BMC Medical Informatics and Decision Making最新文献

{"title":"Natural language processing data services for healthcare providers.","authors":"Joshua Au Yeung, Anthony Shek, Thomas Searle, Zeljko Kraljevic, Vlad Dinu, Mart Ratas, Mohammad Al-Agil, Aleksandra Foy, Barbara Rafferty, Vitaliy Oliynyk, James T Teo","doi":"10.1186/s12911-024-02713-x","DOIUrl":"10.1186/s12911-024-02713-x","url":null,"abstract":"<p><strong>Purpose of review: </strong>Embedding machine learning workflows into real-world hospital environments is essential to ensure model alignment with clinical workflows and real-world data. Many non-healthcare industries undergoing digital transformation have already developed data labelling and data quality management services as a vertically integrated business process.</p><p><strong>Recent findings: </strong>In this paper, we describe our experiences developing and implementing a first-of-its-kind clinical NLP (natural language processing) service in the National Health Service, United Kingdom using parallel harmonised platforms. We report on our work developing clinical NLP resources and implementation framework to distil expert clinical knowledge into our NLP models. To date, we have amassed over 26,086 annotations spanning 556 SNOMED CT concepts working with secondary care specialties. Our integrated language modelling service has delivered numerous clinical and operational use-cases using named entity recognition (NER). Such services improve efficiency of healthcare delivery and drive downstream data-driven technologies. We believe it will only be a matter of time before NLP services become an integral part of healthcare providers.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"356"},"PeriodicalIF":3.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11590340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction model of ICU readmission in Chinese patients with acute type A aortic dissection: a retrospective study.","authors":"Hong Ni, Yanchun Peng, Qiong Pan, Zhuling Gao, Sailan Li, Liangwan Chen, Yanjuan Lin","doi":"10.1186/s12911-024-02770-2","DOIUrl":"10.1186/s12911-024-02770-2","url":null,"abstract":"<p><strong>Background: </strong>Readmission to the intensive care unit (ICU) remains a severe challenge, leading to higher rates of death and a greater financial burden. This study aimed to develop a nomogram-based prediction model for individuals with acute type A aortic dissection (ATAAD).</p><p><strong>Methods: </strong>A total of 846 ATAAD patients were retrospectively enrolled between May 2014 and October 2021. Logistic regression was employed to identify the independent risk factors. The prediction model was evaluated using the Hosmer-Lemeshow (H-L) test, the calibration curve, and the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical utility.</p><p><strong>Results: </strong>57 (6.7%) ATAAD patients were readmitted to ICU following their release from the ICU. ICU readmission was predicted with age ≥ 65 years old, body mass index (BMI) ≥ 28 kg/m², tracheotomy, continuous renal replacement therapy (CRRT), and the length of initial ICU stay were predictors of ICU readmission. The AUC was 0.837 (95%CI: 0.789-0.884) and the model fit the data well (H-L test, P = 0.519). DCA also demonstrated good clinical practicability.</p><p><strong>Conclusions: </strong>This prediction model may be helpful for clinicians to assess the risk of ICU readmission, and facilitate the early identification of ATAAD patients at high risk.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"358"},"PeriodicalIF":3.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11600566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review.","authors":"Cindy N Ho, Tiffany Tian, Alessandra T Ayers, Rachel E Aaron, Vidith Phillips, Risa M Wolf, Nestoras Mathioudakis, Tinglong Dai, David C Klonoff","doi":"10.1186/s12911-024-02757-z","DOIUrl":"10.1186/s12911-024-02757-z","url":null,"abstract":"<p><strong>Background: </strong>The large language models (LLMs), most notably ChatGPT, released since November 30, 2022, have prompted shifting attention to their use in medicine, particularly for supporting clinical decision-making. However, there is little consensus in the medical community on how LLM performance in clinical contexts should be evaluated.</p><p><strong>Methods: </strong>We performed a literature review of PubMed to identify publications between December 1, 2022, and April 1, 2024, that discussed assessments of LLM-generated diagnoses or treatment plans.</p><p><strong>Results: </strong>We selected 108 relevant articles from PubMed for analysis. The most frequently used LLMs were GPT-3.5, GPT-4, Bard, LLaMa/Alpaca-based models, and Bing Chat. The five most frequently used criteria for scoring LLM outputs were \"accuracy\", \"completeness\", \"appropriateness\", \"insight\", and \"consistency\".