Integration of Machine Learning and Blockchain Technology in the Healthcare Field: A Literature Review and Implications for Cancer Care

Impact of Tumor Location on Predicting Early-Stage Breast Cancer Patient Survivability Using Explainable Machine Learning Models

PURPOSE

This study aims to investigate the impact of tumor quadrant location on the 5-year early-stage breast cancer survivability prediction using explainable machine learning (ML) models. By integrating these predictive models with Shapley Additive Explanations (SHAP), feature importance, and coefficient effect size, we aim to provide insights into the significant factors influencing patient outcomes.

METHODS

Data from 401 early-stage patients with breast cancer at the University of Missouri's Ellis Fischel Cancer Center were used, encompassing 20 variables related to demographics, tumor characteristics, and therapeutics. Six ML models, namely, Xtreme Gradient Boosting, Random Forest classifier, Logistic Regression, Decision Tree classifier (DT), Support Vector Machine classifier, and AdaBoost (ADB), were trained and evaluated using various performance metrics, including accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR). Feature importance, coefficient effect size, and SHAP values were used to interpret and visualize the importance of different features, particularly focusing on tumor quadrant variables.

RESULTS

The extreme gradient boosting model outperformed other models, achieving an AUC-ROC score of 0.98 and an AUC-PR score of 0.97. The analysis revealed that tumor quadrant variables, especially the upper outer and miscellaneous or overlapping sites, were among the top predictive features for breast cancer survivability. SHAP analysis further highlighted the significance of these tumor locations in influencing survival outcomes.

CONCLUSION

This study demonstrates the efficacy of explainable ML models in predicting 5-year early-stage breast cancer survivability and identifies tumor quadrant location as an independent prognostic factor. The use of SHAP values provides a clear interpretation of the model's predictions, offering valuable insights for clinicians to refine treatment protocols and improve patient outcomes.

Artificial Intelligence for Neuroimaging in Pediatric Cancer

BACKGROUND/OBJECTIVES

Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pediatric population.

METHODS

A comprehensive literature review was conducted, focusing on AI's impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. Challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models were analyzed.

RESULTS

AI has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging, resulting in improved accuracy in tumor segmentation and outcome prediction for treatment. However, progress is hindered by the scarcity of pediatric datasets, issues with data sharing, and the ethical implications of applying AI in vulnerable populations.

CONCLUSIONS

To overcome current limitations, future research should focus on building robust pediatric datasets, fostering multi-institutional collaborations for data sharing, and developing interpretable AI models that align with clinical practice and ethical standards. These efforts are essential in harnessing the full potential of AI in pediatric neuroimaging and improving outcomes for children with cancer.

Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant's approach

BACKGROUND

In recent years, increased attention has been given to using deep learning (DL) of artificial intelligence (AI) in healthcare to address nursing challenges. The adoption of new technologies in nursing needs to be improved, and AI in nursing is still in its early stages. However, the current literature needs more clarity, which affects clinical practice, research, and theory development. This study aimed to clarify the meaning of deep learning and identify the defining attributes of artificial intelligence within nursing.

METHODS

We conducted a concept analysis of the deep learning of AI in nursing care using Walker and Avant's 8-step approach. Our search strategy employed Boolean techniques and MeSH terms across databases, including BMC, CINAHL, ClinicalKey for Nursing, Embase, Ovid, Scopus, SpringerLink and Spinger Nature, ProQuest, PubMed, and Web of Science. By focusing on relevant keywords in titles and abstracts from articles published between 2018 and 2024, we initially found 571 sources.

RESULTS

Thirty-seven articles that met the inclusion criteria were analyzed in this study. The attributes of evidence included four themes: focus and immersion, coding and understanding, arranging layers and algorithms, and implementing within the process of use cases to modify recommendations. Antecedents, unclear systems and communication, insufficient data management knowledge and support, and compound challenges can lead to suffering and risky caregiving tasks. Applying deep learning techniques enables nurses to simulate scenarios, predict outcomes, and plan care more precisely. Embracing deep learning equipment allows nurses to make better decisions. It empowers them with enhanced knowledge while ensuring adequate support and resources essential for caregiver and patient well-being. Access to necessary equipment is vital for high-quality home healthcare.

CONCLUSION

This study provides a clearer understanding of the use of deep learning in nursing and its implications for nursing practice. Future research should focus on exploring the impact of deep learning on healthcare operations management through quantitative and qualitative studies. Additionally, developing a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.

