BMC Medical Informatics and Decision Making最新文献

{"title":"A new risk assessment model of venous thromboembolism by considering fuzzy population.","authors":"Xin Wang, Yu-Qing Yang, Xin-Yu Hong, Si-Hua Liu, Jian-Chu Li, Ting Chen, Ju-Hong Shi","doi":"10.1186/s12911-024-02834-3","DOIUrl":"10.1186/s12911-024-02834-3","url":null,"abstract":"<p><strong>Background: </strong>Inpatients with high risk of venous thromboembolism (VTE) usually face serious threats to their health and economic conditions. Many studies using machine learning (ML) models to predict VTE risk overlook the impact of class-imbalance problem due to the low incidence rate of VTE, resulting in inferior and unstable model performance, which hinders their ability to replace the Padua model, a widely used linear weighted model in clinic. Our study aims to develop a new VTE risk assessment model suitable for Chinese medical inpatients.</p><p><strong>Methods: </strong>3284 inpatients in the medical department of Peking Union Medical College Hospital (PUMCH) from January 2014 to June 2016 were collected. The training and test set were divided based on the admission time and inpatients from May 2016 to June 2016 were included as the test dataset. We explained the class imbalance problem from a clinical perspective and defined a new term, \"fuzzy population\", to elaborate and model this phenomenon. By considering the \"fuzzy population\", a new ML VTE risk assessment model was built through population splitting. Sensitivity and specificity of our method was compared with five ML models (support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR), and XGBoost) and the Padua model.</p><p><strong>Results: </strong>The 'fuzzy population' phenomenon was explained and verified on the VTE dataset. The proposed model achieved higher specificity (64.94% vs. 63.30%) and the same sensitivity (90.24% vs. 90.24%) on test data than the Padua model. Other five ML models couldn't simultaneously surpass the Padua's sensitivity and specificity. Besides, our model was more robust than five ML models and its standard deviations of sensitivities and specificities were smaller. Adjusting the distribution of negative samples in the training set based on the 'fuzzy population' would exacerbate the instability of performance of five ML models, which limited the application of ML methods in clinic.</p><p><strong>Conclusions: </strong>The proposed model achieved higher sensitivity and specificity than the Padua model, and better robustness than traditional ML models. This study built a population-split-based ML model of VTE by modeling the class-imbalance problem and it can be applied more broadly in risk assessment of other diseases.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"413"},"PeriodicalIF":3.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11686901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906630","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":"Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis.","authors":"Gunjan Shandilya, Sheifali Gupta, Salil Bharany, Ateeq Ur Rehman, Upinder Kaur, Hafizan Mat Som, Seada Hussen","doi":"10.1186/s12911-024-02840-5","DOIUrl":"10.1186/s12911-024-02840-5","url":null,"abstract":"<p><p>Major underlying health issues can be indicated by even minor nail infections. Subungual Melanoma is one of the most severe kinds since it is identified at a much later stage than other conditions. The purpose of this research is to offer novel deep-learning algorithms that target the autonomous categorization of six forms of nail disorders by employing images: Blue Finger, Clubbing, Pitting, Onychogryphosis, Acral Lentiginous Melanoma, and Normal Nail or Healthy Nail Appearance. Based on this, we build an initial baseline CNN model, which is then further advanced by the introduction of the Hybrid Capsule CNN model by the reduction of space hierarchy deficiencies of the classic CNN model. All these models were trained and tested using the Nail Disease Detection dataset with intensive uses of techniques of data augmentation. The Hybrid Capsule CNN model, thus, provided superior classification accuracy compared to the others; the training accuracy was 99.40%, while the validation accuracy was 99.25%, whereas the hybrid model outperformed the Base CNN model with astounding precision, recall of 97.35% and 96.79%. The hybrid model additionally leverages the capsule network and dynamic routing, offering improved robustness against transformations as well as improving spatial properties. The current study consequently provides a very viable, economical, and accessible diagnostic tool, especially for places with a paucity of medical services. The proposed methodology provides tremendous capacity for early diagnosis and better outcomes for the patient in a healthcare scenario. Clinical trial number Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"414"},"PeriodicalIF":3.