To resolve this problem, cognitive computing in healthcare serves as a medical prodigy, anticipating the health issues of human beings and providing doctors with technological insights for swift action. The present and future technological trends in cognitive computing, as they apply to healthcare, are the subject of this review article. Different cognitive computing applications are reviewed in this work, and a particular application is presented as the most suitable for clinical use. In light of this guidance, the healthcare providers are equipped to closely watch and analyze the physical health of their patients.
This work synthesizes the existing literature on the diverse applications and implications of cognitive computing in healthcare. Published articles concerning cognitive computing in healthcare, spanning the period from 2014 to 2021, were gathered from nearly seven online databases, including SCOPUS, IEEE Xplore, Google Scholar, DBLP, Web of Science, Springer, and PubMed. A total of 75 articles were chosen for detailed review; their strengths and weaknesses were subsequently considered. The analysis process fully adhered to the principles outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Mind maps, which encapsulate the review article's core findings and their influence on theory and practice, describe cognitive computing platforms, demonstrate cognitive applications in healthcare, and exemplify cognitive computing use cases in healthcare. A segment exploring in-depth current problems, future research strategies, and recent applications of cognitive computing methods in healthcare. After analyzing various cognitive systems, the Medical Sieve demonstrated an accuracy of 0.95 and Watson for Oncology (WFO) demonstrated an accuracy of 0.93, solidifying their position as prominent healthcare computing systems.
Clinical thought processes are enhanced through the use of cognitive computing, a growing healthcare technology, enabling doctors to make correct diagnoses and maintain patient health. Treatment, both timely and optimal, is a hallmark of these cost-effective systems. This article investigates the impact of cognitive computing on healthcare, examining the relevant platforms, approaches, tools, algorithms, applications, and diverse examples of implementation. This survey examines the literature concerning contemporary healthcare problems and proposes promising directions for future research involving cognitive systems.
The burgeoning field of cognitive computing in healthcare augments the clinical decision-making process, equipping physicians to make the correct diagnoses and ensure the well-being of their patients. These systems deliver timely, optimal, and cost-effective care. Through detailed analyses of platforms, techniques, tools, algorithms, applications, and use cases, this article explores the significance of cognitive computing within the health sector. The present survey examines pertinent literature on current concerns, and suggests future directions for research on the application of cognitive systems within healthcare.
The devastating impact of complications in pregnancy and childbirth is underscored by the daily loss of 800 women and 6700 newborns. The substantial impact of a well-versed midwife is seen in the prevention of many maternal and newborn fatalities. Midwives' learning competencies can be strengthened by integrating user logs from online learning applications with data science models. 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 pilot study of health content demand forecasting for midwifery training highlights DeepAR's capacity for accurate prediction of content demand in operational settings, suggesting its potential for personalized content delivery and adaptive learning experiences.
A review of current studies indicates that alterations in the manner in which one drives could be early markers of mild cognitive impairment (MCI) and dementia. These studies, nonetheless, have limitations stemming from the small sample sizes and the short period of follow-up. 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. Cognitively sound participants, numbering 2977, had their naturalistic driving trajectories documented by in-vehicle recording devices, spanning up to 44 months of data collection. Subsequent processing and aggregation of these data resulted in 31 distinct time-series driving variables. The substantial dimensionality of time-series data concerning driving variables prompted our use of the I-score method in variable selection. To evaluate the predictive capacity of variables, the I-score provides a measure, proven successful in distinguishing between noisy and predictive variables in large datasets. This introduction targets variable modules or groups with significant influence and that consider complex interactions among explanatory variables. It is possible to elucidate how much variables and their interactions affect a classifier's predictive capabilities. selleck kinase inhibitor Classifiers trained on imbalanced datasets see boosted performance, thanks to the I-score's relationship with the F1 score. Employing I-score-selected predictive variables, interaction-based residual blocks are built atop I-score modules. These blocks generate predictors, which are then combined by ensemble learning, thereby boosting the overall classifier's predictive capability. Naturalistic driving data experiments showcase that our classification method achieves the peak accuracy of 96% in predicting MCI and dementia, outperforming random forest (93%) and logistic regression (88%). Our classifier demonstrated high accuracy, achieving F1 and AUC scores of 98% and 87%, respectively. Random forest followed with 96% and 79%, while logistic regression showed 92% and 77%. Improved model performance in predicting MCI and dementia among older drivers is suggested by the results, which show that the inclusion of I-score is essential. 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.
The field of image texture analysis has been a significant contributor to radiomics, a discipline that has developed to allow for promising assessment of cancer and disease progression over many years. Despite this, the way to fully incorporate translation into clinical procedures is still impeded by inherent limitations. Supervised classification models' limitations in creating robust imaging-based prognostic biomarkers underscore the need for cancer subtyping approaches incorporating distant supervision, such as leveraging survival or recurrence data. The domain-generality of our previously presented Distant Supervised Cancer Subtyping model for Hodgkin Lymphoma was assessed, tested, and validated in this investigation. The model's performance is evaluated by analyzing data from two independent hospitals, followed by a comparative analysis of the results. Although demonstrably successful and consistent, the comparison revealed the vulnerability of radiomics to variability in reproducibility across centers, resulting in straightforward conclusions in one center and ambiguous outcomes in the other. We consequently propose an Explainable Transfer Model, employing Random Forests, for examining the domain invariance of imaging biomarkers extracted from historical cancer subtyping. We evaluated the predictive capability of cancer subtyping in a validation and prospective study, obtaining positive results and thus establishing the wide-ranging applicability of the proposed method. selleck kinase inhibitor In contrast, the extraction of decision rules provides a means for pinpointing risk factors and robust biomarkers, ultimately influencing clinical choices. This work presents a Distant Supervised Cancer Subtyping model with potential; however, its dependable clinical translation of radiomic findings hinges on further evaluation within larger, multi-center data sets. The code is hosted and available on this GitHub repository.
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. We employed this construct across two user studies: one with 12 specialist knee MRI radiologists and another with 44 ECG readers of varying expertise, respectively evaluating 240 and 20 cases in distinct collaboration configurations. We uphold the practical use of AI assistance, but our analysis indicates a potential 'white box' paradox in XAI, leading to either a null result or a harmful consequence. Presentation order is a critical factor. AI-driven protocols demonstrate superior diagnostic accuracy compared to human-led protocols, exceeding the precision of both humans and AI working in isolation. We've ascertained the optimal circumstances under which AI augments human diagnostic capabilities, rather than instigating inappropriate responses and cognitive biases that diminish the quality of decisions.
An alarming increase in bacterial resistance to antibiotics is reducing their effectiveness, impacting the treatment of even the most common infections. selleck kinase inhibitor Hospital intensive care units (ICUs) are unfortunately prone to harboring resistant pathogens, thereby increasing the severity of infections patients develop while hospitalized. 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.