Machine Learning In Health Research Articles

SMART WATCH DATA ANALYSIS USING PYTHON AND HUMAN HEALTH PREDICTION

This project leverages smartwatch fitness data to predict health patterns and monitor daily activity trends, underscoring the role of wearables in personal health management. Using Python, Pandas, and Plotly, it handles data preprocessing, visualization, and predictive analysis on metrics such as step counts, calories burned, and active minutes. Data preprocessing includes managing missing values and standardizing the "Activity Date" field. Descriptive statistics and visualizations, including scatter plots, pie charts, and bar charts, uncover trends and behavioral patterns. Descriptive statistics provide insight into data distribution, while visualizations reveal significant trends. Scatter plots highlight correlations, such as between calories burned and steps taken, pie charts depict activity time allocation, and bar charts present active minutes across different days. These visualizations uncover behavioral patterns and emphasize data-driven insights. For predictive analysis, a Random Forest model is applied to forecast "very active minutes," representing high-intensity activity. Key predictive features include steps and calories burned, which strongly correlate with active minutes. The model achieved an accuracy of 80%, and validation metrics, such as MSE and R2, confirmed its reliability. This predictive capability offers users actionable insights for fitness improvement, helping them set realistic goals and monitor progress effectively. In conclusion, this study illustrates the practical applications of machine learning in wearable data analysis, showing potential for integration into fitness-tracking apps. The model’s insights support both short-term fitness and long- term health, with future improvements including additional metrics, like heart rate and sleep data, for comprehensive health monitoring. KEYWORDS: Smartwatch data analysis, very active minutes, Physical activity prediction, Random Forest algorithm, Personalized fitness monitoring, High-intensity activity, Machine learning in health, Predictive modeling.

Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models.

Electronic health records (EHRs) are rich sources of patient-level data, offering valuable resources for medical data analysis. However, privacy concerns often restrict access to EHRs, hindering downstream analysis. Current EHR deidentification methods are flawed and can lead to potential privacy leakage. Additionally, existing publicly available EHR databases are limited, preventing the advancement of medical research using EHR. This study aims to overcome these challenges by generating realistic and privacy-preserving synthetic EHRs time series efficiently. We introduce a new method for generating diverse and realistic synthetic EHR time series data using denoizing diffusion probabilistic models. We conducted experiments on 6 databases: Medical Information Mart for Intensive Care III and IV, the eICU Collaborative Research Database (eICU), and non-EHR datasets on Stocks and Energy. We compared our proposed method with 8 existing methods. Our results demonstrate that our approach significantly outperforms all existing methods in terms of data fidelity while requiring less training effort. Additionally, data generated by our method yield a lower discriminative accuracy compared to other baseline methods, indicating the proposed method can generate data with less privacy risk. The proposed model utilizes a mixed diffusion process to generate realistic synthetic EHR samples that protect patient privacy. This method could be useful in tackling data availability issues in the field of healthcare by reducing barrier to EHR access and supporting research in machine learning for health. The proposed diffusion model-based method can reliably and efficiently generate synthetic EHR time series, which facilitates the downstream medical data analysis. Our numerical results show the superiority of the proposed method over all other existing methods.

The Frontier Exploration of the Development Trend of Key Technologies in Digital Healthcare in Medical Big Data Analysis

With the rapid development of medical big data, digital healthcare as an interdisciplinary field is gradually transforming the traditional healthcare model, continuously advancing the realization of personalized medical services and remote health monitoring. This paper systematically reviews 138 research articles related to the development trends of key technologies in digital healthcare, exploring various critical areas including digital health and privacy security, the application of artificial intelligence and machine learning in health, remote monitoring and mobile health, medical data analysis and electronic health records, and the ethical and policy challenges in digital health. Studies indicate that the improvement of privacy security measures and efficient medical data processing technologies are the foundation for the safe promotion of digital healthcare; Artificial intelligence and machine learning technologies play an important role in enhancing the accuracy of diagnosis and disease prediction. Meanwhile, mobile health and remote monitoring technologies show great potential in improving the accessibility and continuity of medical services. In addition, this paper discusses the challenges of ethics and policies in digital health practice, such as data sharing, conflicts of interest, and respect for patient privacy. Future research must focus on refining technological solutions to address these challenges and fully integrate digital healthcare technologies into clinical practice.