Blockchain integration in healthcare: a comprehensive investigation of use cases, performance issues, and mitigation strategies

Healthcare is a critical area where blockchain technology (BT) is being heralded as a potential game-changer for facilitating secure and efficient data sharing. The purpose of this review is to examine BT applications, performance challenges, and solutions in healthcare. To begin, This review paper explores popular blockchain networks for data exchange, encompassing both public and permissioned platforms, such as Ethereum and Hyperledger Fabric. This paper analyzes the potential applications of BT's decentralized, immutable, and smart contract capabilities in healthcare settings, including secure and interoperable health data exchange, patient consent management, drug supply chain oversight, and clinical trial management. The healthcare industry might greatly benefit from the increased privacy, transparency, and accessibility that these technologies provide. Despite BT's promising medical uses, the technology is not without its drawbacks. High energy consumption, throughput, and scalability are all concerns. We wrapped up by discussing the solutions that have been implemented, including consensus processes, scalability measures like sharding, and off-chain transactions that are designed to mitigate the drawbacks.

Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China

Objective

The aim of this study was to develop and validate a series of breast cancer-related lymphoedema risk prediction models using machine learning algorithms for early identification of high-risk individuals to reduce the incidence of postoperative breast cancer lymphoedema.

Methods

This was a retrospective study conducted from January 2012 to July 2022 in a tertiary oncology hospital. Subsequent to the collection of clinical data, variables with predictive capacity for breast cancer-related lymphoedema (BCRL) were subjected to scrutiny utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) technique. The entire dataset underwent a randomized partition into training and test subsets, adhering to a 7:3 distribution. Nine classification models were developed, and the model performance was evaluated based on accuracy, sensitivity, specificity, recall, precision, F-score, and area under curve (AUC) of the ROC curve. Ultimately, the selection of the optimal model hinged upon the AUC value. Grid search and 10-fold cross-validation was used to determine the best parameter setting for each algorithm.

Results

A total of 670 patients were investigated, of which 469 were in the modeling group and 201 in the validation group. A total of 174 had BCRL (25.97%). The LASSO regression model screened for the 13 features most valuable in predicting BCRL. The range of each metric in the test set for the nine models was, in order: accuracy (0.75-0.84), sensitivity (0.50-0.79), specificity (0.79-0.93), recall (0.50-0.79), precision (0.51-0.70), F score (0.56-0.69), and AUC value (0.71-0.87). Overall, LR achieved the best performance in terms of accuracy (0.81), precision (0.60), sensitivity (0.79), specificity (0.82), recall (0.79), F-score (0.68), and AUC value (0.87) for predicting BCRL.

Conclusion

The study established that the constructed logistic regression (LR) model exhibits a more favorable amalgamation of accuracy, sensitivity, specificity, recall, and AUC value. This configuration adeptly discerns patients who are at an elevated risk of BCRL. Consequently, this precise identification equips nurses with the means to undertake timely and tailored interventions, thus averting the onset of BCRL.

Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health

There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.

Effectiveness of Nonpharmacologic Interventions for Chemotherapy-Related Cognitive Impairment in Breast Cancer Patients: A Systematic Review and Network Meta-analysis

BACKGROUND

Neurotoxicity is a major adverse effect of chemotherapy in breast cancer (BC) patients. A number of nonpharmacologic interventions are used to alleviate chemotherapy-related cognitive impairment (CRCI), but no studies have compared their effectiveness.

OBJECTIVES

The aim of this study was to identify and compare the effectiveness of different nonpharmacologic interventions for CRCI in BC patients.

METHODS

A systematic review and network meta-analysis was conducted following the Cochrane guidelines. All randomized controlled trials were searched in the Cochrane Library, PubMed, MEDLINE (via OVID), Web of Science, EMBASE, and CINAHL databases from inception to September 2021. Studies using nonpharmacologic interventions to manage CRCI symptoms were included. A network meta-analysis and a comparative effects ranking were completed by STATA v14.0.

RESULTS

Twelve studies with 8 nonpharmacologic interventions were included. For subjective outcomes on CRCI, there was no significant difference between nonpharmacologic interventions. For objective outcomes, qigong and exercise were more effective than the psychotherapy. Qigong and exercise were also more effective than music therapy. The top 3 interventions were psychotherapy (83.4%), music therapy (60.8%), and electroacupuncture (52.5%) for subjective outcomes and qigong (87.7%), exercise (82.1%), and electroacupuncture (70.3%) for objective outcomes.

CONCLUSION

In the subjective evaluation, it was difficult to judge which interventions are best, but psychotherapy had the greatest probability. For objective evaluation, qigong and exercise may be the best nonpharmacologic interventions.

IMPLICATIONS FOR PRACTICE

This study provides evidence for the effectiveness of nonpharmacologic interventions for CRCI in BC patients and facilitates support for future clinical trials and work.

AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions

Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.