</p><p><strong>Conclusions: </strong>The most frequently used criteria for defining high-quality LLMs have been consistently selected by researchers over the past 1.5 years. We identified a high degree of variation in how studies reported their findings and assessed LLM performance. Standardized reporting of qualitative evaluation metrics that assess the quality of LLM outputs can be developed to facilitate research studies on LLMs in healthcare.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"357"},"PeriodicalIF":3.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11590327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal machine learning for language and speech markers identification in mental health.","authors":"Georgios Drougkas, Erwin M Bakker, Marco Spruit","doi":"10.1186/s12911-024-02772-0","DOIUrl":"10.1186/s12911-024-02772-0","url":null,"abstract":"<p><strong>Background: </strong>There are numerous papers focusing on diagnosing mental health disorders using unimodal and multimodal approaches. However, our literature review shows that the majority of these studies either use unimodal approaches to diagnose a variety of mental disorders or employ multimodal approaches to diagnose a single mental disorder instead. In this research we combine these approaches by first identifying and compiling an extensive list of mental health disorder markers for a wide range of mental illnesses which have been used for both unimodal and multimodal methods, which is subsequently used for determining whether the multimodal approach can outperform the unimodal approaches.</p><p><strong>Methods: </strong>For this study we used the well known and robust multimodal DAIC-WOZ dataset derived from clinical interviews. Here we focus on the modalities text and audio. First, we constructed two unimodal models to analyze text and audio data, respectively, using feature extraction, based on the extensive list of mental disorder markers that has been identified and compiled by us using related and earlier studies. For our unimodal text model, we also propose an initial pragmatic binary label creation process. Then, we employed an early fusion strategy to combine our text and audio features before model processing. Our fused feature set was then given as input to various baseline machine and deep learning algorithms, including Support Vector Machines, Logistic Regressions, Random Forests, and fully connected neural network classifiers (Dense Layers). Ultimately, the performance of our models was evaluated using accuracy, AUC-ROC score, and two F1 metrics: one for the prediction of positive cases and one for the prediction of negative cases.</p><p><strong>Results: </strong>Overall, the unimodal text models achieved an accuracy ranging from 78% to 87% and an AUC-ROC score between 85% and 93%, while the unimodal audio models attained an accuracy of 64% to 72% and AUC-ROC scores of 53% to 75%. The experimental results indicated that our multimodal models achieved comparable accuracy (ranging from 80% to 87%) and AUC-ROC scores (between 84% and 93%) to those of the unimodal text models. However, the majority of the multimodal models managed to outperform the unimodal models in F1 scores, particularly in the F1 score of the positive class (F1 of 1s), which reflects how well the models perform in identifying the presence of a marker.</p><p><strong>Conclusions: </strong>In conclusion, by refining the binary label creation process and by improving the feature engineering process of the unimodal acoustic model, we argue that the multimodal model can outperform both unimodal approaches. This study underscores the importance of multimodal integration in the field of mental health diagnostics and sets the stage for future research to explore more sophisticated fusion techniques and deeper learning models.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"354"},"PeriodicalIF":3.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Screening for severe coronary stenosis in patients with apparently normal electrocardiograms based on deep learning.","authors":"Zhengkai Xue, Shijia Geng, Shaohua Guo, Guanyu Mu, Bo Yu, Peng Wang, Sutao Hu, Deyun Zhang, Weilun Xu, Yanhong Liu, Lei Yang, Huayue Tao, Shenda Hong, Kangyin Chen","doi":"10.1186/s12911-024-02764-0","DOIUrl":"10.1186/s12911-024-02764-0","url":null,"abstract":"<p><strong>Background: </strong>Patients with severe coronary arterystenosis may present with apparently normal electrocardiograms (ECGs), making it difficult to detect adverse health conditions during routine screenings or physical examinations. Consequently, these patients might miss the optimal window for treatment.