Health data science最新文献

{"title":"Benchmarking of Large Language Models for the Dental Admission Test.","authors":"Yu Hou, Jay Patel, Liya Dai, Emily Zhang, Yang Liu, Zaifu Zhan, Pooja Gangwani, Rui Zhang","doi":"10.34133/hds.0250","DOIUrl":"https://doi.org/10.34133/hds.0250","url":null,"abstract":"<p><p><b>Background:</b> Large language models (LLMs) have shown promise in educational applications, but their performance on high-stakes admissions tests, such as the Dental Admission Test (DAT), remains unclear. Understanding the capabilities and limitations of these models is critical for determining their suitability in test preparation. <b>Methods:</b> This study evaluated the ability of 16 LLMs, including general-purpose models (e.g., GPT-3.5, GPT-4, GPT-4o, GPT-o1, Google's Bard, mistral-large, and Claude), domain-specific fine-tuned models (e.g., DentalGPT, MedGPT, and BioGPT), and open-source models (e.g., Llama2-7B, Llama2-13B, Llama2-70B, Llama3-8B, and Llama3-70B), to answer questions from a sample DAT. Quantitative analysis was performed to assess model accuracy in different sections, and qualitative thematic analysis by subject matter experts examined specific challenges encountered by the models. <b>Results:</b> GPT-4o and GPT-o1 outperformed others in text-based questions assessing knowledge and comprehension, with GPT-o1 achieving perfect scores in the natural sciences (NS) and reading comprehension (RC) sections. Open-source models such as Llama3-70B also performed competitively in RC tasks. However, all models, including GPT-4o, struggled substantially with perceptual ability (PA) items, highlighting a persistent limitation in handling image-based tasks requiring visual-spatial reasoning. Fine-tuned medical models (e.g., DentalGPT, MedGPT, and BioGPT) demonstrated moderate success in text-based tasks but underperformed in areas requiring critical thinking and reasoning. Thematic analysis identified key challenges, including difficulties with stepwise problem-solving, transferring knowledge, comprehending intricate questions, and hallucinations, particularly on advanced items. <b>Conclusions:</b> While LLMs show potential for reinforcing factual knowledge and supporting learners, their limitations in handling higher-order cognitive tasks and image-based reasoning underscore the need for judicious integration with instructor-led guidance and targeted practice. This study provides valuable insights into the capabilities and limitations of current LLMs in preparing prospective dental students and highlights pathways for future innovations to improve performance across all cognitive skills assessed by the DAT.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"5 ","pages":"0250"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Sex and Age Biases in Multimodal Large Language Models for Skin Disease Identification from Dermatoscopic Images.","authors":"Zhiyu Wan, Yuhang Guo, Shunxing Bao, Qian Wang, Bradley A Malin","doi":"10.34133/hds.0256","DOIUrl":"https://doi.org/10.34133/hds.0256","url":null,"abstract":"<p><p><b>Background:</b> Multimodal large language models (LLMs) have shown potential in various health-related fields. However, many healthcare studies have raised concerns about the reliability and biases of LLMs in healthcare applications. <b>Methods:</b> To explore the practical application of multimodal LLMs in skin disease identification, and to evaluate sex and age biases, we tested the performance of 2 popular multimodal LLMs, ChatGPT-4 and LLaVA-1.6, across diverse sex and age groups using a subset of a large dermatoscopic dataset containing around 10,000 images and 3 skin diseases (melanoma, melanocytic nevi, and benign keratosis-like lesions). <b>Results:</b> In comparison to 3 deep learning models (VGG16, ResNet50, and Model Derm) based on convolutional neural network (CNN), one vision transformer model (Swin-B), we found that ChatGPT-4 and LLaVA-1.6 demonstrated overall accuracies that were 3% and 23% higher (and F1-scores that were 4% and 34% higher), respectively, than the best performing CNN-based baseline while maintaining accuracies that were 38% and 26% lower (and F1-scores that were 38% and 19% lower), respectively, than Swin-B. Meanwhile, ChatGPT-4 is generally unbiased in identifying these skin diseases across sex and age groups, while LLaVA-1.6 is generally unbiased across age groups, in contrast to Swin-B, which is biased in identifying melanocytic nevi. <b>Conclusions:</b> This study suggests the usefulness and fairness of LLMs in dermatological applications, aiding physicians and practitioners with diagnostic recommendations and patient screening. To further verify and evaluate the reliability and fairness of LLMs in healthcare, experiments using larger and more diverse datasets need to be performed in the future.