Evaluation of Artificial Intelligence Anxiety Status of Generation Z Candidate Nurses using Machine Learning in Perspective of Leadership

This study aims to determine the artificial intelligence (AI) anxiety levels of Z-generation candidate nurses and the variables affecting the anxiety levels of artificial intelligence by the machine learning (ML) method. Data were collected from 431 candidate nurses by questionnaire using the convenience sampling method. R open access programming language was used for the statistical analysis of the study and the evaluation of significant variables according to their importance levels. The Boruta algorithm, a machine learning method, was used in the determination of the variables affecting the level of artificial intelligence anxiety according to the degree of importance. The findings showed that the most important variable for students' artificial intelligence anxiety level is age. Moreover, there is a statistically significant relationship between students' class and their anxiety level, a significant relationship between artificial intelligence and machine learning in health and their anxiety level, and a significant relationship between gender and technological competence evaluation. Furthermore, nearly half of the participants (48.5%) had very low anxiety, 12.8% had low anxiety, 30.2% had medium anxiety, 6.5% had high-level anxiety and 2.1% of them had very high levels of anxiety. With this research, the artificial intelligence anxiety of generation Z was determined by determining the demographic characteristics that are effective in artificial intelligence. We concluded that more sensitive analysis and different results can be obtained when using a machine learning algorithm compared to classical statistical analysis in determining the complex relationships in the data.

Calories Burnt Prediction Using Machine Learning Approach

Calorie burnt prediction by machine learning algorithm” aim to predict the number of calories burnt by an individual during physical activity using machine learning techniques. We collected a dataset that includes features such as heart rate, body temperature, and duration of activity. We used various machine learning models, including XGBoost, linear regression, SVM and random forest, to predict calorie burn based on 15,000 records with seven features. The results indicate that the XGBboost model can accurately predict calorie burn with a minimum mean absolute error of calories. This work contributes to the growing body of research on using machine learning for health and fitness applications and has potential implications for personalized health coaching and wellness tracking. The highest accuracy of training and testing is gained by the XGBboost model with 99.67% with mean absolute error is almost 1.48%.

Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges.

Machine learning (ML) is a powerful asset to support physicians in decision-making procedures, providing timely answers. However, ML for health systems can suffer from security attacks and privacy violations. This paper investigates studies of security and privacy in ML for health. We examine attacks, defenses, and privacy-preserving strategies, discussing their challenges. We conducted the following research protocol: starting a manual search, defining the search string, removing duplicated papers, filtering papers by title and abstract, then their full texts, and analyzing their contributions, including strategies and challenges. Finally, we collected and discussed 40 papers on attacks, defense, and privacy. Our findings identified the most employed strategies for each domain. We found trends in attacks, including universal adversarial perturbation (UAPs), generative adversarial network (GAN)-based attacks, and DeepFakes to generate malicious examples. Trends in defense are adversarial training, GAN-based strategies, and out-of-distribution (OOD) to identify and mitigate adversarial examples (AE). We found privacy-preserving strategies such as federated learning (FL), differential privacy, and combinations of strategies to enhance the FL. Challenges in privacy comprehend the development of attacks that bypass fine-tuning, defenses to calibrate models to improve their robustness, and privacy methods to enhance the FL strategy. In conclusion, it is critical to explore security and privacy in ML for health, because it has grown risks and open vulnerabilities. Our study presents strategies and challenges to guide research to investigate issues about security and privacy in ML applied to health systems.

Machine Learning for Health: Algorithm Auditing & Quality Control

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

Friends in Comment—A Conversation With Regina Barzilay

This is the first of a series of interviews, named Friends in Comment, where I share conversationswith friends and colleagues in areas related to ours. This first interview is with Regina Barzilay. Regina is currently a School of Engineering Distinguished Professor for AI and Health with the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. She is an AI faculty lead for Jameel Clinic, an MIT center for Machine Learning in Health. She was a postdoc with Cornell University, Itahaca, NY, USA, for a year. Her research interests include natural language processing.