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11686868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906632","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":"The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data.","authors":"Juan G Diaz Ochoa, Faizan E Mustafa, Felix Weil, Yi Wang, Kudret Kama, Markus Knott","doi":"10.1186/s12911-024-02825-4","DOIUrl":"10.1186/s12911-024-02825-4","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Medical narratives are fundamental to the correct identification of a patient's health condition. This is not only because it describes the patient's situation. It also contains relevant information about the patient's context and health state evolution. Narratives are usually vague and cannot be categorized easily. On the other hand, once the patient's situation is correctly identified based on a narrative, it is then possible to map the patient's situation into precise classification schemas and ontologies that are machine-readable. To this end, language models can be trained to read and extract elements from these narratives. However, the main problem is the lack of data for model identification and model training in languages other than English. First, gold standard annotations are usually not available due to the high level of data protection for patient data. Second, gold standard annotations (if available) are difficult to access. Alternative available data, like MIMIC (Sci Data 3:1, 2016) is written in English and for specific patient conditions like intensive care. Thus, when model training is required for other types of patients, like oncology (and not intensive care), this could lead to bias. To facilitate clinical narrative model training, a method for creating high-quality synthetic narratives is needed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method: &lt;/strong&gt;We devised workflows based on generative AI methods to synthesize narratives in the German language to avoid the disclosure of patient's health data. Since we required highly realistic narratives, we generated prompts, written with high-quality medical terminology, asking for clinical narratives containing both a main and co-disease. The frequency of distribution of both the main and co-disease was extracted from the hospital's structured data, such that the synthetic narratives reflect the disease distribution among the patient's cohort. In order to validate the quality of the synthetic narratives, we annotated them to train a Named Entity Recognition (NER) algorithm. According to our assumptions, the validation of this system implies that the synthesized data used for its training are of acceptable quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Result: &lt;/strong&gt;We report precision, recall and F1 score for the NER model while also considering metrics that take into account both exact and partial entity matches. Trained models are cautious, with a precision up to 0.8 for Entity Type match metric and a F1 score of 0.3.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Despite its inherent limitations, this technology has the potential to allow data interoperability by using encoded diseases across languages and regions without compromising data safety. Additionally, it facilitates the synthesis of unstructured patient data. In this way, the identification and training of models can be accelerated. We believe that this method may be able to generate discharge letters for any combination of main and co-diseases, ","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"409"},"PeriodicalIF":3.3,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892320","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":"Association between serum hypertriglyceridemia and hematological indices: data mining approaches.","authors":"Somayeh Ghiasi Hafezi, Amin Mansoori, Alireza Kooshki, Marzieh Hosseini, Sahar Ghoflchi, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan","doi":"10.1186/s12911-024-02835-2","DOIUrl":"10.1186/s12911-024-02835-2","url":null,"abstract":"<p><strong>Background: </strong>High triglyceride (TG) affects and is affected of other hematological factors. The determination of serum fasted triglycerides concentrations, as part of a lipid profile, is crucial key point in hematological factors and significantly affect various systemic diseases. This study was carried out to assess the potential relation between the concentration of TG and hematological factors.</p><p><strong>Method: </strong>Our sample size was 9704 participants beginning in 2007 and ending in 2020 aged between 35 and 65 years, sourced from the MASHAD cohort (northeastern Iran). Machine learning methodologies, specifically logistic regression, decision tree, and random forest algorithms, were utilized for data analysis in the investigation of individuals with normal and high TG levels.</p><p><strong>Results: </strong>The highest Gini score belongs to RLR (Red cell distribution width/Lymphocyte) (236.10), RPR (Red cell distribution width/Platelets) (215.78), and PHR (Platelets/high-density lipoprotein) (273.66). We also found that factors such as age are statistically associated with the level of TG in women probably due to the drop in menopausal estrogen. RF model showed to have higher accuracy in predicting the TG level in both males and females.</p><p><strong>Conclusion: </strong>Our model assessed the association between serum TG with several hematological factors like RLR, RPR, and PHR. Other hematological factors also have been reported to be related to the TG level. As these results give us new insights into the association of TG on various hematological factors and their possible interactions with each other. future studies are needed to provide sufficient data for the mechanism and the pathophysiology of the findings.