</p><p><strong>Methods: </strong>We aimed to develop an effective model to distinguish severe coronary stenosis from no or mild coronary stenosis in patients with apparently normal ECGs. A total of 392 patients, including 138 with severe stenosis, were selected for the study. Deep learning (DL) models were trained from scratch and using pre-trained parameters via transfer learning. These models were evaluated based on ECG data alone and in combination with clinical information, including age, sex, hypertension, diabetes, dyslipidemia and smoking status.</p><p><strong>Results: </strong>We found that DL models trained from scratch using ECG data alone achieved a specificity of 74.6% but exhibited low sensitivity (54.5%), comparable to the performance of logistic regression using clinical data. Adding clinical information to the ECG DL model trained from scratch improved sensitivity (90.9%) but reduced specificity (42.3%). The best performance was achieved by combining clinical information with the ECG transfer learning model, resulting in an area under the receiver operating characteristic curve (AUC) of 0.847, with 84.8% sensitivity and 70.4% specificity.</p><p><strong>Conclusions: </strong>The findings demonstrate the effectiveness of DL models in identifying severe coronary stenosis in patients with apparently normal ECGs and validate an efficient approach utilizing existing ECG models. By employing transfer learning techniques, we can extract \"deep features\" that summarize the inherent information of ECGs with relatively low computational expense.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"355"},"PeriodicalIF":3.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data.","authors":"David Cheng-Zarate, James Burns, Cathy Ngo, Agnes Haryanto, Gregory Duncan, David Taniar, Michael Wybrow","doi":"10.1186/s12911-024-02732-8","DOIUrl":"10.1186/s12911-024-02732-8","url":null,"abstract":"<p><strong>Background: </strong>Blood management is an important aspect of healthcare and vital for the well-being of patients. For effective blood management, it is essential to determine the quality and documentation of the processes for blood transfusions in the Electronic Medical Records (EMR) system. The EMR system stores information on most activities performed in a digital hospital. As such, it is difficult to get an overview of all data. The National Safety and Quality Health Service (NSQHS) Standards define metrics that assess the care quality of health entities such as hospitals. To produce these metrics, data needs to be analysed historically. However, data in the EMR is not designed to easily perform analytical queries of the kind which are needed to feed into clinical decision support tools. Thus, another system needs to be implemented to store and calculate the metrics for the blood management national standard.</p><p><strong>Methods: </strong>In this paper, we propose a clinical data warehouse that stores the transformed data from EMR to be able to identify that the hospital is compliant with the Australian NSQHS Standards for blood management. Firstly, the data needed was explored and evaluated. Next, a schema for the clinical data warehouse was designed for the efficient storage of EMR data. Once the schema was defined, data was extracted from the EMR to be preprocessed to fit the schema design. Finally, the data warehouse allows the data to be consumed by decision support tools.</p><p><strong>Results: </strong>We worked with Eastern Health, a major Australian health service, to implement the data warehouse that allowed us to easily query and supply data to be ingested by clinical decision support systems. Additionally, this implementation provides flexibility to recompute the metrics whenever data is updated. Finally, a dashboard was implemented to display important metrics defined by the National Safety and Quality Health Service (NSQHS) Standards on blood management.</p><p><strong>Conclusions: </strong>This study prioritises streamlined data modeling and processing, in contrast to conventional dashboard-centric approaches. It ensures data readiness for decision-making tools, offering insights to clinicians and validating hospital compliance with national standards in blood management through efficient design.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"353"},"PeriodicalIF":3.