Machine learning and algorithmic fairness in public and population health

Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health; there is greater potential for impact by leveraging machine learning in health tasks more broadly. In this Perspective we aim to highlight potential opportunities and challenges for machine learning within a holistic view of health and its influences. To do so, we build on research in population and public health that focuses on the mechanisms between different cultural, social and environmental factors and their effect on the health of individuals and communities. We present a brief introduction to research in these fields, data sources and types of tasks, and use these to identify settings where machine learning is relevant and can contribute to new knowledge. Given the key foci of health equity and disparities within public and population health, we juxtapose these topics with the machine learning subfield of algorithmic fairness to highlight specific opportunities where machine learning, public and population health may synergize to achieve health equity. Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

Ethical Machine Learning in Healthcare.

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.

Big data and machine learning in health

Abstract Introduction Big data is defined as the amount of data that once organized and analysed, can make a value, make decisions, make predictions and discover patterns in order to reduce costs, avoid risks and optimize services. Machine Learning (ML) is a field of artificial intelligence and is characterized as a method of machine learning, which uses algorithms that learn from data analysis, allowing computers to find patterns, draw conclusions and make predictions. These tools can be used in different areas of human knowledge, particularly in the health sector which are generated daily a huge amount of information, allowing the creation of algorithms that learn and gain understanding to assist in various clinical practices. Objectives The purpose of this paper is to analyse the benefits of Big Data and Machine Learning in providing overall health care. Methodology We conducted a review of the scientific literature published in the electronic databases PubMed/MEDLINE and Google Scholar, according to specific criteria, using keywords: Big Data”, “Machine Learning". Results In the field of oncology (skin cancer, breast, lung, leukaemia) ML and Big Data have contributed to early diagnosis of different pathologies and their evolution, as well as optimizing therapies. In ophthalmology (diabetic retinopathy and congenital cataract) has shown high efficacy in rapid diagnosis and appropriate treatment crucial to prevent the progress of the disease. The tested algorithms achieved very favourable results in cases of Parkinson’s and cardiovascular diseases. In the pharmaceutical industry these computer and digital tools have contributed to the optimization of clinical trials, genome sequencing of tumours to then identify and develop specific drugs to fight it. Conclusion Advances of MIL and Big data are notorious and development opportunities are immense and can come to revolutionize tasks such as diagnosis, treatment and health care in general.

The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data.

Recent years have seen a surge of studies in machine learning in health and biomedicine, driven by digitalization of healthcare environments and increasingly accessible computer systems for conducting analyses. Many of us believe that these developments will lead to significant improvements in patient care. Like many academic disciplines, however, progress is hampered by lack of code and data sharing. In bringing together this PLOS ONE collection on machine learning in health and biomedicine, we sought to focus on the importance of reproducibility, making it a requirement, as far as possible, for authors to share data and code alongside their papers.

Editorial: special issue on machine learning for health and medicine

The exponential proliferation of heterogeneous health-related information presents unprecedented opportunities for improvingpatient care.Health-related data arise fromdiverse sources including, but not limited to, individual patient health records, genomic data, data fromwearable health monitors, online reviews of physicians, clinical literature, and medical imagery. Currently, clinicians and other domain experts—including physicians and biostatisticians— are overwhelmed by the volume and variety of the available data. Transforming these data into actionable knowledge presents a barrage of pragmatic and technical challenges. Members of themachine learning community, broadly construed and including researchers in core machine learning, statistical natural language processing, computer vision and other related sub-fields, have been leading the way in developing methods to turn massive amounts of data into actionable knowledge with the goal of ultimately improving patient care. While there is a long history ofwork at the intersection ofmachine learning and health, recently there has been a resurgence in the area due to the increased availability of data and computational power, and the potential of the latter to capitalize on the former. Reflecting this excitement, health-related workshops have taken place in recent years at several machine learning related conferences, including NIPS, AAAI, KDD, and ICML (amongst others). These workshops have attracted a diverse set of participants from related sub-communities. This special issue is meant to provide a unified forum for a sample of high-quality work in this interdisciplinary area, thus showcasing the promise of machine learning for data-driven healthcare. Machine learning in the context of healthcare applications is especially challenging due to a combination of issues. First, there is the sheer volume and variety of the data. As mentioned above, machine learning in healthcare involves many data types, from waveforms to unstructured text. Second, research in this area spans the entire learning pipeline from problem formulation, to feature engineering/selection, to model learning, and output. At each step along the pipeline one may encounter challenges related to a variety of issues including: confounding, missing data, class imbalance, temporal consistency, and task heterogeneity. Finally, it is not enough to develop accurate models; to have impact, the technology must