Opportunities and challenges of using a health information system in adolescent health management:A qualitative study of healthcare providers’ perspectives in the West Bank, occupied Palestinian territory

Background: Adolescents are a critical demographic facing unique health challenges who are further impacted in humanitarian settings. This article focuses on the urgent need for a structured health information system (HIS) to address the gaps in data availability and evidence-based interventions for adolescent health. The study aims to identify opportunities and challenges in utilizing the HIS to enhance adolescent health in the West Bank by gathering insights from healthcare providers. Methods: Semi-structured key informant interviews were conducted with participants involved in the HIS regarding adolescent health in the West Bank. They were selected by purposive sampling. Nineteen interviews were conducted between July and October 2022, and thematic analysis was carried out using MAXQDA software. Results: The opportunities identified were the small-scale victories the participants described in building the HIS for adolescent health. These included institutional and individual capacity building, digitalizing parts of the HIS, connection fragmentation of adolescent health activities, multi-sectoral collaboration, reorienting services based on health information, working with limited resources, enhancing community engagement to encourage ownership and active participation, and taking strategic actions for adolescents for information. The challenges were the high workload of staff, lack of health information specialists, limited resources, lack of a unified system in data collection, lack of data on essential indicators, data quality, data sharing, and data sources and use. Conclusion: This study showed the potential of the HIS with capacity building, digitization, and collaborative initiatives; it also suffers from issues like staff shortages, non-standardized data collection, and insufficient data for essential indicators. To maximize the impact of the HIS, urgent attention to staff shortages through comprehensive training programs, standardization of data collection systems, and development of a unified core indicator list for adolescent health is recommended. Embracing these measures will allow the HIS to provide evidence-based adolescent health programs, even in resource-constrained and complex humanitarian settings

Makro İktisat Verilerinde Kayıp Verilerin Regresyona Dayalı En Yakın Komşu "Hot Deck" Yöntemi İle Tamamlanması

Ülke verileri söz konusu olduğunda araştırmacılar çok boyutlu uzaklıkları kullanan kümeleme yaklaşımlarını tercih etmektedirler. Ancak karşılıklı bağımlılık içeren değişkenler arasında kayıp verilerin tamamlanmasında regresyon yönteminin kullanılması yaygındır. Bunun sebebi değişkenler arasındaki bağımlılık yapısının bozulmamasının amaçlanmasıdır. Bu durumda her iki analiz yaklaşımının da ortak ele alınarak hem göstergeler arasındaki bağımlılığın korunması hem de ülkeler arasındaki anlamlı uzaklıkların yok edilmemesini sağlayacak yöntemler tercih edilmelidir. Çalışmada regresyona dayalı en yakın komşuluk algoritmasının kullanılmasıyla elde edilen sonuçlar diğer yöntemlerle elde edilen sonuçlarla karşılaştırılmıştır.The researchers prefer usually classification approaches which are depending on multidimensional distances to handle country data. However, it is used to impute the missing data with regression analysis at interdependent variables. The reason is to prevent the interdependency structure among variables. Therefore, there is a need for missing value imputation techniques, which will prevent the collinearity among economic variables and the significant distances among the countries. This research discussed the comparison between the results of hot-deck imputation with regression analysis and other imputation methods

Effective techniques for handling incomplete data using decision trees

Decision Trees (DTs) have been recognized as one of the most successful formalisms for knowledge representation and reasoning and are currently applied to a variety of data mining or knowledge discovery applications, particularly for classification problems. There are several efficient methods to learn a DT from data. However, these methods are often limited to the assumption that data are complete. In this thesis, some contributions to the field of machine learning and statistics that solve the problem of extracting DTs for learning and classification tasks from incomplete databases are presented. The methodology underlying the thesis blends together well-established statistical theories with the most advanced techniques for machine learning and automated reasoning with uncertainty. The first contribution is the extensive simulations which study the impact of missing data on predictive accuracy of existing DTs which can cope with missing values, when missing values are in both the training and test sets or when they are in either of the two sets. All simulations are performed under missing completely at random, missing at random and informatively missing mechanisms and for different missing data patterns and proportions. The proposal of a simple, novel, yet effective proposed procedure for training and testing using decision trees in the presence of missing data is the next contribution. Original and simple splitting criteria for attribute selection in tree building are put forward. The proposed technique is evaluated and validated in empirical tests over many real world application domains. In this work, the proposed algorithm maintains (sometimes exceeds) the outstanding accuracy of multiple imputation, especially on datasets containing mixed attributes and purely nominal attributes. Also, the proposed algorithm greatly improves in accuracy for IM data. Another major advantage of this method over multiple imputation is the important saving in computational resources due to it simplicity. The next contribution is the proposal of three versions of simple probabilistic techniques that could be used for classifying incomplete vectors using decision trees based on complete data. The proposed procedure is superficially similar to that of fractional cases but more effective. The experimental results demonstrate that these approaches can achieve comparative quality to sophisticated algorithms like multiple imputation and therefore are applicable to all kinds of datasets. Finally, novel uses of two proposed ensemble procedures for handling incomplete training and test data are proposed and discussed. The algorithms combine the two best approaches either with resampling (REMIMIA) or without resampling (EMIMIA) of the training data before growing the decision trees. Experiments are used to evaluate and validate the success of the proposed ensemble methods with respect to individual missing data techniques in the form of empirical tests. EMIMIA attains the highest overall level of prediction accuracy