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthcare dashboard technologies and data visualization for lipid management: A scoping review.","authors":"Mahnaz Samadbeik, Teyl Engstrom, Elton H Lobo, Karem Kostner, Jodie A Austin, Jason D Pole, Clair Sullivan","doi":"10.1186/s12911-024-02730-w","DOIUrl":"10.1186/s12911-024-02730-w","url":null,"abstract":"<p><strong>Background: </strong>Lipid disorders significantly increase cardiovascular disease (CVD) risk, the leading cause of mortality worldwide. Effective lipid management is critical for improving health outcomes. Traditional screening methods face challenges due to data complexity and the need for timely decision-making. Data visualization and dashboard technologies offer clear, actionable insights and supporting informed decision-making. This study investigates the use of these technologies in lipid management and their impacts on the quadruple aim of healthcare.</p><p><strong>Methods: </strong>This scoping review followed the Joanna Briggs Institute (JBI) approach, focusing on studies involving dashboard technologies or data visualization in lipid management. A comprehensive search across multiple databases (Embase, Web of Science, PubMed, Scopus, CINAHL) and gray literature was conducted, including English-language publications from 2014 to 2024. Data were analyzed using quantitative descriptive and qualitative content analysis to evaluate the key features, clinical applications, and outcomes.</p><p><strong>Results: </strong>Twenty-seven studies met the inclusion criteria, primarily focusing on dashboard utilization by physicians for managing diabetes and CVD, utilizing electronic medical records and clinical guidelines. Key analysis methods included comparing key performance indicators (KPIs) (85.2%) and trend analysis (74.1%). Lipid management workflows emphasized prevention (88.9%) and treatment planning (77.8%). Interventions included care packages (comprehensive sets of interventions for patient care), decision support systems, web-based tools, and mobile health solutions. Regarding Quadruple Aim outcomes: 12 studies focused on improving population health (8 positive, 4 no change), 9 on clinical outcomes (5 positive, 4 no change), 6 on provider work life (5 positive), 5 on patient experience (positive changes in education and time management), and 2 on cost reduction (1 positive, 1 negative).</p><p><strong>Conclusions: </strong>Dashboards are important tools in managing lipid disorders in managing lipid disorders, integrating with educational tools, collaborative care models, and decision support systems. Although they are effective in enhancing population health and clinical experiences, their impact on patient outcomes and cost reduction requires further exploration. Future research should focus on detailed evaluations of dashboard impacts on patient outcomes and cost-effectiveness, emphasizing precision prevention of chronic diseases.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"352"},"PeriodicalIF":3.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive model for congenital heart disease in children of Pakistan by using structural equation modeling.","authors":"Sana Shahid, Haris Khurram, Muhammad Ahmed Shehzad, Muhammad Aslam","doi":"10.1186/s12911-024-02774-y","DOIUrl":"10.1186/s12911-024-02774-y","url":null,"abstract":"<p><strong>Background: </strong>The structural abnormality of the heart and its blood vessels at the time of birth is known as congenital heart disease. Every year in Pakistan, sixty thousand children are born with CHD, and 44 in 1000 die before they are a month old. Various studies used different techniques to estimate the risk factors of congenital heart disease, but these techniques suffer from a deficiency of capacity to present human understanding and a deficiency of adequate data. The current study provided an innovative approach by defining the latent variables to handle this issue and building a reasonable model.</p><p><strong>Method: </strong>Data used in this study has been collected from mothers and hospital records of the children. The dataset contains information on 3900 children who visited the OPD of the Chaudry Pervaiz Elahi Institute of Cardiology (CPEIC) Multan, Pakistan from October 2021 to September 2022. The latent variables were defined from the data and structural equation modeling was used to model them.</p><p><strong>Result: </strong>The results show that there are 53.6% of males have acyanotic CHD and 54.5% have cyanotic CHD. There are 46.4% of females have acyanotic CHD and 45.5% have cyanotic CHD. The children who have no diabetes in the family are 64.0% and children who have diabetes in the family are 36.0% in acyanotic CHD, the children who have no diabetes in the family are 59.7% and children have diabetes in the family are 40.3% in cyanotic CHD. The value of standardized root mean residual is 0.087 is less than 0.089 which shows that the model is a good fit. The value of root mean square error of approximation is 0.113 is less than 0.20 which also shows the good fit of the model.