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Frontiers in Medicine 2024The German health and care system is transforming due to advancing digitalization. New technological applications in nursing, such as social and assistance robotics,... (Review)
Review
INTRODUCTION
The German health and care system is transforming due to advancing digitalization. New technological applications in nursing, such as social and assistance robotics, artificial intelligence and legal framework conditions are increasingly focused in numerous research projects. However, the approaches to digitalization in nursing are very different. When integrating technologies such as robotics and artificial intelligence into nursing, it is particularly important to ensure that ethical and human aspects are taken into account. A structured classification of the development of digitalization in nursing care is currently hardly possible. In order to be able to adequately deal with this digital transformation, the acquisition of digital competences in nursing education programs is pivotal. These include the confident, critical and creative use of information and communication technologies in a private and professional context. This paper focuses on the question which specific training offers already exist at national and international level for nursing professions to acquire digital competences.
METHODS
A scoping review according to the PRISMA scheme was conducted in the PubMed and CINAHL databases. The search period for the scoping review extended from 2017 to 2024.
RESULTS
The selection of the studies took place by inclusion and exclusion criteria and the content-related orientation of the publications. After reviewing the titles and abstracts, eight studies were included. Of these, four were published in German-speaking countries and another four in international English-language journals.
DISCUSSION
The topic of digitization of the nursing professions and the question of how nurses can acquire digital competences is gaining international attention. Nevertheless, the research on explicit continuing education programs for nursing professions is still undifferentiated. No specific continuing education offer for the development of digital competences of nursing professionals was identified. Many authors remained at the meta-level when developing methodological concepts for the acquisition of digital competences. The systematic integration of digitalization into higher education and continuing vocational training is mentioned in the publications. The development of theory- and research-based educational frameworks, which can be used as a basis for curricula in nursing studies and continuing education, is highly recommendable.
PubMed: 38947234
DOI: 10.3389/fmed.2024.1358398 -
Meta-radiology Sep 2024Fairness of artificial intelligence and machine learning models, often caused by imbalanced datasets, has long been a concern. While many efforts aim to minimize model...
Fairness of artificial intelligence and machine learning models, often caused by imbalanced datasets, has long been a concern. While many efforts aim to minimize model bias, this study suggests that traditional fairness evaluation methods may be biased, highlighting the need for a proper evaluation scheme with multiple evaluation metrics due to varying results under different criteria. Moreover, the limited data size of minority groups introduces significant data uncertainty, which can undermine the judgement of fairness. This paper introduces an innovative evaluation approach that estimates data uncertainty in minority groups through bootstrapping from majority groups for a more objective statistical assessment. Extensive experiments reveal that traditional evaluation methods might have drawn inaccurate conclusions about model fairness. The proposed method delivers an unbiased fairness assessment by adeptly addressing the inherent complications of model evaluation on imbalanced datasets. The results show that such comprehensive evaluation can provide more confidence when adopting those models.
PubMed: 38947177
DOI: 10.1016/j.metrad.2024.100084 -
Research Square Jun 2024Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only....
AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis.
Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths. We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score. During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342). In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.
PubMed: 38947043
DOI: 10.21203/rs.3.rs-4433105/v1 -
Frontiers in Big Data 2024Hyperdimensional Computing (HDC) is a brain-inspired and lightweight machine learning method. It has received significant attention in the literature as a candidate to...
INTRODUCTION
Hyperdimensional Computing (HDC) is a brain-inspired and lightweight machine learning method. It has received significant attention in the literature as a candidate to be applied in the wearable Internet of Things, near-sensor artificial intelligence applications, and on-device processing. HDC is computationally less complex than traditional deep learning algorithms and typically achieves moderate to good classification performance. A key aspect that determines the performance of HDC is encoding the input data to the hyperdimensional (HD) space.
METHODS
This article proposes a novel lightweight approach relying only on native HD arithmetic vector operations to encode binarized images that preserves the similarity of patterns at nearby locations by using point of interest selection and .
RESULTS
The method reaches an accuracy of 97.92% on the test set for the MNIST data set and 84.62% for the Fashion-MNIST data set.
DISCUSSION
These results outperform other studies using native HDC with different encoding approaches and are on par with more complex hybrid HDC models and lightweight binarized neural networks. The proposed encoding approach also demonstrates higher robustness to noise and blur compared to the baseline encoding.
PubMed: 38946939
DOI: 10.3389/fdata.2024.1371518 -
World Journal of Gastroenterology Jun 2024Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models...
BACKGROUND
Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.
AIM
To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.
METHODS
Data of patients treated for colorectal cancer ( = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group ( = 60) and a control group ( = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.
RESULTS
More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation ( < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.
CONCLUSION
This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.
Topics: Humans; Male; Colorectal Neoplasms; Female; Machine Learning; Middle Aged; Reoperation; Retrospective Studies; Risk Factors; Risk Assessment; Aged; Postoperative Complications; Nomograms; ROC Curve; China; Adult
PubMed: 38946868
DOI: 10.3748/wjg.v30.i23.2991 -
World Journal of Gastrointestinal... Jun 2024Improved adenoma detection rate (ADR) has been demonstrated with artificial intelligence (AI)-assisted colonoscopy. However, data on the real-world application of AI and...
BACKGROUND
Improved adenoma detection rate (ADR) has been demonstrated with artificial intelligence (AI)-assisted colonoscopy. However, data on the real-world application of AI and its effect on colorectal cancer (CRC) screening outcomes is limited.
AIM
To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or symptoms.
METHODS
AI software (GI Genius, Medtronic) was implemented into the standard procedure protocol in November 2022. Data was collected on patient demographics, procedure indication, polyp size, location, and pathology. CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up.