Improving the Scalability of Reduct Determination in Rough Sets

Rough Set Data Analysis (RSDA) is a non-invasive data analysis approach that solely relies on the data to find patterns and decision rules. Despite its noninvasive approach and ability to generate human readable rules, classical RSDA has not been successfully used in commercial data mining and rule generating engines. The reason is its scalability. Classical RSDA slows down a great deal with the larger data sets and takes much longer times to generate the rules. This research is aimed to address the issue of scalability in rough sets by improving the performance of the attribute reduction step of the classical RSDA - which is the root cause of its slow performance. We propose to move the entire attribute reduction process into the database. We defined a new schema to store the initial data set. We then defined SOL queries on this new schema to find the attribute reducts correctly and faster than the traditional RSDA approach. We tested our technique on two typical data sets and compared our results with the traditional RSDA approach for attribute reduction. In the end we also highlighted some of the issues with our proposed approach which could lead to future research

Computational intelligence techniques for missing data imputation

Despite considerable advances in missing data imputation techniques over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions, including the Expectation Maximisation (EM), and the combination of autoassociative neural networks and genetic algorithms (NN-GA). The merits of both these techniques have been discussed at length in the literature, but have never been compared to each other. This thesis contributes to knowledge by firstly, conducting a comparative study of these two techniques.. The significance of the difference in performance of the methods is presented. Secondly, predictive analysis methods suitable for the missing data problem are presented. The predictive analysis in this problem is aimed at determining if data in question are predictable and hence, to help in choosing the estimation techniques accordingly. Thirdly, a novel treatment of missing data for online condition monitoring problems is presented. An ensemble of three autoencoders together with hybrid Genetic Algorithms (GA) and fast simulated annealing was used to approximate missing data. Several significant insights were deduced from the simulation results. It was deduced that for the problem of missing data using computational intelligence approaches, the choice of optimisation methods plays a significant role in prediction. Although, it was observed that hybrid GA and Fast Simulated Annealing (FSA) can converge to the same search space and to almost the same values they differ significantly in duration. This unique contribution has demonstrated that a particular interest has to be paid to the choice of optimisation techniques and their decision boundaries. iii Another unique contribution of this work was not only to demonstrate that a dynamic programming is applicable in the problem of missing data, but to also show that it is efficient in addressing the problem of missing data. An NN-GA model was built to impute missing data, using the principle of dynamic programing. This approach makes it possible to modularise the problem of missing data, for maximum efficiency. With the advancements in parallel computing, various modules of the problem could be solved by different processors, working together in parallel. Furthermore, a method for imputing missing data in non-stationary time series data that learns incrementally even when there is a concept drift is proposed. This method works by measuring the heteroskedasticity to detect concept drift and explores an online learning technique. New direction for research, where missing data can be estimated for nonstationary applications are opened by the introduction of this novel method. Thus, this thesis has uniquely opened the doors of research to this area. Many other methods need to be developed so that they can be compared to the unique existing approach proposed in this thesis. Another novel technique for dealing with missing data for on-line condition monitoring problem was also presented and studied. The problem of classifying in the presence of missing data was addressed, where no attempts are made to recover the missing values. The problem domain was then extended to regression. The proposed technique performs better than the NN-GA approach, both in accuracy and time efficiency during testing. The advantage of the proposed technique is that it eliminates the need for finding the best estimate of the data, and hence, saves time. Lastly, instead of using complicated techniques to estimate missing values, an imputation approach based on rough sets is explored. Empirical results obtained using both real and synthetic data are given and they provide a valuable and promising insight to the problem of missing data. The work, has significantly confirmed that rough sets can be reliable for missing data estimation in larger and real databases

Predictive Modelling Approach to Data-driven Computational Psychiatry

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry