Applying Machine Learning Algorithms to Predict Endometriosis Onset

BackgroundClinicians’ use of electronic health record (EHR) systems while multitasking may increase the risk of making errors, but silent EHR system use may lower patient satisfaction. Delaying EHR system use until after patient visits may increase clinicians’ EHR workload, stress, and burnout.ObjectiveWe aimed to describe the perspectives of clinicians, educators, administrators, and researchers about misses and near misses that they felt were related to clinician multitasking while using EHR systems.MethodsThis observational study was a thematic analysis of perspectives elicited from 63 continuing medical education (CME) participants during 2 workshops and 1 interactive lecture about challenges and strategies for relationship-centered communication during clinician EHR system use. The workshop elicited reflection about memorable times when multitasking EHR use was associated with “misses” (errors that were not caught at the time) or “near misses” (mistakes that were caught before leading to errors). We conducted qualitative analysis using an editing analysis style to identify codes and then select representative themes and quotes.ResultsAll workshop participants shared stories of misses or near misses in EHR system ordering and documentation or patient-clinician communication, wondering about “misses we don’t even know about.” Risk factors included the computer’s position, EHR system usability, note content and style, information overload, problematic workflows, systems issues, and provider and patient communication behaviors and expectations. Strategies to reduce multitasking EHR system misses included clinician transparency when needing silent EHR system use (eg, for prescribing), narrating EHR system use, patient activation during EHR system use, adapting visit organization and workflow, improving EHR system design, and improving team support and systems.ConclusionsCME participants shared numerous stories of errors and near misses in EHR tasks and communication that they felt related to EHR multitasking. However, they brainstormed diverse strategies for using EHR systems safely while preserving patient relationships.

IntroductionThe ability to utilize the electronic health record (EHR) without compromising the doctor-patient relationship (DPR) is an essential skill of all physicians and trainees, yet little time is spent on educating or assessing learners on needed techniques. To address this gap, we developed a conventional OSCE station coupled with a simulated patient chart within the Epic program in order to assess our students' skills utilizing the EHR during a patient encounter.MethodsOf third-year medical students, 119 were given full access to the patient's simulated chart 24 hours in advance of their OSCE to review clinical data. During an in-person OSCE with a standardized patient (SP), students performed a focused history and physical, using the EHR to verify allergies and medications. Students completed an electronic patient note graded by faculty. SPs evaluated the students on communication and interpersonal skills with specific rubric elements. Faculty graded the students' notes to evaluate their expression of clinical reasoning in the assessment and plan.ResultsTraining SPs and faculty to assess students on EHR skills was feasible. After implementation of a comprehensive curriculum focused on EHR and DPR, there was a significant difference on EHR-related communication skills (M = 76.4, SD = 17.6) versus (M = 37, SD = 28.9) before curriculum enhancement t (117.9) = −12.4, p <.001.DiscussionThe EHR OSCE station provided a standardized method of assessing students' EHR skills during a patient encounter. Challenges still exist in the technological requirements to develop and deliver cases in today's EHR platform.

Usability problems do not heal by themselves: National survey on physicians’ experiences with EHRs in Finland

This paper presents the impact of the Electronic Health Records (EHRs) systems jointly in the Spanish Primary Public Health System. Different EHRs that exist in each of the Spanish regions are discussed. Moreover, other purpose of this analysis is to identify the current state of knowledge about health information systems adoption in primary care in Spain. For the analysis and study of EHRs systems in Spain we have relied on the use of different sources, mostly items related to the study of EHRs systems in different areas. We will analyze some technical aspects of these and some of their major implications, both positive and negative. Moreover, we have resorted to make direct contact with the organizations that have implemented the EHRs systems. The result of this study leads to a main idea, the need for interoperability between different systems. We will delve into how we have reached this conclusion and that is the key to EHRs systems homogenization of Spanish territory. EHR systems used in different regions of Spain offer the access to medical information as well as provide a clinical analysis of each patient more quickly. The adoption of health information systems is seen world wide as one method to mitigate the widening health care demand and supply gap.

Diabetes Mellitus (DM) is caused due to the elevated levels of blood sugar i.e., said to be hyperglycemia. The DM is a metabolic chronic disease; therefore, early diagnosis and treatment is necessary to avoid life-threatening risks. According to the World Health Organization (WHO), the diabetes cause high mortality rate with 1.5 million deaths in a year. With the remarkable improvisations in the technology, the disease can be diagnosed earlier. In this paper, we have developed a decision-making support with the machine learning algorithms for DM diagnosis. The Pima Indians Diabetes dataset was chosen to train with Machine Learning algorithms. Our approach begins with Exploratory data analysis, and later the data is sent for data pre-processing and perform the feature Selection techniques. The important features are selected and finally, the data is trained with six various Machine learning (ML) algorithms such as Naïve Bayes, KNN, Random Forest, Logistic Regression, Decision Tree, and eXtreme gradient boosting. The Experimental results of the ML algorithms are calculated by the performance metrics in which that the eXtreme Gradient Boosting has scored highest with 88.2% accuracy than other machine learning algorithms.

Nurses are one of the largest user groups of the electronic health record (EHR) system, relying on its tools to support patient care and nursing workflows. Recent studies suggested that the redesign of nursing documentation may reduce the time spent in the EHR system and improve nurse satisfaction. We aimed to assess nurses' perceptions of the redesigned EHR, evaluate the impact of documentation interventions, and identify future improvement needs. Guided by the American Nursing Informatics Association's Six Domains of Burden conceptual framework, this multimethod project combined both qualitative and quantitative approaches. Registered nurses across the academic health system were recruited via email invitations to participate in focus group discussions. The focus groups were conducted via a web conference and ranged from 60 to 90 minutes in duration. The focus group discussions were transcribed and analyzed through thematic analysis. The EHR vendor's time data were used to analyze nurses' time spent in documentation. In total, 20 registered nurses participated in the focus group discussions, and 17 nurses completed the demographic survey; 88% (15/17) of participants had ≥3 years of EHR experience at the academic health system, and 53% (9/17) self-reported being competent in the EHR system. The following six themes emerged: positive feedback, usability and workflow opportunities, nuisance, training and education, communication, and time spent in the system. EHR vendor time data revealed that the time spent in flowsheets averaged 31.11% per 12-hour shift. Overall, participants reported a positive experience and that the EHR supported patient care. There are opportunities to further reduce redundancies in documentation and implement programs that support continuous learning about EHR and health technology tools. Specific suggestions include optimizing the oral health assessment tool. Analyzing frontline nursing perspectives in the redesign of EHR workflows is imperative for identifying interventions that support nurses' satisfaction with the EHR.

With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.

The aim of this paper is to review machine learning (ML) algorithms and techniques for hate speech detection in social media (SM). Hate speech problem is normally model as a text classification task. In this study, we examined the basic baseline components of hate speech classification using ML algorithms. There are five basic baseline components &#x2013; data collection and exploration, feature extraction, dimensionality reduction, classifier selection and training, and model evaluation, were reviewed. There have been improvements in ML algorithms that were employed for hate speech detection over time. New datasets and different performance metrics have been proposed in the literature. To keep the researchers informed regarding these trends in the automatic detection of hate speech, it calls for a comprehensive and an updated state-of-the-art. The contributions of this study are three-fold. First to equip the readers with the necessary information on the critical steps involved in hate speech detection using ML algorithms. Secondly, the weaknesses and strengths of each method is critically evaluated to guide researchers in the algorithm choice dilemma. Lastly, some research gaps and open challenges were identified. The different variants of ML techniques were reviewed which include classical ML, ensemble approach and deep learning methods. Researchers and professionals alike will benefit immensely from this study.

The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms. This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP). We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis. With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.