Meta-analysis of neurocognitive rehabilitation for cognitive dysfunction among pediatric cancer survivors

Cancer and its treatment significantly affect the cognitive functioning of pediatric cancer survivors. This meta-analysis aimed to examine the effects of neurocognitive rehabilitation interventions on the cognitive functioning and intellectual performance of pediatric cancer survivors. Four databases were searched until December 15, 2021. RevMan 5.4 was used to analyze the effects of neurocognitive rehabilitation interventions on the cognitive functioning of pediatric cancer survivors. Ten eligible randomized controlled trials were initially identified, and nine of these were included in the meta-analysis. For the working memory outcome, the pooled effect results favored study interventions and had statistical significance at postintervention assessment (Z = 2.24, P = 0.03). For the attention outcome, there were significant statistical differences at postintervention and 3/6-month follow-up assessment (Z = 2.72, P = 0.007 and Z = 10.45, P < 0.001, respectively). For the executive functioning outcome, there were significant statistical differences at postintervention and 3/6-month follow-up assessment (Z = 2.90, P = 0.004 and Z = 14.75, P < 0.001, respectively). For the academic/intellectual performance secondary outcome, the pooled overall effects of study interventions on the academic/intellectual outcome were positive at postintervention and follow-up assessment (Ps < 0.001). No studies reported any adverse events related to neurocognitive and educational interventions. This meta-analysis found that neurocognitive rehabilitation interventions improve the working memory, attention, and executive functioning of pediatric cancer survivors at postintervention and short-term follow-up. Neurocognitive rehabilitation also has positive effects on the academic/intellectual performance of this study population during a vulnerable period in their development.

Neuropsychological Interventions for Cancer-Related Cognitive Impairment: A Network Meta-Analysis of Randomized Controlled Trials

The aim of this network meta-analysis was to evaluate the comparative effects of neuropsychological interventions for cancer-related cognitive impairment (CRCI), and to rank the best intervention options for adult cancer patients with CRCI. Twenty-seven eligible randomized controlled trials (RCTs) were searched, and a total of six interventions identified: cognitive behavioral therapies (CBT), cognitive rehabilitation (CR), cognitive training (CT), meditation/mindfulness-based interventions, psychoeducation, and supportive care. In terms of effectiveness, the relative effect size of CBT, CR, and CT in managing subjective cognition had statistically significant differences - 0.94 (0.43-1.44), 0.54 (0.03-1.05), and 0.47 (0.13-0.81), respectively. The most effective interventions to manage the objective cognition of attention were meditation or mindfulness-based interventions: intervention effect size was 0.58 (0.24-0.91). The relative effect size of CT had a statistically significant difference in managing verbal memory, and the intervention effect size was 1.16 (0.12-2.20). The relative effect size of psychoeducation in managing executive function compared with control had a statistically significant difference, which was 0.56 (0.26-0.86). For managing information processing speed, the most effective intervention was CT and the effect size was -0.58 (-1.09--0.06). This network meta-analysis found that CT is the most effective intervention for managing the objective cognition of verbal memory and processing speed; meditation/mindfulness-based interventions may be the best option for enhancing attention; psychoeducation is the most effective intervention for managing executive function; CT may be the best option for managing verbal fluency as the intervention ranking probability. For the management of subjective cognition, CBT may be the most effective intervention.

Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women

Objective

Predictive models for the occurrence of cancer symptoms by using machine learning (ML) algorithms could be used to aid clinical decision-making in order to enhance the quality of cancer care. This study aimed to develop and validate a selection of classification models that used ML algorithms to predict the occurrence of breast cancer-related lymphedema (BCRL) among Chinese women.

Methods

This was a retrospective cohort study of consecutive cases that had been diagnosed with breast cancer, stages I-IV. Forty-eight variables were grouped into five feature sets. Five classification models with ML algorithms were developed, and the models' performance and the variables’ relative importance were assessed accordingly.

Results

Of 370 eligible female participants, 91 had BCRL (24.6%). The mean age of this study sample was 49.89 (SD ​= ​7.45). All participants had had breast cancer surgery, and more than half of them had had a modified radical mastectomy (n ​= ​206, 55.5%). The mean follow-up time after breast cancer surgery was 28.73 months (SD ​= ​11.71). Most of the tumors were either stage I (n ​= ​49, 31.2%) or stage II (n ​= ​252, 68.1%). More than half of the sample had had postoperative chemotherapy (n ​= ​227, 61.4%). Overall, the logistic regression model achieved the best performance in terms of accuracy (91.6%), precision (82.1%), and recall (91.4%) for BCRL. Although this study included 48 predicting variables, we found that the five models required only 22 variables to achieve predictive performance. The most important variable was the number of positive lymph nodes, followed in descending order by the BCRL occurring on the same side as the surgery, a history of sentinel lymph node biopsy, a dietary preference for meat and fried food, and an exercise frequency of less than three times per week. These factors were the most influential predictors for enhancing the ML models’ performance.

Conclusions

This study found that in the ML training dataset, the multilayer perceptron model and the logistic regression model were the best discrimination models for predicting the outcome of BCRL, and the k-nearest neighbors and support vector machine models demonstrated good calibration performance in the ML validation dataset. Future research will need to use large-sample datasets to establish a more robust ML model for predicting BCRL deeply and reliably.