about local free HIV testing services. Participants in the intervention group had access to the enhanced WeChat mini program. The enhanced mini program covered all the information in the standard mini program. In addition, the enhanced mini program assessed users' behaviors and invited users to watch different web-based videos on reducing nonmarital sexual contacts and promoting HIV testing based on their behavioral characteristics at months 0 and 1. The videos were developed based on in-depth interviews with male factory workers. Intention-to-treat analysis was used for outcome analyses. Multiple imputation was used to replace missing outcome values at T1 and T2. At T1, fewer participants in the intervention group reported sexual intercourse with a nonregular female sex partner in the past 6 months compared with the control group (1/125, 0.8% vs 8/122, 6.6%; relative risk=0.12, 95% CI 0.02-0.96; P=.02). However, there were no between-group differences in sexual intercourse with a nonregular female sex partner at T2 (10/125, 8% vs 14/122, 11.5%; P=.36) or sexual intercourse with a female sex worker at T1 (2/125, 1.6% vs 2/122, 1.6%; P=.98) or T2 (8/125, 6.4% vs 8/122, 6.6%; P=.96). The enhanced WeChat mini program was more effective than the standard WeChat mini program in reducing sexual intercourse with nonregular female sex partners among male factory workers in the short term but not in the longer term. Improvements should be made to the WeChat mini program before implementation. ClinicalTrials.gov NCT05811611; https://clinicaltrials.gov/study/NCT05811611.

Optimizing Prehospital Stroke Diagnosis: Integrating Machine Learning with the FAST Scoring System

Strokes are a leading cause of disability and death worldwide, and timely diagnosis is critical for effective treatment. Prehospital stroke diagnosis by emergency medical services (EMS) is essential for ensuring prompt care. The FAST (Face, Arms, Speech, and Time) scoring system is a common tool used to identify stroke symptoms quickly. However, the traditional FAST system has limitations, including reliance on subjective assessments and potential misdiagnoses due to symptom variability. This study aims to optimize the FAST scoring system and compare its performance with various machine learning models to enhance stroke prediction accuracy. A dataset from an ambulance service, including prehospital data such as age, sex, and FAST test results, was used. Data preprocessing involved handling missing values, encoding categorical variables, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset. A grid search was conducted to test different weighting schemes for the FAST system. Machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Decision Tree, were trained and evaluated using cross-validation on the training set and tested on a separate test set. The traditional FAST system showed an accuracy of 62.95%. Optimizing the FAST system through grid search marginally increased accuracy to 64.36%. However, machine learning models, particularly Random Forest, significantly outperformed the FAST system, achieving a test set accuracy of 88.54%. XGBoost also demonstrated strong performance with an accuracy of 77.07%, while Logistic Regression, SVM, and Decision Tree showed lower accuracies. These findings suggest that integrating machine learning models into EMS protocols could substantially improve the accuracy of prehospital stroke diagnosis, leading to quicker and more accurate identification and treatment of stroke patients. Although the optimized FAST system offers slight improvements, machine learning models present a promising avenue for enhancing stroke prediction and improving patient outcomes in emergency settings while acknowledging practicality issues.

Predicting suicidal ideation from irregular and incomplete time series of questionnaires in a smartphone-based suicide prevention platform: a pilot study

Over 700,000 people die by suicide annually. Collecting longitudinal fine-grained data about at-risk individuals, as they occur in the real world, can enhance our understanding of the temporal dynamics of suicide risk, leading to better identification of those in need of immediate intervention. Self-assessment questionnaires were collected over time from 89 at-risk individuals using the EMMA smartphone application. An artificial intelligence (AI) model was trained to assess current level of suicidal ideation (SI), an early indicator of the suicide risk, and to predict its progression in the following days. A key challenge was the unevenly spaced and incomplete nature of the time series data. To address this, the AI was built on a missing value imputation algorithm. The AI successfully distinguished high SI levels from low SI levels both on the current day (AUC = 0.804, F1 = 0.625, MCC = 0.459) and three days in advance (AUC = 0.769, F1 = 0.576, MCC = 0.386). Besides past SI levels, the most significant questions were related to psychological pain, well-being, agitation, emotional tension, and protective factors such as contacts with relatives and leisure activities. This represents a promising step towards early AI-based suicide risk prediction using a smartphone application.

Prediction of oil production based on multivariate analysis and self-attention mechanism integrated with long short-term memory

The accurate oil prediction of wells is essential for making informed decisions regarding the extension of the well lifespan and the enhancement of oil recovery rates. However, the prediction of oil well production is highly challenging due to the complex, nonlinear, and non-stationary data influenced by reservoir geological characteristics and operational adjustments. To address this, a novel prediction method for oil well production is proposed in this study. Firstly, the data-cleaning approach designed in this study is utilized to eliminate outliers from the raw dataset and impute missing values. Subsequently, relevant features are identified through analysis to form a usable dataset. The proposed deep learning neural network, namely the self-attention mechanism integrated with long short-term memory, is trained and learned on this dataset. Finally, the predictive performance of the model is validated using a set of actual oil production data. Through data experiments, the proposed model effectively predicts oil well production with superior accuracy compared to baseline models, achieving an R-squared of 0.872. This method provides reliable decision support for optimizing oil field development and management.

