To tackle this challenge, cognitive computing in healthcare acts like a medical prodigy, proactively anticipating diseases and illnesses in individuals and providing doctors with pertinent technological data for appropriate responses. The present and future technological trends in cognitive computing, as they apply to healthcare, are the subject of this review article. We examine several cognitive computing applications and present the top choice for medical practitioners in this work. This proposed method enables clinicians to meticulously monitor and analyze the patients' physical health indicators.
The current state of the literature concerning the multiple facets of cognitive computing in the healthcare field is meticulously reviewed in this article. Seven major online databases (SCOPUS, IEEE Xplore, Google Scholar, DBLP, Web of Science, Springer, and PubMed) were systematically scrutinized to compile all published articles on cognitive computing in healthcare from 2014 to 2021. A total of 75 articles were selected for examination, and their respective advantages and disadvantages were assessed. The analysis methodology was consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
The central discoveries of this review article, and their impact on both theory and practice, are mind maps illustrating cognitive computing platforms, cognitive healthcare applications, and healthcare use cases of cognitive computing. A section dedicated to a detailed discussion of current healthcare challenges, future research paths, and recent implementations of cognitive computing. The findings from an accuracy analysis of distinct cognitive systems, notably the Medical Sieve and Watson for Oncology (WFO), reveal the Medical Sieve achieving 0.95 and Watson for Oncology (WFO) achieving 0.93, signifying their preeminence in healthcare computing systems.
Evolving healthcare technology, cognitive computing, enhances clinical reasoning, allowing doctors to make accurate diagnoses and maintain optimal patient well-being. The systems' ability to provide timely, optimal, and cost-effective care is noteworthy. The article offers an exhaustive analysis of cognitive computing within the health sector, showcasing the various platforms, methods, tools, algorithms, applications, and examples of its use. This survey investigates relevant literature on current healthcare issues, and proposes prospective research directions for incorporating cognitive systems.
Cognitive computing, an innovative healthcare technology, facilitates enhanced clinical thinking, empowering doctors to achieve accurate diagnoses and maintain patients' health at optimal levels. These systems deliver timely, optimal, and cost-effective care. Cognitive computing's importance in healthcare is evaluated in this article, including in-depth analyses of platforms, techniques, tools, algorithms, applications, and practical examples. By examining existing literature regarding contemporary issues, this survey also identifies prospective research directions for the implementation of cognitive systems in healthcare.
800 women and 6700 newborns tragically lose their lives every day from complications stemming from pregnancy and childbirth. The substantial impact of a well-versed midwife is seen in the prevention of many maternal and newborn fatalities. Data science models, coupled with user-generated logs from online midwifery learning platforms, can contribute to improved learning competencies for midwives. We utilize several forecasting approaches to evaluate the future user interest in diverse content types available within the Safe Delivery App, a digital training resource for skilled birth attendants, categorized by profession and geographic location. This initial attempt at forecasting the demand for health content in midwifery learning, employing DeepAR, demonstrates the model's capacity to accurately anticipate operational needs. This accuracy opens possibilities for tailored learning resources and adaptable learning pathways.