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"5 ","pages":"0256"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-Hospital Mortality Prediction among Intensive Care Unit Patients with Acute Ischemic Stroke: A Machine Learning Approach.","authors":"Jack A Cummins, Ben S Gerber, Mayuko Ito Fukunaga, Nils Henninger, Catarina I Kiefe, Feifan Liu","doi":"10.34133/hds.0179","DOIUrl":"10.34133/hds.0179","url":null,"abstract":"<p><p><b>Background:</b> Acute ischemic stroke is a leading cause of death in the United States. Identifying patients with stroke at high risk of mortality is crucial for timely intervention and optimal resource allocation. This study aims to develop and validate machine learning-based models to predict in-hospital mortality risk for intensive care unit (ICU) patients with acute ischemic stroke and identify important associated factors. <b>Methods:</b> Our data include 3,489 acute ischemic stroke admissions to the ICU for patients not discharged or dead within 48 h from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Demographic, hospitalization type, procedure, medication, intake (intravenous and oral), laboratory, vital signs, and clinical assessment [e.g., Glasgow Coma Scale Scores (GCS)] during the initial 48 h of admissions were used to predict in-hospital mortality after 48 h of ICU admission. We explored 3 machine learning models (random forests, logistic regression, and XGBoost) and applied Bayesian optimization for hyperparameter tuning. Important features were identified using learned coefficients. <b>Results:</b> Experiments show that XGBoost tuned for area under the receiver operating characteristic curve (AUC ROC) was the best performing model (AUC ROC 0.86, F1 0.52), compared to random forests (AUC ROC 0.85, F1 0.47) and logistic regression (AUC ROC 0.75, F1 0.40). Top features include GCS, blood urea nitrogen, and Richmond RASS score. The model also demonstrates good fairness for males versus females and across racial/ethnic groups. <b>Conclusions:</b> Machine learning has shown great potential in predicting in-hospital mortality risk for people with acute ischemic stroke in the ICU setting. However, more ethical considerations need to be applied to ensure that performance differences across different racial/ethnic groups will not exacerbate existing health disparities and will not harm historically marginalized populations.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"5 ","pages":"0179"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence and Risk Factors of Type 2 Diabetes Mellitus among Depression Inpatients from 2005 to 2018 in Beijing, China.","authors":"Peng Gao, Fude Yang, Qiuyue Ma, Botao Ma, Wenzhan Jing, Jue Liu, Moning Guo, Juan Li, Zhiren Wang, Min Liu","doi":"10.34133/hds.0111","DOIUrl":"10.34133/hds.0111","url":null,"abstract":"<p><p><b>Background:</b> There are few data on the comorbidity of diabetes in Chinese patients with depression. We aimed to calculate the prevalence and explore risk factors of type 2 diabetes mellitus (T2DM) among depression inpatients from 2005 to 2018 in Beijing. <b>Methods:</b> This study is a cross-sectional study. The data collected from 19 specialized psychiatric hospitals in Beijing were analyzed. The prevalence of T2DM and its distribution were analyzed. The multivariable logistic regression was performed to explore the risk factors of T2DM. <b>Results:</b> A total of 20,899 depression inpatients were included. The prevalence of T2DM was 9.13% [95% confidence interval (CI), 8.74% to 9.52%]. The prevalence of T2DM showed an upward trend with year (<i>P</i> for trend < 0.001) and age (<i>P</i> for trend < 0.001). The prevalence of T2DM was higher among readmitted patients (12.97%) and patients with comorbid hypertension (26.16%), hyperlipidemia (21.28%), and nonalcoholic fatty liver disease (NAFLD) (18.85%). The prevalence of T2DM in females was lower than in males among patients aged 18 to 59 years, while the prevalence of T2DM in females was higher than in males among patients aged ≥60 years. T2DM was associated with older age [adjusted odds ratios (aORs) ranged from 3.68 to 29.95, <i>P</i> < 0.001], hypertension (aOR, 3.01; 95% CI, 2.70 to 3.35; <i>P</i> < 0.001), hyperlipidemia (aOR, 1.69; 95% CI, 1.50 to 1.91; <i>P</i> < 0.001), and NAFLD (aOR, 1.58; 95% CI, 1.37 to 1.82; <i>P</i> < 0.001). <b>Conclusions:</b> The prevalence of T2DM among depression inpatients from 2005 to 2018 in Beijing was high and increased with the year. Depression inpatients who were older and with hypertension, hyperlipidemia, and NAFLD had a higher prevalence and risk of T2DM.