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"410"},"PeriodicalIF":3.3,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892294","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":"Identification of confounders and estimating the causal effect of place of birth on age-specific childhood vaccination.","authors":"Ashagrie Sharew Iyassu, Haile Mekonnen Fenta, Zelalem G Dessie, Temesgen T Zewotir","doi":"10.1186/s12911-024-02827-2","DOIUrl":"10.1186/s12911-024-02827-2","url":null,"abstract":"<p><strong>Background: </strong>In causal analyses, some third factor may distort the relationship between the exposure and the outcome variables under study, which gives spurious results. In this case, treatment groups and control groups that receive and do not receive the exposure are different from one another in some other essential variables, called confounders.</p><p><strong>Method: </strong>Place of birth was used as exposure variable and age-specific childhood vaccination status was used as outcome variables. Three approaches of confounder selection techniques such as all pre-treatment covariates, outcome cause covariates, and common cause covariates were proposed. Multiple logistic regression was used to estimate the propensity score for inverse probability treatment weighting (IPTW) confounder adjustment techniques. The proportional odds model was used to estimate the causal effect of place of birth on age-specific childhood vaccination. To validate the result obtained from observed data, we used a plasmode simulation of resampling 1000 samples from actual data 500 times.</p><p><strong>Result: </strong>Outcome cause and common cause confounder identification techniques gave comparable results in terms of treatment effect in the plasmode data. However, outcome causes that contain common causes and predictors of the outcome confounder identification gave relatively better treatment effect results. The treatment effect result in the IPTW confounder adjustment method was better than that of the regression adjustment method. The effect of place of birth on log odds of cumulative probability of age-specific childhood vaccination was 0.36 with odds ratio of 1.43 for higher level vaccination status.</p><p><strong>Conclusion: </strong>It is essential to use plasmode simulation data to validate the reproducibility of the proposed methods on the observed data. It is important to use outcome-cause covariates to adjust their confounding effect on the outcome. Using inverse probability treatment weighting gives unbiased treatment effect results as compared to the regression method of confounder adjustment. Institutional delivery increases the likelihood of childhood vaccination at the recommended schedule.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"406"},"PeriodicalIF":3.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11673688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892308","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":"Evaluation of mobile applications related to patients with Parkinson's disease based on their essential features and capabilities.","authors":"Hamid Azadi, Mohammad-Reza Akbarzadeh-Totonchi, Yones Jahani, Sadrieh Hajesmaeel Gohari, Reza Khajouei","doi":"10.1186/s12911-024-02804-9","DOIUrl":"10.1186/s12911-024-02804-9","url":null,"abstract":"<p><strong>Background: </strong>Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Mobile technologies enable Parkinson's patients to improve their quality of life, manage symptoms, and enhance overall well-being through various applications (apps). There is no integrated list of specific capabilities available to cater to the unique needs of Parkinson's patient-focused mobile apps.</p><p><strong>Objective: </strong>This study aimed to identify the key features and capabilities prioritized in developing mobile apps for patients with Parkinson's disease (PWP) and rank the related apps in this field.</p><p><strong>Methods: </strong>We searched iTunes and Google Play for PWP apps with \"Parkinson\" or \"Parkinson's\" in their title or description. We evaluated existing mobile apps through a four-step process: identification, screening, eligibility, and feature analysis. We installed apps on Android and iOS devices, categorized their features/capabilities by the \"use case model\" and other additional identified features. We scored them using a tool called FARM (Feature-based Application Rating Method) and ranked PWP-related apps.</p><p><strong>Results: </strong>Thirty-three apps related to the PWP were included and evaluated. Almost half of the apps were available on both the Android and iOS platforms. Seventy-five percent of the genres were associated with health and fitness. Although the included apps utilized certain features, none of the capabilities were used simultaneously. According to the experts' opinions, 'large font' was the most important feature and was utilized in 70% of the mobile applications. Additionally, the average score for all Parkinson's disease-related applications was 17.71 (SD = 7.92). The app titled 'Swiss Parkinson' had the highest score.</p><p><strong>Conclusions: </strong>Integrating a relevant list of features used for Parkinson's patients' applications yielded valuable insights for the design of mobile applications tailored to patients' needs. These features are highly efficient in dealing with the specific obstacles related to this disease.