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This study investigates severe weather events that lead to power outages. Despite extensive research on using social media during disasters, little work has focused on combining social media information with power outage data. To address this limitation, we propose a novel and effective approach to enhance the prediction accuracy of weather-related power outages by learning a spatio-temporal multiplex network that integrates information on the impact of inclement weather on the residents extracted from their social media posts with relevant weather, geographic, and grid topology data. In the multiplex network framework, the outage risk is estimated using logistic regression, neural network, support vector machine, random forest, xgboost, and decision tree classifiers through which machine learning models are applied separately on individual layers and jointly on multiplex networks representing layers capturing information related to weather, lightning, and vegetation. Experiments were conducted to predict the risk for weather-related power outages three hours in advance at a county level in five states of the Pacific Northwest region from November 2021 to April 2022. Evidence suggests that using vegetation information improves the quality of all models compared to relying on weather and lighting layers alone. Integrating an additional layer on the impact of inclement weather on the community residents retrieved from public social media posts (Twitter, Reddit) with weather, lightning, and vegetation layers improves the accuracy of outage prediction by $5\%-7\%$ . The results demonstrate that the proposed spatio-temporal multiplex network-based approach offers beneficial insights for predicting power outages three hours ahead at the county level.

Electric grid continually monitors spatiotemporal data from sparse service areas. As power systems grow and get more complex, and with the deployment of more sensors and data collection capabilities, monitoring and analyzing data streams for outage prediction will get more complicated. In addition, the burden on human operators to analyze such data is getting challenging. Furthermore, climate change introduces new challenges to power grid reliability and makes the human grid operators’ task more critical. To address some of these challenges, this research proposes a novel model to jointly predict power grid outages and discover precursors from spatiotemporal data using multi-level data. The new method utilizes multi-task learning (MTL) and multi-instance learning (MIL) to jointly predict outages and learn event precursors. This is achieved by introducing distance-aware self-attention to capture relationships between locations and improve event detection and precursor discovery while utilizing multi-level data (local weather data, global demand, and forecast data) in a sparse setting. Experiments are conducted using five years of data collected in the U.S. Pacific Northwest. The proposed methodology achieves an Area Under the Precision-Recall Curve (AU-PRC) of 0.97 using 12 hours of data before the event. Experiments showed that the proposed model could predict events several hours ahead with high accuracy, where such early predictions allow grid operators to deploy outage mitigation plans. In addition, the new framework effectively discovers spatiotemporal precursors for power outages. Grid operators can use such event precursors to help mitigate outages and improve grid reliability.

AbstractRepresentation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative representation can especially benefit the analysis of fMRI data because of the complexities and dynamic dependencies present in such datasets. In this work, we propose a framework based on transformer models to learn an embedding of the fMRI data by taking the spatiotemporal contextual information in the data into account. This approach takes the multivariate BOLD time series of the regions of the brain as well as their functional connectivity network simultaneously as the input to create a set of meaningful features that can in turn be used in various downstream tasks such as classification, feature extraction, and statistical analysis. The proposed spatiotemporal framework uses the attention mechanism as well as the graph convolution neural network to jointly inject the contextual information regarding the dynamics in time series data and their connectivity into the representation. We demonstrate the benefits of this framework by applying it to two resting-state fMRI datasets, and provide further discussion on various aspects and advantages of it over a number of other commonly adopted architectures.

Disagreement among text annotators as a part of a human (expert) labeling process produces noisy labels, which affect the performance of supervised learning algorithms for natural language processing. Using only high agreement annotations introduces another challenge: the data imbalance problem. We study this challenge within the problem of relating user comments to the content of a news article. We show that traditional techniques for learning from imbalanced data, such as oversampling, using weighted loss functions, or assigning weak labels using crowdsourcing, may not be sufficient for modeling complex temporal relationships between news articles and user comments. In this study, we propose a framework for aligning comments and articles 1) from imbalanced news data characterized with 2) different degrees of annotator agreement, under 3) a constrained budget for human labeling and computing resources. Within the framework, we propose a Semi-Automatic Labeling solution based on Human-AI collaboration. We compare our proposed technique with traditional data imbalance handling techniques and synthetic data generation on the article-comment alignment problem, where the goal is to determine a category of an article-comment pair that represents how relevant the comment is to the article. Finding an effective and efficient solution is essential because it is time-consuming and prohibitively costly to manually label a sufficiently large amount of article-comment pairs based on the semantic understanding of an article and its comments. We discover that the Human-AI collaboration outperforms all alternative techniques by 17% of article-comment alignment accuracy. When there is no time or budget for re-labeling some article-comment pairs, we found that synonym augmentation is a reasonable alternative. We also provide a detailed analysis of the effect of humans in the loop and the use of unlabeled data.

