Missing Ratio Research Articles

Earliest Deadline First Scheduling for Real-Time Computing in Sustainable Sensors

Energy harvesting is a green technology that authorizes small electronic devices to be supplied for perpetual operation. It enables wireless sensors to be integrated in applications that previously were not feasible with conventional battery-powered designs. Intermittent computing and scheduling are the two central aspects of designing a Real-Time Energy Harvesting (RTEH) sensor, generally used to monitor a mission critical process. Traditional scheduling algorithms fail to timely execute the hard deadline tasks because they accommodate no fluctuations in power supply and therefore no intermittent computing. A suitable energy-harvesting-aware scheduling algorithm has been proposed so as to achieve a higher schedulability rate. Unlike the classical EDF (Earliest Deadline First) scheduler, the ED-H algorithm is idling and clairvoyant, with an improved performance in terms of the deadline missing ratio. This paper reviews the main advances in dynamic priority scheduling based on EDF for energy-neutral systems.

Univariate imputation method for recovering missing data in wastewater treatment process

• A univariate imputation method, namely UIM, is proposed for WWTP time series. • The employ STL is the best choice for decomposing the WWTP data. • A self-similarity decomposition is designed to detect the repeating patterns. • Extensive experimental results verify the effectiveness of UIM. High-quality data play a paramount role in monitoring, control, and prediction of wastewater treatment process (WWTP) and can effectively ensure the efficient and stable operation of system. Missing values seriously degrade the accuracy, reliability and completeness of the data quality due to network collapses, connection errors and data transformation failures. In these cases, it is infeasible to recover missing data depending on the correlation with other variables. To tackle this issue, a univariate imputation method (UIM) is proposed for WWTP integrating decomposition method and imputation algorithms. First, the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal, trend and remainder components to deal with the nonstationary characteristics of WWTP data. Second, the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values. A self-similarity decomposition is conducted to fill the seasonal component based on its periodic pattern. Third, all the imputed results are merged to obtain the imputation result. Finally, six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two indicators. The experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ratios. Therefore, the proposed UIM is a promising method to impute WWTP time series.

Dynamic Scheduling Strategies for Firm Semi-Periodic Real-Time Tasks

This paper introduces and assesses novel strategies to schedule firm semi-periodic real-time tasks. Jobs are released periodically and have the same relative deadline. Job execution times obey an arbitrary probability distribution and can take either bounded or unbounded values. We investigate several optimization criteria, the most prominent being the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deadline Miss Ratio</i> ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DMR</small> ). All previous work uses some admission policies but never interrupt the execution of an admitted job before its deadline. On the contrary, we introduce three new control parameters to dynamically decide whether to interrupt a job at any given time. We derive a Markov model and use its stationary distribution to determine the best value of each control parameter. Finally we conduct an extensive simulation campaign with 16 different probability distributions. The results nicely demonstrate how the new strategies help improve system performance compared with traditional approaches. In particular, we show that (i) compared to pre-execution admission rules, the control parameters make significantly better decisions; (ii) specifically, the key control parameter is to upper bound the waiting time of each job; (iii) the best scheduling strategy decreases the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DMR</small> by up to 0.35 over traditional competitors.

Three-Dimensional Seismic Data Reconstruction Based on Fully Connected Tensor Network Decomposition

Rank-reduction approaches assume that seismic data in the frequency-space domain is of low-rank after a specific pre-transformation. The presence of noise or missing traces will increase the rank; therefore, seismic data can be denoised and recovered via rank-reduction techniques. The iterative weighted project onto the convex set (POCS) framework can be used for noise attenuation and data reconstruction simultaneously. Multichannel singular spectrum analysis (MSSA) is a classic 3D seismic data reconstruction algorithm that rearranges the temporal frequency slices of the data with missing traces into a block Hankel matrix, and then uses randomized singular value decomposition (RSVD) to interpolate slices. To further improve the efficiency and precision of 3D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results. Moreover, FCTN decomposes a fourth-order tensor into four factor contractions, which breaks the limitations that traditional tensor decomposition methods such as CANDECOMP/PARAFAC (CP) and Tucker decomposition cannot establish the connections between different factors, and are less effective at characterizing relationships. The newly proposed approach does not require singular value decomposition (SVD), leading to an overall reduction in computational complexity. Synthetic and field examples are used to compare the performance of our method with MSSA, and our numerical results reveal the better performance of the proposed FCTN decomposition method for seismic data with large gaps or a high missing ratio.

An autoencoder-based deep learning method for genotype imputation.