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"5 ","pages":"0111"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Caring for the \"Osteo-Cardiovascular Faller\": Associations between Multimorbidity and Fall Transitions among Middle-Aged and Older Chinese.","authors":"Mingzhi Yu, Longbing Ren, Rui Yang, Yuling Jiang, Shijie Cui, Jingjing Wang, Shaojie Li, Yang Hu, Zhouwei Liu, Yifei Wu, Gongzi Zhang, Ye Peng, Lihai Zhang, Yao Yao","doi":"10.34133/hds.0151","DOIUrl":"10.34133/hds.0151","url":null,"abstract":"<p><p><b>Background:</b> It is still uncertain how multimorbidity patterns affect transitions between fall states among middle-aged and older Chinese. <b>Methods:</b> Data were obtained from China Health and Retirement Longitudinal Study (CHARLS) 2011-2018. We utilized latent class analysis to categorize baseline multimorbidity patterns, Markov multi-state model to explore the impact of multimorbidity characterized by condition counts and multimorbidity patterns on subsequent fall transitions, and Cox proportional hazard models to assess hazard ratios of each transition. <b>Results:</b> A total of 14,244 participants aged 45 years and older were enrolled at baseline. Among these participants, 11,956 (83.9%) did not have a fall history in the last 2 years, 1,054 (7.4%) had mild falls, and 1,234 (8.7%) had severe falls. Using a multi-state model, 10,967 transitions were observed during a total follow-up of 57,094 person-times, 6,527 of which had worsening transitions and 4,440 had improving transitions. Among 6,711 multimorbid participants, osteo-cardiovascular (20.5%), pulmonary-digestive-rheumatic (30.5%), metabolic-cardiovascular (22.9%), and neuropsychiatric-sensory (26.1%) patterns were classified. Multimorbid participants had significantly higher risks of transitions compared with other participants. Among 4 multimorbidity patterns, osteo-cardiovascular pattern had higher transition risks than other 3 patterns. <b>Conclusions:</b> Multimorbidity, especially the \"osteo-cardiovascular pattern\" identified in this study, was associated with higher risks of fall transitions among middle-aged and older Chinese. Generally, the effect of multimorbidity is more significant in older adults than in middle-aged adults. Findings from this study provide facts and evidence for fall prevention, and offer implications for clinicians to target on vulnerable population, and for public health policymakers to allocate healthcare resources.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"5 ","pages":"0151"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11836196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG-LM: Understanding Electrocardiogram with a Large Language Model.","authors":"Kai Yang, Massimo Hong, Jiahuan Zhang, Yizhen Luo, Suyuan Zhao, Ou Zhang, Xiaomao Yu, Jiawen Zhou, Liuqing Yang, Ping Zhang, Mu Qiao, Zaiqing Nie","doi":"10.34133/hds.0221","DOIUrl":"10.34133/hds.0221","url":null,"abstract":"<p><p><b>Background:</b> The electrocardiogram (ECG) is a valuable, noninvasive tool for monitoring heart-related conditions, providing critical insights. However, the interpretation of ECG data alongside patient information demands substantial medical expertise and resources. While deep learning methods help streamline this process, they often fall short in integrating patient data with ECG readings and do not provide the nuanced clinical suggestions and insights necessary for accurate diagnosis. <b>Methods:</b> Although recent advancements in multi-modal large language modeling have propelled their application scope beyond the natural language processing domain, their applicability to ECG processing remains largely unexplored, partly due to the lack of text-ECG data. To this end, we develop ECG-Language Model (ECG-LM), the first multi-modal large language model able to process natural language and understand ECG signals. The model employs a specialized ECG encoder that transforms raw ECG signals into a high-dimensional feature space, which is then aligned with the textual feature space derived from the large language model. To address the scarcity of text-ECG data, we generated text-ECG pairs by leveraging detailed ECG pattern descriptions from medical guidelines, creating a robust dataset for pre-training ECG-LM. Additionally, we fine-tune ECG-LM with public clinical conversation datasets and build an additional supervised fine-tuning dataset based on real clinical data from the hospital, aiming to provide a more comprehensive and customized user experience. <b>Results:</b> ECG-LM outperforms existing few-shot and zero-shot solutions in cardiovascular disease detection across all 3 tasks (diagnostic, rhythm, and form) while also demonstrating strong potential in ECG-related question answering. <b>Conclusions:</b> The results across various tasks demonstrate that ECG-LM effectively captures the intricate features of ECGs, showcasing its versatility in applications such as disease prediction and advanced question answering.