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"407"},"PeriodicalIF":3.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11673987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892298","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":"Reusing routine electronic health record data for nationwide COVID-19 surveillance in nursing homes: barriers, facilitators, and lessons learned.","authors":"Y Wieland-Jorna, R A Verheij, A L Francke, R Coppen, S C de Greeff, A Elffers, M G Oosterveld-Vlug","doi":"10.1186/s12911-024-02818-3","DOIUrl":"10.1186/s12911-024-02818-3","url":null,"abstract":"<p><strong>Background: </strong>At the beginning of the COVID-19 pandemic in 2020, little was known about the spread of COVID-19 in Dutch nursing homes while older people were particularly at risk of severe symptoms. Therefore, attempts were made to develop a nationwide COVID-19 repository based on routinely recorded data in the electronic health records (EHRs) of nursing home residents. This study aims to describe the facilitators and barriers encountered during the development of the repository and the lessons learned regarding the reuse of EHR data for surveillance and research purposes.</p><p><strong>Methods: </strong>Using inductive content analysis, we reviewed 325 documents written and saved during the development of the COVID-19 repository. This included meeting minutes, e-mails, notes made after phone calls with stakeholders, and documents developed to inform stakeholders. We also assessed the fitness for purpose of the data by evaluating the completeness, plausibility, conformity, and timeliness of the data.</p><p><strong>Results: </strong>Key facilitators found in this study were: 1) inter-organizational collaboration to create support; 2) early and close involvement of EHR software vendors; and 3) coordination and communication between partners. Key barriers that hampered the fitness of EHR data for surveillance were: 1) changes over time in national SARS-CoV-2 testing policy; 2) differences between EHR systems; 3) increased workload in nursing homes and lack of perceived urgency; 4) uncertainty regarding the legal requirements for extracting EHR data; 5) the short notice at which complete and understandable information about the repository had to be developed; and 6) lack of clarity about the differences between various COVID-19 monitors.</p><p><strong>Conclusions: </strong>Despite the urgent need for information on the spread of SARS-CoV-2 among nursing home residents, setting up a repository based on EHR data proved challenging. The facilitators and barriers found in this study affected the extent to which the data could be used. We formulated nine lessons learned for developing future repositories based on EHR data for surveillance and research purposes. These lessons were in three main areas: legal framework, contextual circumstances, and quality of the data. Currently, these lessons are being applied in setting up a new registry in the nursing home sector.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"408"},"PeriodicalIF":3.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11674179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892312","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":"Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.","authors":"Ahmed Saihood, Wijdan Rashid Abdulhussien, Laith Alzubaid, Mohamed Manoufali, Yuantong Gu","doi":"10.1186/s12911-024-02820-9","DOIUrl":"10.1186/s12911-024-02820-9","url":null,"abstract":"<p><strong>Background: </strong>The detection and classification of lung nodules are crucial in medical imaging, as they significantly impact patient outcomes related to lung cancer diagnosis and treatment. However, existing models often suffer from mode collapse and poor generalizability, as they fail to capture the complete diversity of the data distribution. This study addresses these challenges by proposing a novel generative adversarial network (GAN) architecture tailored for semi-supervised lung nodule classification.</p><p><strong>Methods: </strong>The proposed DDDG-GAN model consists of dual generators and discriminators. Each generator specializes in benign or malignant nodules, generating diverse, high-fidelity synthetic images for each class. This dual-generator setup prevents mode collapse. The dual-discriminator framework enhances the model's generalization capability, ensuring better performance on unseen data. Feature fusion techniques are incorporated to refine the model's discriminatory power between benign and malignant nodules. The model is evaluated in two scenarios: (1) training and testing on the LIDC-IDRI dataset and (2) training on LIDC-IDRI, testing on the unseen LUNA16 dataset and the unseen LUNGx dataset.</p><p><strong>Results: </strong>In Scenario 1, the DDDG-GAN achieved an accuracy of 92.56%, a precision of 90.12%, a recall of 95.87%, and an F1 score of 92.77%. In Scenario 2, the model demonstrated robust performance with an accuracy of 72.6%, a precision of 72.3%, a recall of 73.82%, and an F1 score of 73.39% when testing using Luna16 and an accuracy of 71.23%, a precision of 67.56%, a recall of 73.52%, and an F1 score of 70.42% when testing using LungX. The results indicate that the proposed model outperforms state-of-the-art semi-supervised learning approaches.