Electric power system operators monitor large multi-modal data streams from wide service areas. The current data setups stand to get more complex as utilities add more smart-grid sensors to collect additional data from power system substations and other in-situ locations. We propose a methodology to utilize multi-modal data for automated power system fault prediction, and precursor discovery that takes advantage of not only the utility owned measurements but also an abundance of data from other related databases such as weather observation systems. The process is automated to help operators analyze multi-modal data that may be impossible to process manually due to the size and variety. We automatically preprocess multi-source data and learn a joint latent representation from collocated streamed, sparse, and high-dimensional data collected from Phasor Measurement Units and external weather data. Then we utilize multi-instance learning to predict events and discover precursors simultaneously without relying on post-mortem studies of fault signatures. We apply the proposed methodology to provide early predictions of faults in the U.S. Western Interconnection. AU-ROC of 0.94 is achieved in predicting events by utilizing information 5 hours before event time using season-specific models. We show how precursors can be extracted from multi-modal data and interpreted for predicted events.

Electric grids are vulnerable to the impacts of extreme weather. Utility companies face the necessity to reduce the number of power outages caused by weather. This paper expands the approach of predicting weather outages in the distribution grid by incorporating wind modeling. The models for the grid outage State of Risk (SoR) prediction are used by utilities to mitigate potential impacts and reduce outage durations. We study the performance of such models when they are enhanced by incorporating data from Wide Area Fine Grid Wind Modeling (WAFGWM). For a given period, WAFGWM produces wind fields that characterize the direction and speed of the wind over the area of interest. The process of extracting features for the Machine Learning (ML) algorithm is described. The new solution is tested utilizing actual grid performance data from a utility company. The results from nested cross-validation obtained on three years of data reveal that the proposed method improves model performance.

Abstract Background Hemodialysis clinic patient social networks may reinforce positive and negative attitudes towards kidney transplantation. We examined whether a patient’s position within the hemodialysis clinic social network could improve machine learning classification of the patient’s positive or negative attitude towards kidney transplantation when compared to sociodemographic and clinical variables. Methods We conducted a cross-sectional social network survey of hemodialysis patients in two geographically and demographically different hemodialysis clinics. We evaluated whether machine learning logistic regression models using sociodemographic or network data best predicted the participant’s transplant attitude. Models were evaluated for accuracy, precision, recall, and F1-score. Results The 110 surveyed participants’ mean age was 60 ± 13 years old. Half (55%) identified as male, and 74% identified as Black. At facility 1, 69% of participants had a positive attitude towards transplantation whereas at facility 2, 45% of participants had a positive attitude. The machine learning logistic regression model using network data alone obtained a higher accuracy and F1 score than the sociodemographic and clinical data model (accuracy 65% ± 5% vs. 61% ± 7%, F1 score 76% ± 2% vs. 70% ± 7%). A model with a combination of both sociodemographic and network data had a higher accuracy of 74% ± 3%, and an F1-score of 81% ± 2%. Conclusion Social network data improved the machine learning algorithm’s ability to classify attitudes towards kidney transplantation, further emphasizing the importance of hemodialysis clinic social networks on attitudes towards transplant.

In the current situation of war conflicts, but also as a consequence of the COVID-19 pandemics, the economic crisis caused by the lack of goods, primarily food, energy sources, weapons and military equipment and multiple other products and services, has induced price increases and inflation. In this regard, there are substantial challenges present in the field of procurement of agricultural products. In that sense, we methodologically included analysis and synthesis, compilations, inductions and deductions of “military budgets”, GDP and some other indicators in several currently most significant countries and in the Republic of Serbia, in the context of compromised global security. The aim is to attain relevant indicators and conclusions which will provide certain guidelines for improvement of procurement of agricultural products for the needs of the defense system in the future.