Emerging research suggests that atypical changes in driving behavior may be indicative of early-stage mild cognitive impairment (MCI) and dementia. Despite their value, these studies are hampered by the small sample sizes and brevity of their follow-up durations. An interaction-based classification system for predicting mild cognitive impairment (MCI) and dementia, based on the Influence Score (i.e., I-score), is the focus of this study. Data used is from the Longitudinal Research on Aging Drivers (LongROAD) project, using naturalistic driving data. Data on naturalistic driving trajectories, collected from 2977 participants who were cognitively healthy at enrollment, was obtained using in-vehicle recording devices, and the collection extended up to 44 months. Subsequent processing and aggregation of these data resulted in 31 distinct time-series driving variables. Considering the significant dimensionality of time-series driving variables, the I-score method was applied in the variable selection process. Demonstrating its proficiency in distinguishing between noisy and predictive variables in substantial datasets, I-score acts as a measure for evaluating variable predictive ability. Influential variable modules or groups, exhibiting compound interactions among explanatory variables, are identified here. It is possible to elucidate how much variables and their interactions affect a classifier's predictive capabilities. https://www.selleckchem.com/products/ca77-1.html Moreover, the I-score's impact on the performance of classifiers trained on imbalanced data sets is linked to its relationship with the F1 score. With predictive variables selected by the I-score, interaction-based residual blocks are constructed atop I-score modules, generating predictors. The final prediction of the overall classifier is then fortified by the aggregation of these predictors using ensemble learning methods. In naturalistic driving studies, our classification method achieved the top accuracy (96%) in predicting MCI and dementia, outpacing random forest (93%) and logistic regression (88%). The proposed classifier exhibited an F1 score of 98% and an AUC of 87%, significantly outperforming random forest (96% F1, 79% AUC) and logistic regression (92% F1, 77% AUC). The data indicates a substantial potential for enhancing predictive capabilities regarding MCI and dementia in older motorists by integrating the I-score into machine learning algorithms. The feature importance analysis indicated that the right-to-left turning ratio and the number of hard braking events emerged as the most significant driving factors for predicting MCI and dementia.
Decades of image texture analysis have paved the way for a promising area of study in cancer assessment and disease progression evaluation, which has led to the development of radiomics. Nonetheless, the path toward fully integrating translation into clinical settings remains constrained by inherent limitations. The employment of distant supervision, particularly the use of survival/recurrence information, can potentially bolster cancer subtyping methods in overcoming the limitations of purely supervised classification models regarding the development of robust imaging-based prognostic biomarkers. This work involved assessing, testing, and validating the domain-generalizability of our previously developed Distant Supervised Cancer Subtyping model, utilizing Hodgkin Lymphoma as a case study. The model's performance is evaluated on two separate hospital data sets; results are then compared and scrutinized. While demonstrating consistent success, the comparative analysis underscored the unreliability of radiomics, attributable to a lack of reproducibility between different centers, yielding clear results in one location but presenting difficulties in interpreting findings in the other. We, therefore, suggest a Random Forest-based Explainable Transfer Model for verifying the domain generality of imaging biomarkers from historical cancer subtyping. By examining the predictive power of cancer subtyping within both validation and prospective settings, we obtained successful results, underscoring the broad applicability of our proposed framework. https://www.selleckchem.com/products/ca77-1.html However, the development of decision rules enables the determination of risk factors and reliable biomarkers, ultimately informing clinical decision-making. Further evaluation in larger, multi-center datasets is necessary to fully realize the potential of the Distant Supervised Cancer Subtyping model for reliably translating radiomics into medical practice, as suggested by this work. At this GitHub repository, the code is accessible.
We examine human-AI collaboration protocols in this paper, a design-centric model for understanding and evaluating the potential for human-AI cooperation in cognitive endeavors. Two user studies utilizing this construct, comprising 12 specialist knee MRI radiologists and 44 ECG readers with varying expertise (ECG study), evaluated a total of 240 and 20 cases, respectively, in diverse collaboration configurations. Despite the utility of AI support, we've encountered a potential 'white box' paradox with XAI, which can result in a null effect or negative consequences. Our analysis reveals that the order of presentation matters critically. AI-led protocols achieve higher diagnostic accuracy than human-led ones and outperform both the isolated accuracy of humans and AI working alone. Our results indicate the ideal conditions that facilitate AI's augmentation of human diagnostic proficiency, averting the generation of maladaptive reactions and cognitive biases that compromise decision-making effectiveness.
Bacteria are increasingly resisting antibiotics, leading to a significant decline in their ability to treat common infections. https://www.selleckchem.com/products/ca77-1.html Hospital intensive care units (ICUs) with resistant pathogens present within their environment, unfortunately, increase the risk of admission-acquired infections. This research investigates the prediction of antibiotic resistance in Pseudomonas aeruginosa nosocomial infections within the Intensive Care Unit (ICU), utilizing Long Short-Term Memory (LSTM) artificial neural networks as the predictive approach.