Machine learning (ML) algorithms can accurately extract quantitative patterns from datasets without requiring prior knowledge, playing an increasingly crucial role in tasks such as information extraction from remotely sensed data. This study employs several ML algorithms, including Back Propagation Network (BPN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop global oceanic chlorophyll-a (Chl-a) concentration inversion models for MODIS/Aqua (MODISA). The models utilize the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) dataset, which contains in situ Chl-a concentration measurements paired with MODISA remote sensing reflectance (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\mathrm {rs}}$ </tex-math></inline-formula>) for various water color bands from 2002 to 2017. This study includes the following processing steps. First, the SeaBASS dataset is randomly divided into training dataset and testing dataset. Second the Chl-a concentration inversion models are developed using the training dataset. Third, the testing dataset is utilized to evaluate the developed models. Finally, the inversion experiment is conducted using MODISA data. Experimental results indicate that all ML algorithms achieve higher inversion accuracy compared to the traditional ocean color index (OCI) algorithm. Particularly, the SVR model demonstrates the best performance. Additionally, to address the challenge of multidimensional parameter optimization in ML algorithms, this study introduces the Differential Evolution (DE) algorithm and develops a DE-based SVM model (DE-SVM). Compared to the standard SVM model, the DE-SVM model significantly reduces the mean deviation, mean absolute deviation, and root mean square error of the inverted chlorophyll-a concentration, while increasing the coefficient of determination (R2) from 0.87 to 0.926. These results indicate that the DE-SVM model further improves the inversion accuracy of Chl-a concentration, and holds substantial potential in satellite remote sensing inversion of Chl-a concentration.

An Electronic Health Record (EHR) system is a computerised medical information system that collects, displays, and stores a patient’s information. It is an evidence base that addresses issues associated with patients’ paper records. Implementing such a system will have a high positive impact on healthcare quality and healthcare services. For example, an EHR is an electronic record that sequentially stores any resident’s health data from nearly the first month of gestation until death, and can bring those records anytime and to any authorised physician. The objectives of this study are to investigate the present status of EHR implementations around the world, and to identify best practice solutions. Additionally, the study focuses on how to adopt best practices in Australia. The methodology of this paper involved an academic research consisting of 250 articles and over 100 websites. All of the information in this paper was obtained through a search strategy—using PubMed, Google Scholar, and Google—of the best practices applied in many countries, including the US, Canada, and several nations in Western Europe. With 30 references used that provided the recommendations to adopt the best practice solutions for Australian My Health Record system while implementation. This paper has further exposed the problems with EHR systems as implemented worldwide. The recommendations can be summarised as follows: improve the overall awareness of the stakeholders, conduct training sessions for stakeholders on My Health Record system, reward physicians for using the system, achieve ongoing technical and systems security integrity and compliance, implement a response plan in the event of a breach of the EHR system, and implement a simple graphical user interface to facilitate access to stakeholders. Further results as recommendations are provided in the results section. The research concluded that by adopting these best practice solutions, Australia will be able to achieve a stable healthcare system, which will ensure a higher level of healthcare quality to patients and healthcare alike. This paper will give stakeholders a clear vision to determine the original cause that hinders satisfying results while implementing My Health Record system in Australia.

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

Electronic Health Record (EHR) systems are the cornerstone of the healthcare industry. Looking through the combined theoretical lens of value co-creation (VCC) and the relational view, this article uses the qualitative case study methodology to get an in-depth understanding of value co-creation in EHR systems. The spirit of this study is discovery. Value co-creation perspectives of different business-unit heads within an extensive hospital system were gathered to identify: (1) the key values reciprocally co-created and recognized in a multi-unit EHR environment, and (2) critical challenges facing the implementation of EHR systems in such multi-unit environments. Based on the study results, a conceptual framework is proposed highlighting five VCC dimensions of EHR in multi-unit environments: systems & data, information, processes, relationships, and regulations.

The smart-phone has become a critical cybernetic victim especially of cellular botnets. The current exploration examines mobile botnet attacks for Android smart-phone launched from a Windows based PC and detects those attacks using an ensemble machine learning classification algorithm. This investigation is to breach the gap to develop malware framework in perspective of headers’ field examination of PE format with Machine Learning algorithm used. The Homogeneous architecture model proposed in this investigation chooses the most representative subset of the features toward accurate classification of botnet traffic. The ensemble classification technique used in this architecture consists of four machine learning algorithms namely; Random Forest, Gradient Boosting Algorithm, Extreme learning machine and eXtreme Gradient Boosting. After exhaustive literature survey, it is concluded that the architecture proposed in this investigation is unique homogeneous combination of four algorithm mention above. The efficiency of the proposed architecture is evaluated on existing data-set CLaMP (Classification of Malware with PE headers). It has been evaluated on a ClaMP data-set to achieve better botnet detection accuracy relative to its peer techniques.

Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study