Rationale & Objective: Most living kidney donors are members of a hemodialysis patient’s social network. Network members are divided into core members, those strongly connected to the patient and other members; and peripheral members, those weakly connected to the patient and other members. We identify how many hemodialysis patients’ network members offered to become kidney donors, whether these offers were from core or peripheral network members, and whose offers the patients accepted. Study Design: A cross-sectional interviewer-administered hemodialysis patient social network survey. Setting & Participants: Prevalent hemodialysis patients in 2 facilities. Predictors: Network size and constraint, a donation from a peripheral network member. Outcomes: Number of living donor offers, accepting an offer. Analytical Approach: We performed egocentric network analyses for all participants. Poisson regression models evaluated associations between network measures and number of offers. Logistic regression models determined the associations between network factors and accepting a donation offer. Results: The mean age of the 106 participants was 60 years. Forty-five percent were female, and 75% self-identified as Black. Fifty-two percent of participants received at least one living donor offer (range 1-6); 42% of the offers were from peripheral members. Participants with larger networks received more offers (incident rate ratio [IRR], 1.26; 95% CI, 1.12-1.42; P = 0.001), including networks with more peripheral members (constraint, IRR, 0.97; 95% CI, 0.96-0.98; P

Healthcare services planning and regulation involve finding patterns in hospitals admission to detect their needs in a timely manner. Admission patterns for certain diseases are more precise than a general pattern including all diseases. Towards the objective of clustering hospitals based on their monthly admission behavior for different diseases, this study investigates the similarity among multiple disease-specific hospital networks to guide a joint clustering of hospitals. In this paper, the disease super network is generated from health records data using graph matching instead of relying on biomedical literature that is used in the previous work. The health records-based disease network is constructed using more than 7 million discharge records that are extracted from the California State Inpatient Database between 2009 and 2011. Comparison of the disease network results obtained using health records of different years shows consistency in clustering structure despite temporal changes in admission data. We show that the joint clustering guided by the health records-based similarity improves clustering group homogeneity measures as compared to the clustering guided by literature-based similarity (average homogeneity 53% vs 41%, respectively). The code used to conduct this work is available at https://github.com/Nouf-Barakati/JointCLusteringofHospitals.

Event detection in electrical grids is a challenging problem for machine learning methods due to spatiotemporally nonstationary systems and the inability to automate event labeling in high-volume data such as PMU measurements. As a result, the existing historical event logs created manually do not correlate well with the corresponding PMU measurements due to scarce and temporally imprecise labels. Trying to overcome this problem by extending event logs to a complete set of labeled events is very costly and often infeasible. We focused on utilizing a transfer learning model to reduce the need for additional data labeling by leveraging some labeled data instances available from a small number of well-defined event detection task. To demonstrate the feasibility, we tested our approach on a large dataset collected by 38 PMUs from the Western Interconnection of the U.S.A. over two years. The model evaluation performed based on varying percentages of labeled source data corresponding to $\sim 20$ -700 characteristic events on different sizes of time windows ranging from 2-seconds to 1-minute demonstrates that the developed method can significantly improve automated event detection based on PMU measurements when extensive labeling is costly or impossible to obtain. When compared to the state-of-the-art machine learning algorithms (unsupervised, semi-supervised, and supervised), the results show that the transfer learning method has significantly better performances when detecting events by learning from as low as 20 representative labeled data instances.

AbstractTemporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are nonessential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networks with binary weights for extracting significant ties whose likelihood cannot be reduced to the local properties of the nodes. In this work, we propose a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting-state fMRI data with continuous weights. This framework includes a null model that estimates the latent characteristics of the distributions of temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the activities and local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a resting-state fMRI dataset, and provide further discussion on various aspects and advantages of it.

Cancer is one of the most common causes of death in the world. It is characterized by the multi-stage transformation of normal cells into tumor cells. Early cancer detection can significantly reduce its consequences, which was the objective of many machine learning (ML) published studies. However, most of them focused on microarray, gene expression, or publicly available medical datasets. Almost none offered an approach for predicting cancer through analysis of sequential data, such as Electronic Health Record (EHR) data.This paper presents a sequential ML approach to predict the occurrence of lung cancer, breast cancer, cervical cancer, and liver cell cancer using EHR data. The accuracy of sequence learning models based on long short-term memory (LSTM) and bidirectional gated recurrent units (GRU) were compared to traditional ML methods based on multilayer perceptron, random forest, decision tree, and K-nearest neighbor. The models were trained and tested on 50,606 patient hospitalization histories. Unsupervised and supervised data reduction methods (singular value decomposition (SVD) and a neural network embedding layer) were applied to overcome the challenges of high-dimensionality and sparsity of EHR data.The results provided evidence that for this application GRU outperforms alternatives based on accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), sensitivity (recall), specificity, precision, and F1 score. It was the best performing model with accuracy between 81% (breast cancer) and 88% (liver cancer) on balanced out of sample EHRs. Multilayer perceptron and LSTM manifested comparable performances (accuracies between 78% and 87%) among the alternatives, while decision tree was the worst-performing model.The findings of this study could potentially aid medical professionals in cancer diagnostics, treatment, and prevention. In particular, experiments confirmed that GRU could accurately predict cancer by learning from simplified patient representations using an embedding layer or SVD. Therefore, GRU's predictions could be used in early cancer detection, potentially improving patients' survival rates.