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses.

DLIN: Deep Ladder Imputation Network.

Many efforts have been dedicated to addressing data loss in various domains. While task-specific solutions may eliminate the respective issue in certain applications, finding a generic method for missing data estimation is rather complex. In this regard, this article proposes a novel missing data imputation algorithm, which has supreme generalization ability for a vast variety of applications. Making use of both complete and incomplete parts of data, the proposed algorithm reduces the effect of missing ratio, which makes it suitable for situations with very high missing ratios. In addition, this feature enables model construction on incomplete training sets, which is rarely addressed in the literature. Moreover, the nonparametric nature of this new algorithm brings about supreme flexibility against all variations of missing values and data distribution. We incorporate the advantages of denoising autoencoders and ladder architecture into a novel formulation based on deep neural networks. To evaluate the proposed algorithm, a comparative study is performed using a number of reputable imputation techniques. In this process, real-world benchmark datasets from different domains are selected. On top of that, a real cyber-physical system is also evaluated to study the generalization ability of the proposed algorithm for distinct applications. To do so, we conduct studies based on three missing data mechanisms, namely: 1) missing completely at random; 2) missing at random; and 3) missing not at random. The attained results indicate the superiority of the proposed method in these experiments.

Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns

Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Recent research has employed graph neural networks (GNNs) for spatiotemporal data imputation and achieved promising performance. However, there still exist two limitations to be addressed: first, existing approaches are generally limited in directly leveraging global spatiotemporal information from different nodes at different time; second, most of these approaches do not consider the unique characteristics of transportation systems or traffic data, including dynamic spatial dependencies and correlated missing patterns. To fill these research gaps, we propose a novel deep learning framework called Memory-augmented Dynamic Graph Convolution Networks (MDGCN) to impute missing traffic data. The model uses a recurrent layer to capture temporal information and a graph convolution layer to capture spatial information. To address the first research gap, we introduce an external memory network to store and share the global spatiotemporal information across the traffic network. For the second research gap, a graph structure estimation technique is proposed to learn dynamic spatial dependencies directly from traffic data. In addition, four types of missing patterns with various missing ratios are considered in model evaluation. Extensive experiments based on two public traffic speed datasets are conducted. The results show that our proposed model outperforms existing state-of-the-art deep learning approaches in all kinds of missing scenarios, and both the proposed external memory network and graph structure estimation technique contribute to the model performance. The model performance is competitive in most cases even without complete training data.

Segmentation of ten fetal heart components with coarse‐to‐fine cascading and dynamic feature powering

Segmenting heart components in the apical four-chamber view of fetal echocardiography is of critical significance in clinical practice. However, it is difficult to recognize these components due to small-scale components and the imbalanced ventricular apex orientation. In this study, a novel segmentation framework is proposed to segment ten general fetal heart components for the first time. This framework consists of a multi-directional fine-density (MDFD) data augmentation method and a coarse-to-fine cascade network (CFCN). MDFD enhances the apex orientation diversity and balances the orientation distribution. CFCN has two stages including a coarse network and a fine network. These two stages have similar structures that consist of a feature extractor and a feature refined layer named as Element-Wise Power with Dynamic Exponent layer (EWPDE). EWPDE which is a plug-and-play module for segmentation refines the features from the feature extractor to position small components accurately. By adopting EWPDE, the influence of each pixel is adjusted and hard pixels of small components are segmented precisely. Based on the dataset, the method is proved to be effective with the high mean intersection over union (mIoU) value and low missing ratio (MR). With MDFD and EWPDE, CFCN that adopts DeepLabV3+ as the feature extractor outperforms the best segmentation results (mIoU:0.480, MR:0.035). Compared to the original performance (mIoU:0.407, MR:0.085) of DeepLabV3+, the method improves the results significantly.

Reliable Task Allocation for Time-Triggered IoT-WSN Using Discrete Particle Swarm Optimization