Improving Air Quality Data Reliability through Bi-Directional Univariate Imputation with the Random Forest Algorithm

Forecasting the future levels of air pollution provides valuable information that holds importance for the general public, vulnerable populations, and policymakers. High-quality data are essential for precise and reliable forecasts and investigations of air pollution. Missing observations arise when the sensors utilized for assessing air quality parameters experience malfunctions, which result in erroneous measurements or gaps in the dataset and hinder the data quality. This research paper presents a novel approach for imputing missing values in air quality data in a univariate approach. The algorithm employs the random forest (RF) algorithm to impute missing observations in a bi-directional (forward and reverse in time) manner for air quality (particulate matter less than 2.5 μm (PM2.5)) data from the Republic of Serbia. The algorithm was evaluated against simple methods, such as the mean and median imputation methods, for missing observations over durations of 24, 48, and 72 h. The results indicate that our algorithm yielded comparable error rates to the median imputation method for all periods when imputing the PM2.5 data. Ultimately, the algorithm’s higher computational complexity proved itself as not justified considering the minimal error decrease it achieved compared with the simpler methods. However, for future improvement, additional research is needed, such as utilizing low-code machine learning libraries and time-series forecasting techniques.

Addressing immortal time bias in precision medicine: Practical guidance and methods development.

To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study. Methods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes. Case study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015. While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations. Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.

ADVANCED MACHINE LEARNING FOR PREDICTIVE MODELING FOR ASTHMA RISK FACTORS :COMPARATIVE STUDIES AND ANALYSIS

Asthma is a chronic and multifactorial respiratory disease that depends on the patient's genetic make-up, the internal environment in the body and the external environment within which the patient exists. These complex influences are explored in this project by employing a large number of patients’ records, 2,392. It consists of numerous demographic characteristics, lifestyles (tobacco use and exercise), environmental conditions (pollution and irritants), past medical history (asthma in first-degree relatives and allergies), physical examination (spirometry), and symptoms (wheezing, breathlessness). Some of the crucial goals of the present work are to determine the factors that would impact the prevalence of Gastroesophageal Reflux and Smoking status among patients with asthma. It involves data cleaning by dealing with the missing values and categorical variables, and feature scaling by using z-score to deal with outliers. The SMOTE algorithm is used on the dataset to cope with the problem of class imbalance. Logistic Regression, Decision Trees, KNN, AdaBoost, and NB algorithms are used to forecast the target variables. There are a lot of evaluation matrices such as accuracy, precision, recall, F1 measure, Receiver Operating Characteristic (ROC) Area Under the Curve, and many more to evaluate the model. ROC curves and precision-recall curves are commonly used to give a full and accurate evaluation of the model’s performance with the help of confusion matrices. The outcomes of the study shall help to increase understanding of factors that underlie asthma and enhance the potential for prognosis and patient management at the individual level KEYWORDS: phase, age, gender, occupation, geographic location, history of diseases, vital statistics, signs, artificial intelligence, prognostic modeling, performances, health facilities

Generalized Denoising Coder with Multi-modal Approach for Handling Incomplete Information in Public Health Big Data Analytics

A data-driven health model incorporates many missing and incomplete values, and the effectiveness of a health model that relies on characteristics not captured by the consumer is low. A Deep Learning (DL) health paradigm aims to improve the precision of learning weights. A DL-based health model is trained by optimizing the weights to maximize accuracy. If the learning process is excluded, the accuracy of applying a DL-based health model to the user's circumstance may be reduced, and it becomes the responsibility of people to manage Public Health Big Data Records (PHBDR). To handle incomplete information, this study suggested a generalized denoising coder with a multi-modal approach in public health big data records (GDC-MM-PHBDR). The suggested technique uses a Generalized Denoising Coder (GDC), which estimates the output values following the relevant input values using Neural Networks (NN). The National Health and Nutrition Examination Study (NHNES) provided data for this investigation. This approach allows for the estimation of incomplete information in the PHBDR. Multimodality, which the PHBDRs provide, enables compiling data for a single item from many sources. The layout of the GDC is multi-modal. The GDC-MM-PHBDR approach continuously demonstrates excellent performance, attaining maximum accuracy across all noise levels. It begins at 98.31% accuracy with a noise factor of 0.05 and significantly decreases to 94.5% with a noise factor of 0.25.