</p><p><strong>Conclusion: </strong>It was concluded that the model is a good fit. Also, the latent variables, socioeconomic factors, and environmental factors of mothers during pregnancy have a significant effect in causing cyanotic while poor general health factor increases the risk of Acyanotic congenital heart disease.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"351"},"PeriodicalIF":3.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11580548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.","authors":"Ha Na Cho, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Hyeram Seo, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Tae Joon Jun, Young-Hak Kim","doi":"10.1186/s12911-024-02755-1","DOIUrl":"10.1186/s12911-024-02755-1","url":null,"abstract":"<p><strong>Background: </strong>Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes.</p><p><strong>Objective: </strong>Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction.</p><p><strong>Methods: </strong>A retrospective cohort study was conducted from January 2018 to October 2020. A total cohort of 240,000 patients' medical records was collected. The data were collected exclusively for preoperative variables to accurately analyze the predictive factors impacting the postoperative length of stay. The main outcome of this study is an analysis of the length of stay (in days) after surgery until discharge. The prediction was performed with ridge regression, random forest, XGBoost, and multi-layer perceptron neural network models.</p><p><strong>Results: </strong>The XGBoost resulted in the best performance with an average error within 3 days. Moreover, we explain each feature's contribution over the XGBoost model and further display distinct predictors affecting the overall prediction outcome at the patient level. The risk factors that most importantly contributed to the stay after surgery were as follows: a direct bilirubin laboratory test, department change, calcium chloride medication, gender, and diagnosis with the removal of other organs. Our results suggest that healthcare providers take into account the risk factors such as the laboratory blood test, distributing patients, and the medication prescribed prior to the surgery.</p><p><strong>Conclusion: </strong>We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"350"},"PeriodicalIF":3.3,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified multiscale Renyi distribution entropy for short-term heart rate variability analysis.","authors":"Manhong Shi, Yinuo Shi, Yuxin Lin, Xue Qi","doi":"10.1186/s12911-024-02763-1","DOIUrl":"10.1186/s12911-024-02763-1","url":null,"abstract":"<p><strong>Background: </strong>Multiscale sample entropy (MSE) is a prevalent complexity metric to characterize a time series and has been extensively applied to the physiological signal analysis. However, for a short-term time series, the likelihood of identifying comparable subsequences decreases, leading to higher variability in the Sample Entropy (SampEn) calculation. Additionally, as the scale factor increases in the MSE calculation, the coarse-graining process further shortens the time series. Consequently, each newly generated time series at a larger scale consists of fewer data points, potentially resulting in unreliable or undefined entropy values, particularly at higher scales. To overcome the shortcoming, a modified multiscale Renyi distribution entropy (MMRDis) was proposed in our present work.</p><p><strong>Methods: </strong>The MMRDis method uses a moving-averaging procedure to acquire a family of time series, each of which quantify the dynamic behaviors of the short-term time series over the multiple temporal scales. Then, MMRDis is constructed for the original and the coarse-grained time series.</p><p><strong>Results: </strong>The MMRDis method demonstrated superior computational stability on simulated Gaussian white and 1/f noise time series, effectively avoiding undefined measurements in short-term time series. Analysis of short-term heart rate variability (HRV) signals from healthy elderly individuals, healthy young people, and subjects with congestive heart failure and atrial fibrillation revealed that MMRDis complexity measurement values decreased with aging and disease. Additionally, MMRDis exhibited better distinction capability for short-term HRV physiological/pathological signals compared to several recently proposed complexity metrics.</p><p><strong>Conclusions: </strong>MMRDis was a promising measurement for screening cardiovascular condition within a short time.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"346"},"PeriodicalIF":3.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}