A wireless sensor network (WSN) constitutes of nodes that are used to sense a certain physical environment phenomena and sends these collected data to a remote node for further processing and decision making. With the proliferation of Internet of Things (IoT), WSN is evolving to be used as reliable, energy efficient, cost effective, and scalable network infrastructure for many IoT applications. To have a robust WSN with IoT (IoT-WSN), it is a challenge to allocate the tasks among different sensor nodes because the distribution of dependent tasks among wireless nodes must satisfy precedence and time constraints, minimize energy consumption, and prevent signal interference. The problem becomes more complex if a system requires high reliability in the event of link or node failures. This article presents an offline discrete particle swarm optimization algorithm for reliable task allocation (DPSO-TA) in IoT-WSN. DPSO-TA defines a utility function to optimize the task allocation problem by iteratively trying to improve the solution. The defined function is designed to satisfy goals, including saving energy, reducing task completion time, and minimizing the failure rates. A load balance mechanism is applied to make balancing of the tasks on several hosts. During the task allocation process, the frame replication and elimination for reliability (FRER) approach is used to support flow fault tolerance by replicating transmitted flows over redundant routes. DPSO-TA considers the periodicity of the time-trigged (TT) flows and applies a physical interference model to prevent signal interference on the transmitted flows through assigning them to conflict-free time slots. The analysis and simulation results show the optimization of DPSO-TA on timeliness, deadline missing ratio, failure rates, and energy efficiency comparing it with algorithms that allocate tasks to hosts that produce the minimum completion time or that use a traditional procedure to find the most suitable nodes during the task allocation process.

Significant Improvement in Rainfall Forecast over Delhi: Annual and Seasonal Verification

Regional Weather Forecasting Centre (RWFC) New Delhi has the responsibility to issue and disseminate rainfall forecast for Delhi. So it is very important to scientifically verify the rainfall forecast issued by RWFC. In this study rainfall forecast verification of Delhi has been carried out annually and season wise for the period 2011 to 2021. Various statistical parameters such as Percentage Correct (PC), Probability of Detection (POD), Missing Ratio (MR), False Alarm Ratio (FAR), Critical Success Index (CSI), True Skill Statistics (TSS) and Heidke Skill Score (HSS) have been calculated for season wise and annually. A forecast is considered to be improved if PC, POD, CSI, TSS and HSS increase and FAR and MR decrease over a period of time. The author can conclude that annual accuracy of forecast has increased significantly over the period of time from 2011 to 2021, as PC, POD, CSI, TSS and HSS increase and FAR and MR decrease over a period of time. Maximum contribution in the improved forecast has observed in transition season (pre-monsoon season followed by post-monsoon, having rainfall activity mainly in association with thunderstorms), when FAR and MR have decreased drastically.

To Tolerate or To Impute Missing Values in V2X Communications Data?

Misbehavior detection is a critical task in vehicular <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> networks (VANETs). However, state-of-the-art data-driven techniques for misbehavior detection are usually conducted through complete V2X communications data collected from simulated experiments. This article evaluates main strategies for the treatment of missing values to find out the best match for misbehavior detection with incomplete V2X communications data. This article proposes two novel methods for imputing and tolerating missing data. The former is a novel imputation method that is based on the collaborative clustering and the latter is a missing-tolerant method that is an ensemble learning based on the random subspace selection and Dempster–Shafer fusion. The effectiveness of the proposed techniques is evaluated by the ground-truth vehicular reference misbehavior (VeReMi) data. Moreover, a multifactor amputation framework has been developed to induce missingness over V2X communications data with different missing ratios, mechanisms, and distributions. This provides a comprehensive benchmark resembling missingness over V2X communications data. The proposed methods are compared with five missing-tolerant and nine imputation methods. The attained results over the benchmark data indicate that the proposed missing-tolerant method is significantly better than other treatment methods in terms of accuracy and F-measure.

Trimmed scores regression for k-means clustering data with high-missing ratio

Data sets with missing values bring great challenges to k-means clustering (KMC). At present, most studies focus on KMC data with low missing ratio while few studies on KMC data with high missing ratio. The current imputation methods have the following problems when dealing with the KMC data: (1) the error between imputation value and original true value is large, which leads to the poor imputation precision; (2) the imputation results have a great influence on the clustering results, which reduce the accuracies of the clustering results. We propose a novel imputation method, to deal with the problems, called as trimmed scores regression (TSR), which obtains an imputation estimator from a regression equation with a trimmed score matrix, and a novel cluster with k-means method. Compared with other imputation methods in numerical analysis, the TSR method exhibits better performance.

Online subspace learning and imputation by Tensor-Ring decomposition

This paper considers the completion problem of a partially observed high-order streaming data, which is cast as an online low-rank tensor completion problem. Though the online low-rank tensor completion problem has drawn lots of attention in recent years, most of them are designed based on the traditional decomposition method, such as CP and Tucker. Inspired by the advantages of Tensor Ring decomposition over the traditional decompositions in expressing high-order data and its superiority in missing values estimation, this paper proposes two online subspace learning and imputation methods based on Tensor Ring decomposition. Specifically, we first propose an online Tensor Ring subspace learning and imputation model by formulating an exponentially weighted least squares with Frobenium norm regularization of TR-cores. Then, two commonly used optimization algorithms, i.e. alternating recursive least squares and stochastic-gradient algorithms, are developed to solve the proposed model. Numerical experiments show that the proposed methods are more effective to exploit the time-varying subspace in comparison with the conventional Tensor Ring completion methods. Besides, the proposed methods are demonstrated to be superior to obtain better results than state-of-the-art online methods in streaming data completion under varying missing ratios and noise.

Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm

ABSTRACT Aerosol loadings and their spatial distribution are among the most important atmospheric information needed for a range of applications such as air quality monitoring, climate research, and public health. A key measure of aerosol quantity is aerosol optical depth (AOD) and it has been routinely observed from space by Earth observing satellites/instrument, especially the Moderate Resolution Imaging Spectroradiometer (MODIS). Despite its global coverage and daily temporal resolution, the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product is fraught with missing values, severely limiting its use. A gap-filling method which is suitable for large-area application with high efficiency to obtain gapless AOD with reasonable spatial pattern and complete coverage is still lacking. Here, we proposed a novel spatiotemporal fitting algorithm to gap-fill the daily MODIS AOD product. Our algorithm is a multi-stage method aimed to address the non-stationary nature of AOD time series. First, the trend of daily AOD in a year in each pixel was fitted via smoothing splines and the residual was derived based on the original data and the trend. Second, the residual was spatially interpolated, leveraging the spatial correlation between the target pixel and the neighboring pixels. Third, the actual AOD was calculated as the sum of the trend and interpolated residual. We tested the algorithm against ground-based AOD data from 2011 to 2018 in China and further evaluated it via cross-validation at the global scale based on 10 selected MODIS tiles. Compared to the ground-reference AOD, the RMSE of our gapless datasets were 0.24 and 0.27 for Terra and Aqua, respectively; and the cross-validation showed a RMSE ranging from 0.045 to 0.055 (Terra) and 0.047 to 0.057 (Aqua) under different missing ratios. The novel gap-filling method outperforms the Interpolation-based Correlation Weighting (ICW) and Inverse Distance Weighting (IDW) algorithms in accuracy. Meanwhile, the gapless AOD using the novel algorithm shows lower accuracy than original MAIAC AOD, similar accuracy with the AOD from the Long-term Gap-free High-resolution Air Pollutants (LGHAP) concentration dataset, higher accuracy than the AOD from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, the accuracy of gapless AOD using this algorithm meets the need of typical applications in relevant studies. The proposed algorithm is transferable to other regions, with the potential to be used even operationally and efficiently for generating accurate gapless global daily AOD datasets with the input of only MODIS MAIAC AOD data.

Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (&gt;27%) and a high missing ratio (&gt;33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD &gt; 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered.

Generation of High-Quality Spaceborne Interrupted FMCW SAR Images via Singular Value Threshold-Based Matrix Completion

The concept of spaceborne interrupted frequency-modulated continuous-wave (IFMCW) synthetic aperture radar (SAR) subverts the design principles of the traditional frequency-modulated continuous-wave (FMCW) SAR system, which uses a single physical antenna to transmit signal and receive echo alternatively. Due to the demand of turning off the receiver in the process of transmitting signal, periodical data-missing phenomena will be aroused in the echo data, and the missing ratio is close to 50&#x0025;. When traditional imaging algorithms were used to process the echo data, the artifacts, appearing as periodically replicated false targets, will be generated in the focused SAR image due to the periodical missing data, thus affecting the image interpretation. In order to effectively suppress the artifacts, a novel method exploiting singular value threshold-based matrix completion (SVT-MC) technology was proposed in this letter, where the singular value decomposition of the Hankel matrix was utilized to recover the missing data with high precision. Finally, the experimental results based on both point targets and distributed targets verify the effectiveness and superiority of the proposed method.

An observational study on maternal mortality and maternal near miss in a selected facility of West Bengal.

An observational study was performed at Purba Medinipur District Hospital, West Bengal, from April 1, 2018, to December 31, 2020, with an aim to find out the magnitude of maternal mortality and near miss cases and to assess the utilization of available maternal health care services by the deceased women and near miss cases. Result showed 4.5% women developed potentially life-threatening condition (PLTC) of which 21% women developed LTC. Maternal Near Miss (MNM) ratio was 9.46/1000 live birth and the MNM-to-Maternal Mortality ratio was 8.3:1 and the leading causes of MNM and maternal death were hemorrhage, pregnancy induced hypertension/eclampsia. The utilization of maternal health-care services revealed that there is a scope to increase the service delivery. Study finding indicates that health-care programs need to enhance the existing efforts to improve timely health seeking behavior of women.

SAR Imaging Method for Moving Target With Azimuth Missing Data

To solve the problem of the moving target SAR imaging from an incomplete echo, we proposed a novel SAR imaging method for the moving target with the azimuth missing data (MTIm-AMD). Instead of directly reconstructing the moving target image using the sparse SAR imaging method, we estimate and reconstruct the non-cooperative moving target&#x0027;s complete echo from the azimuth incomplete echo based on its sparsity. At first, the non-cooperative moving target&#x0027;s motion parameters are estimated from the incomplete echo. Then, to ensure that the complete echo can be well reconstructed using the compressed sensing method, these parameters are exploited to design a phase compensation function. Finally, using the reconstructed data, the fine-focused moving target can be obtained via the traditional SAR imaging algorithm. The simulation and experiment data results verify the effectiveness of the proposed MTIm-AMD method and demonstrate that the moving target can be fine-imaging when the echo SNR is higher than <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>20 dB and the azimuth missing ratio (AMR) is less than 70&#x0025;. This implies that the proposed MTIm-AMD method achieves satisfactory robustness to echo SNR and can handle most AMR cases.

Modification of the ALBI-PLT Score for the Prediction of High-risk Varices.

A new scoring system [albumin-bilirubin-platelet (ALBI-PLT) score] was reported for identifying cirrhotic patients without high-risk varices (HRV), and patients with ALBI grade 1 (≤-2.60) and a platelet count over 150×109/l were shown to have a low risk of having HRV. The present study modified the cut-off values of the variables in the ALBI-PLT score. Among a total of 338 patients with chronic liver diseases, possible cut-off values of the ALBI score and the platelet count were determined by analyzing the first-half group (training cohort: N=169) with the receiver operating characteristic (ROC) method. The utility of the determined values was evaluated in the second-half group (validation cohort: N=169) and total cohort (N=338). In addition, the utility of the modified cut-off values was evaluated in patients with compensated cirrhosis (cirrhotic cohort: N=87). Possible cut-off values of the ALBI score and platelet count were found to be -2.36 and 114×109/l, respectively. In the training cohort, these cut-off values provided a higher ratio of avoiding esophagogastroduodenoscopy than the original ALBI-PLT score (53.3% vs. 25.4%, p<0.01). Consistent results were observed in the validation cohort (28.4% vs. 15.4%, p<0.01), total cohort (40.8% vs. 20.4%, p<0.01), and cirrhotic cohort (32.2% vs. 11.5%, p<0.01). However, the missing ratio of patients with the HRV was not significantly increased in any cohort studied. Modification of the ALBI-PLT score may be useful for predicting patients without HRV.

MATERNAL HEALTH CARE IN TERTIARY HOSPITAL IN TERMS OF MATERNAL NEAR MISSES (MNM) INDICATORS.

Objective: To assess maternal health care by proxy indicators in terms of frequency of near misses, Maternal Near Miss (MNM) ratio and Mortality Index in a tertiary care, Lady Reading, hospital.&#x0D; &#x0D; Methodology: &#x0D; It was a Cross-sectional descriptive case study conducted in Obstetrics A unit of Lady Reading Hospital from January, 2019 to December, 2019.The WHO near miss “severe maternal complication” based inclusion criteria was used for case identification. The study population consisted of all women who were admitted during pregnancy, labor, or within the first 42 days of postpartum to our unit. Outcome was measured using the three indicators i.e. the mortality indices, maternal mortality ratio, mortality to near miss ratio, near miss cases/1000 deliveries &#x0D; &#x0D; Results:&#x0D; Total cases included in this study were 10439. The total Maternal Near Miss cases were 1776 (1.84%). Maternal Near Miss Rate MNMR was calculated as 170 per 1000 live births. There were 44 total maternal deaths, and maternal mortality rate was calculated as 421 per 100,000 live births. The Maternal Near Miss to Mortality ratio was 41:1. Mortality Index was 2.5%.&#x0D; &#x0D; Conclusion: Our hospital has a higher maternal near miss to mortality ratio of 41:1 showing good maternal care management. The WHO near miss indicators can be used in tertiary care care settings to evaluate the standard of care especially so in developing countries like Pakistan.&#x0D; &#x0D; Keywords: maternal near-miss, near miss indicators mortality index, maternal mortality.&#x0D; &#x0D;