Background Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs–based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. Objective This study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning–based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. Methods PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs,” “clinical deterioration,” and “machine learning.” Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. Results We identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. Conclusions In studies that compared performance, reported results suggest that machine learning–based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

Cancer cell biology takes advantage of identifying diverse cellular signaling pathways that are disrupted in cancer. Signaling pathways are an important means of communication from the exterior of cell to intracellular mediators, as well as intracellular interactions that govern diverse cellular processes. Oncogenic mutations or abnormal expression of signaling components disrupt the regulatory networks that govern cell function, thus enabling tumor cells to undergo dysregulated mitogenesis, to resist apoptosis, and to promote invasion to neighboring tissues. Unraveling of dysregulated signaling pathways may advance the understanding of tumor pathophysiology and lead to the improvement of targeted tumor therapy. In this review article, different signaling pathways and how their dysregulation contributes to the development of tumors have been discussed.

BACKGROUND Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively and preventing adverse outcomes. Vital signs-based aggregate-weighted Early Warning Systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. OBJECTIVE To identify, summarize, and evaluate the available research, current state of utility and challenges with machine learning based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. METHODS PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs”, “clinical deterioration”, and “machine learning”. Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. RESULTS 24 peer-reviewed studies were identified for inclusion from 417 articles. 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, ICUs, emergency departments, step-down units, medical assessment units, post-anesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. CONCLUSIONS In studies that compared performance, reported results suggest that machine learning based early warning systems can achieve greater accuracy than aggregate weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.