Integrative Approaches in Healthcare: The Convergence of Biology, Medicine, and Technology

The technological environment, difficulties, as well as innovations related to the incorporation of digital health technologies into healthcare systems are examined in this study. Using a descriptive design alongside a deductive methodology, interpretivism as a philosophy is applied to the analysis of secondary data from various sources. The part on the technical landscape describes the complex network of hardware, software, and other components that enable integration. Interoperability issues worries about data security, as well as stakeholder resistance are among the difficulties. Interoperability standards, cybersecurity protocols, and cooperative platforms are examples of innovations. Improved healthcare workflows, heightened cybersecurity, and increased interoperability are highlighted as technical outcomes. Performance metrics evaluate system uptime, dependability, and the efficiency of data exchange. Suggestions emphasize the necessity of user-centric design, evolving cybersecurity measures, in addition to universal standards. Future research should concentrate on scalability, emerging technologies, and real-time assessments

Differential evolution algorithm with strategy adaptation for global numerical optimization

Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants

Oblique and rotation double random forest

Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques (Tikhonov regularization, axis-parallel split regularization, Null space regularization) are employed for tackling the small sample size problems in the decision trees of oblique double random forest. The proposed ensembles of decision trees produce trees with bigger size compared to the standard ensembles of decision trees as bagging is used at each non-leaf node which results in improved performance. The evaluation of the baseline models and the proposed oblique and rotation double random forest models is performed on benchmark 121 UCI datasets and real-world fisheries datasets. Both statistical analysis and the experimental results demonstrate the efficacy of the proposed oblique and rotation double random forest models compared to the baseline models on the benchmark datasets.This work is supported by Science and Engineering Research Board (SERB), Government of India under Ramanujan Fellowship Scheme, Grant No. SB/S2/RJN-001/2016 , and Department of Science and Technology under Interdisciplinary Cyber Physical Systems (ICPS) Scheme grant no. DST/ICPS/CPS-Individual/2018/276 . We gratefully acknowledge the Indian Institute of Technology Indore for providing facilities and support

Extensive Superior Vena Caval Territory Thrombosis and Pulmonary Embolism: A Rare Clinical Entity of Haemorrhagic Pancreatitis

Acute haemorrhagic pancreatitis is a severe form of pancreatitis often encountered in ethanol abuse. Extensive venous thrombosis resulting in pulmonary embolism is a rare presenting clinical entity of acute haemorrhagic pancreatitis. Here, we report a young male with an extensive deep vein thrombosis involving superior vena caval territory associated with haemorrhagic pancreatitis presented with pulmonary embolism managed supportively. Prompt recognition and appropriate intervention of this rare complication would improve the outcome in patients with acute pancreatitis