Introduction: Thyroid cancer represents 3.1 % of diagnosed cancers in the United States. The objective of this research was to identify comorbidities and discover additional genes potentially related to thyroid cancer and improve current knowledge of genetics and comorbidities associated with this cancer. Methods: Healthcare Cost and Utilization Project (HCUP) California State Inpatient Database (SID) was used to extract and rank the comorbidities of thyroid cancer. The text mining software - BeFree was utilized to identify and extract genes associated with thyroid cancer and the comorbidities from PubMed abstracts and the DisGeNET expert-curated repositories. Results: Female patients had 4,485, and male patients 2912 different comorbidities in early stages of thyroid cancer. Females had 3,587, and males 2817 different comorbidities in advanced stages. Through PubMed utilizing the BeFree method, 504 different genes associated with thyroid cancer were discovered, as well as five genes on DisGeNET. The most often genes on PubMed, associated with thyroid cancer were: BRAF, RET, SLC5A5, RAS, and PTEN. Genes found via DisGeNET were BRAF, RET, KRAS, NRAS, and PRKAR1A. Conclusion: Identified genes and comorbidities, as potential additional risk factors for thyroid cancer, not previously known, could improve the early diagnosis and the survival of patients with thyroid cancer. Genes discovered in this research in association with thyroid cancer could be used to direct decision making for optimal, more personalized treatment of thyroid cancer.

Background: Colorectal cancer (CRC) is the third most common cancer in the United States and the second leading cause of cancer death. The goal was to identify comorbidities and genes associated with CRC. Methods: A novel social network model was developed on the Healthcare Cost and Utilization Project (HCUP) - State Inpatient Databases (SID) California database to study comorbidities of CRC. Ranked lists of comorbidities and comorbidity networks were created, and the prevalence of comorbidities in different stages of CRC was calculated. Ranked lists of comorbidities were utilized for text mining of PubMed and DisGeNET to extract genes associated with CRC. Results: 5,786 comorbidities were identified in females and 5,607 in males in early stages and 5,609 comorbidities in females and 5,427 in males in advanced stages of CRC. Associations between 1,937 different genes and CRC were extracted from PubMed. 150 genes are associated with CRC in DisGeNET. The most mentioned genes associated with CRC were: TP53 (241 abstracts in PubMed), APC (115), and KRAS (106). These 3 genes as well as MLH1 (98) and TGFBR2 (18) had DisGeNET scores of 0.5. PPARG gene (43) had DisGeNET score of 0.6. Conclusions: The results of comorbidity network analyses suggest which comorbidities of CRC are highly expected. Discovered genes could be used to recruit more individuals who would benefit from genetic consultations. Identified associations between comorbidities, CRC, and shared genes can have important implications on early discovery, and prognosis of CRC. Prevention and treatment of discovered comorbidities would potentially lead to improved quality of life and better outcomes of CRC.

Triploidy is chromosomal abnormality characterized by the presence of three sets of chromosomes instead of the normal two sets, so the triploid fetus has 69 chromosomes. Within the Department for medical genetics at University Clinical Center of Republic of Srpska, from 2009 to 2016, 5853 prenatal karyotipizations were performed from amniotic fluid. Two cases (0,03%) of tryploid karyotipe were detected during this period. In this report we will present the findings of triploid karyotype 69, XXX in prenatal diagnosis. Origin of those triploid karyotipes was determined using molecular methods at Institute of Forensic Medicine of Republic of Srpska, Banja Luka. One of the detected triploid fetuses originated from twin pregnancy, where the pregnancy was spontaneously aborted two weeks after amniocentesis. Using molecular methods, it was determined that one set of chromosomes originated from the mother, and two sets from the father- diandric origin. The other triploid fetus originated from one fetus pregnancy, where the pregnancy was spontaneously aborted two weeks after amniocentesis. The first trimester combined test indicated triploid kariotype with digyny origin. Triploid karyotype was confirmed by prenatal karyotipisation, while molecular analysis confirmed digyny origin of this case of triploidy – two sets of chromosomes originated from mother and one set from father. Since cytogenetic analyses are performed from amniotic cells at 16-18 weeks, in order to reduce waiting period, parents should be informed about karyotypisation and DNA profiling from chorionic villi sample at 11-12 weeks as a faster option to get final results .

Triploidy is chromosomal abnormality characterized by the presence of three sets of chromosomes instead of the normal two sets, so the triploid fetus has 69 chromosomes. Within the Department for medical genetics at University Clinical Center of Republic of Srpska, from 2009 to 2016, 5853 prenatal karyotipizations were performed from amniotic fluid. Two cases (0,03%) of tryploid karyotipe were detected during this period. In this report we will present the findings of triploid karyotype 69, XXX in prenatal diagnosis. Origin of those triploid karyotipes was determined using molecular methods at Institute of Forensic Medicine of Republic of Srpska, Banja Luka. One of the detected triploid fetuses originated from twin pregnancy, where the pregnancy was spontaneously aborted two weeks after amniocentesis. Using molecular methods, it was determined that one set of chromosomes originated from the mother, and two sets from the father- diandric origin. The other triploid fetus originated from one fetus pregnancy, where the pregnancy was spontaneously aborted two weeks after amniocentesis. The first trimester combined test indicated triploid kariotype with digyny origin. Triploid karyotype was confirmed by prenatal karyotipisation, while molecular analysis confirmed digyny origin of this case of triploidy – two sets of chromosomes originated from mother and one set from father. Since cytogenetic analyses are performed from amniotic cells at 16-18 weeks, in order to reduce waiting period, parents should be informed about karyotypisation and DNA profiling from chorionic villi sample at 11-12 weeks as a faster option to get final results .

Background Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients. Methods The California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results. Results Area under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation. Discussion The results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.

Learning disease severity scores automatically from collected measurements may aid in the quality of both healthcare and scientific understanding. Some steps in that direction have been taken and machine learning algorithms for extracting scoring functions from data have been proposed. Given the rapid increase in both quantity and diversity of data measured and stored, the large amount of information is becoming one of the challenges for learning algorithms. In this work, we investigated the direction of the problem where the dimensionality of measured variables is large. Learning the severity score in such cases brings the issue of which of measured features are relevant. We have proposed a novel approach by combining desirable properties of existing formulations, which compares favorably to alternatives in accuracy and especially in the robustness of the learned scoring function. The proposed formulation has a nonsmooth penalty that induces sparsity. This problem is solved by addressing a dual formulation which is smooth and allows an efficient optimization. The proposed approach might be used as an effective and reliable tool for both scoring function learning and biomarker discovery, as demonstrated by identifying a stable set of genes related to influenza symptoms’ severity, which are enriched in immune-related processes.

Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students' fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.

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Results of testing taekwondo head team Serbia and Monte Negro may produce important information for coaches and their sportsman performance. The main object of this study was importance of detail analyses of specifi c sport activity with all details which are important for construction of the specifi c taekwon-do sport

Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755-0.771) to 0.769 (95% CI: 0.761-0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.

The topic has showed motoric tasks which have compatibility of fitting in elements of wrestling technic. Elements of wrestling technic have been mentioned as algorithms which are in compatible relationship with given motoric tasks. In this topic approach, criteria which determine specificity and compatibility of given motoric tasks with basic wrestling algorithms, have been mentioned. Conclusion finds that topics with this kind of analytical-didactic content, should cover, important field of organization in modeling system of wrestling training technique

In the topic, wrestling has been observed through medical and educational aspects. In that point of view, observation of the best wrestling characteristics has been made. Using these, we can make an influence on educational and health state within young people. With this approach the aim and tasks, which contain specific psycho-motoric space of wrestling, have been determined. This brings us to conclusion that, through determined, this specific psycho-motoric space represent start point for further analytic observation. These would recommend wrestling, to some new system of calocagatia.