RESULTS
We evaluated 1008 colonoscopies (278 pre-AI, 255 early post-AI, 285 established post-AI, and 190 late post-AI). The ADR was 38.1% pre-AI, 42.0% early post-AI ( = 0.77), 40.0% established post-AI ( = 0.44), and 39.5% late post-AI ( = 0.77). There were no significant differences in polyp detection rate (PDR, baseline 59.7%), advanced ADR (baseline 16.2%), and non-neoplastic PDR (baseline 30.0%) before and after AI introduction.
CONCLUSION
In patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy. Although the potential of AI in colonoscopy is undisputed, current AI technology may not universally elevate screening metrics across all situations and patient populations. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.
PubMed: 38946853
DOI: 10.4253/wjge.v16.i6.335 -
World Journal of Clinical Oncology Jun 2024Thyroid carcinoma is a complex disease with several types, the most common being well-differentiated and undifferentiated. The latter, "undifferentiated carcinoma", also...
Thyroid carcinoma is a complex disease with several types, the most common being well-differentiated and undifferentiated. The latter, "undifferentiated carcinoma", also known as anaplastic thyroid carcinoma (ATC), is a highly aggressive malignant tumor accounting for less than 0.2% of all thyroid carcinomas and carries a poor prognosis with a median survival of 5 months. gene mutations are the most common molecular factor associated with this type of thyroid carcinoma. Recent advances in targeted biological agents, immunotherapy, stem cell therapy, nanotechnology, the dabrafenib/trametinib combination therapy, immune checkpoint inhibitors (ICI) and artificial intelligence offer novel treatment options. The combination therapy of dabrafenib and trametinib is the current standard treatment for patients with gene mutations. Besides, the dabrafenib/trametinib combination therapy, ICI, used alone or in combination with targeted therapies have raised some hopes for improving the prognosis of this deadly disease. Younger age, earlier tumor stage and radiotherapy are all prognostic factors for improved outcomes. Ultimately, therapeutic regimens should be tailored to the individual patient based on surveillance and epidemiological data, and a multidisciplinary approach is essential.
PubMed: 38946831
DOI: 10.5306/wjco.v15.i6.674 -
Biomedical Engineering Letters Jul 2024: The purpose of this study was to investigate the neuromodulatory effects of transauricular vagus nerve stimulation (taVNS) and determine optimal taVNS duration to...
UNLABELLED
: The purpose of this study was to investigate the neuromodulatory effects of transauricular vagus nerve stimulation (taVNS) and determine optimal taVNS duration to induce the meaningful neuromodulatroty effects using resting-state electroencephalography (EEG). : Fifteen participants participated in this study and taVNS was applied to the cymba conchae for a duration of 40 min. Resting-state EEG was measured before and during taVNS application. EEG power spectral density (PSD) and brain network indices (clustering coefficient and path length) were calculated across five frequency bands (delta, theta, alpha, beta and gamma), respectively, to assess the neuromodulatory effect of taVNS. Moreover, we divided the whole brain region into the five regions of interest (frontal, central, left temporal, right temporal, and occipital) to confirm the neuromodulation effect on each specific brain region. : Our results demonstrated a significant increase in EEG frequency powers across all five frequency bands during taVNS. Furthermore, significant changes in network indices were observed in the theta and gamma bands compared to the pre-taVNS measurements. These effects were particularly pronounced after approximately 10 min of stimulation, with a more dominant impact observed after approximately 20-30 min of taVNS application. : The findings of this study indicate that taVNS can effectively modulate the brain activity, thereby exerting significant effects on brain characteristics. Moreover, taVNS duration of approximately 20-30 min was considered appropriate for inducing a stable and efficient neuromodulatory effects. Consequently, these findings have the potential to contribute to research aimed at enhancing cognitive and motor functions through the modulation of EEG using taVNS.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s13534-024-00361-8.
PubMed: 38946812
DOI: 10.1007/s13534-024-00361-8 -
Health Science Reports Jul 2024Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the...
PURPOSE
Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person.
METHOD
In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected.
RESULTS
The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer.
CONCLUSION
Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.
PubMed: 38946777
DOI: 10.1002/hsr2.2203 -
Facial Plastic Surgery & Aesthetic... Jul 2024ChatGPT and Google Bard™ are popular artificial intelligence chatbots with utility for patients, including those undergoing aesthetic facial plastic surgery. To...
ChatGPT and Google Bard™ are popular artificial intelligence chatbots with utility for patients, including those undergoing aesthetic facial plastic surgery. To compare the accuracy and readability of chatbot-generated responses to patient education questions regarding aesthetic facial plastic surgery using a response accuracy scale and readability testing. ChatGPT and Google Bard™ were asked 28 identical questions using four prompts: none, patient friendly, eighth-grade level, and references. Accuracy was assessed using Global Quality Scale (range: 1-5). Flesch-Kincaid grade level was calculated, and chatbot-provided references were analyzed for veracity. Although 59.8% of responses were good quality (Global Quality Scale ≥4), ChatGPT generated more accurate responses than Google Bard™ on patient-friendly prompting ( < 0.001). Google Bard™ responses were of a significantly lower grade level than ChatGPT for all prompts ( < 0.05). Despite eighth-grade prompting, response grade level for both chatbots was high: ChatGPT (10.5 ± 1.8) and Google Bard™ (9.6 ± 1.3). Prompting for references yielded 108/108 of chatbot-generated references. Forty-one (38.0%) citations were legitimate. Twenty (18.5%) provided accurately reported information from the reference. Although ChatGPT produced more accurate responses and at a higher education level than Google Bard™, both chatbots provided responses above recommended grade levels for patients and failed to provide accurate references.
PubMed: 38946595
DOI: 10.1089/fpsam.2023.0368