</p><p><strong>Conclusions: </strong>The DDDG-GAN model mitigates mode collapse and improves generalizability in lung nodule classification. It demonstrates superior performance on both the LIDC-IDRI and the unseen LUNA16 and LungX datasets, offering significant potential for improving diagnostic accuracy in clinical practice.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"403"},"PeriodicalIF":3.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881400","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":"A nomogram to distinguish noncardiac chest pain based on cardiopulmonary exercise testing in cardiology clinic.","authors":"Mingyu Xu, Rui Li, Bingqing Bai, Yuting Liu, Haofeng Zhou, Yingxue Liao, Fengyao Liu, Peihua Cao, Qingshan Geng, Huan Ma","doi":"10.1186/s12911-024-02813-8","DOIUrl":"10.1186/s12911-024-02813-8","url":null,"abstract":"<p><strong>Background: </strong>Psychological disorders, such as anxiety and depression, are considered to be one of the causes of noncardiac chest pain (NCCP). And these patients can be challenging to differentiate from coronary artery disease (CAD), leading to a considerable number of patients still undergoing angiography. We aim to develop a practical prediction model and nomogram using cardiopulmonary exercise testing (CPET), to help identify these patients.</p><p><strong>Methods: </strong>1,531 eligible patients' electronic medical record data were obtained from Guangdong Provincial People's Hospital. They were randomly divided into a training dataset (N = 918) and a testing dataset (N = 613) at a ratio of 6:4, and 595 cases without missing data were also selected from testing dataset to form a complete dataset. The training set is used to build the model, and the testing set and the complete set are used for internal validation. Eight machine learning (ML) methods are used to build the model and the best model is finally adopted.</p><p><strong>Results: </strong>The model built by logistic regression performed the best, and among the 29 parameters, six parameters were determined to be valuable parameters for establishing the diagnostic equation and nomogram. The nomogram showed favorable calibration and discrimination with an area under the receiver operating characteristic curve (AUC) of 0.857 in the training set, 0.851 in the testing set, and 0.848 in the complete set. Meanwhile, decision curve analysis demonstrated the clinical utility of the nomogram.</p><p><strong>Conclusions: </strong>A nomogram using CPET to distinguish anxiety/depression from CAD was developed. It may optimize the disease management and improve patient prognosis.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"405"},"PeriodicalIF":3.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884784","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":"Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions.","authors":"Meron W Shiferaw, Taylor Zheng, Abigail Winter, Leigh Ann Mike, Lingtak-Neander Chan","doi":"10.1186/s12911-024-02824-5","DOIUrl":"10.1186/s12911-024-02824-5","url":null,"abstract":"<p><strong>Background: </strong>Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of this study was to assess the consistency, quality, and accuracy of the responses generated by ChatGPT on healthcare-related inquiries.</p><p><strong>Methods: </strong>A total of 18 open-ended questions including six questions in three defined clinical areas (2 each to address \"what\", \"why\", and \"how\", respectively) were submitted to ChatGPT v3.5 based on real-world usage experience. The experiment was conducted in duplicate using 2 computers. Five investigators independently ranked each response using a 4-point scale to rate the quality of the bot's responses. The Delphi method was used to compare each investigator's score with the goal of reaching at least 80% consistency. The accuracy of the responses was checked using established professional references and resources. When the responses were in question, the bot was asked to provide reference material used for the investigators to determine the accuracy and quality. The investigators determined the consistency, accuracy, and quality by establishing a consensus.</p><p><strong>Results: </strong>The speech pattern and length of the responses were consistent within the same user but different between users. Occasionally, ChatGPT provided 2 completely different responses to the same question. Overall, ChatGPT provided more accurate responses (8 out of 12) to the \"what\" questions with less reliable performance to the \"why\" and \"how\" questions. We identified errors in calculation, unit of measurement, and misuse of protocols by ChatGPT. Some of these errors could result in clinical decisions leading to harm. We also identified citations and references shown by ChatGPT that did not exist in the literature.</p><p><strong>Conclusions: </strong>ChatGPT is not ready to take on the coaching role for either healthcare learners or healthcare professionals. The lack of consistency in the responses to the same question is problematic for both learners and decision-makers. The intrinsic assumptions made by the chatbot could lead to erroneous clinical decisions. The unreliability in providing valid references is a serious flaw in using ChatGPT to drive clinical decision making.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"404"},"PeriodicalIF":3.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884961","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}