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"5 ","pages":"0221"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Modal CLIP-Informed Protein Editing.","authors":"Mingze Yin, Hanjing Zhou, Yiheng Zhu, Miao Lin, Yixuan Wu, Jialu Wu, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou, Jintai Chen, Jian Wu","doi":"10.34133/hds.0211","DOIUrl":"10.34133/hds.0211","url":null,"abstract":"<p><p><b>Background:</b> Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. However, current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions, limiting their interactivity with human feedback. <b>Methods:</b> To fill these gaps, we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality learning. Our approach comprises 2 stages: In the pretraining stage, contrastive learning aligns protein-biotext representations encoded by 2 large language models (LLMs). Subsequently, during the protein editing stage, the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein sequences. <b>Results:</b> Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains, including enzyme catalytic activity, protein stability, and antibody-specific binding ability. ProtET improves the state-of-the-art results by a large margin, leading to substantial stability improvements of 16.67% and 16.90%. <b>Conclusions:</b> This capability positions ProtET to advance real-world artificial protein editing, potentially addressing unmet academic, industrial, and clinical needs.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0211"},"PeriodicalIF":0.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11658819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Burden of Type 2 Diabetes in Adolescents and Young Adults in China: A Secondary Analysis from the Global Burden of Disease Study 2021.","authors":"Junting Yang, Siwei Deng, Houyu Zhao, Feng Sun, Xiaotong Zou, Linong Ji, Siyan Zhan","doi":"10.34133/hds.0210","DOIUrl":"10.34133/hds.0210","url":null,"abstract":"<p><p><b>Background:</b> Early-onset type 2 diabetes (T2D) is an increasingly serious public health issue, particularly in China. This study aimed to analyze the characteristics of disease burden, secular trend, and attributable risk factors of early-onset T2D in China. <b>Methods:</b> Using data from the Global Burden of Disease (GBD) 2021, we analyzed the age-standardized rate (ASR) of incidence, disability-adjusted life years (DALYs), and mortality rates of T2D among individuals aged 15 to 39 years in China from 1990 to 2021. Joinpoint regression analysis was employed to analyze secular trend, calculating the average annual percent change (AAPC). We also examined changes in the proportion of early-onset T2D within the total T2D burden and its attributable risk factors. <b>Results:</b> From 1990 to 2021, the ASR of incidence of early-onset T2D in China increased from 140.20 [95% uncertainty interval (UI): 89.14 to 204.74] to 315.97 (95% UI: 226.75 to 417.55) per 100,000, with an AAPC of 2.67% (95% CI: 2.60% to 2.75%, <i>P</i> < 0.001). DALYs rose from 116.29 (95% UI: 78.51 to 167.05) to 267.47 (95% UI: 171.08 to 387.38) per 100,000, with an AAPC of 2.75% (95% CI: 2.64% to 2.87%, <i>P</i> < 0.001). Mortality rates slightly decreased from 0.30 (95% UI: 0.24 to 0.38) to 0.28 (95% UI: 0.23 to 0.34) per 100,000, with an AAPC of -0.22% (95% CI: -0.33% to -0.11%, <i>P</i> < 0.001). The 15 to 19 years age group showed the fastest increase in incidence (AAPC: 4.08%, 95% CI: 3.93% to 4.29%, <i>P</i> < 0.001). The burden was consistently higher and increased more rapidly among males compared to females. The proportion of early-onset T2D within the total T2D burden fluctuated but remained higher than global levels. In 2021, high body mass index (BMI) was the primary attributable risk factor for DALYs of early-onset T2D (59.85%, 95% UI: 33.54% to 76.65%), and its contribution increased substantially from 40.08% (95% UI: 20.71% to 55.79%) in 1990, followed by ambient particulate matter pollution (14.77%, 95% UI: 8.24% to 21.24%) and diet high in red meat (9.33%, 95% UI: -1.42% to 20.06%). <b>Conclusion:</b> The disease burden of early-onset T2D in China is rapidly increasing, particularly among younger populations and males. Despite a slight decrease in mortality rates, the continued rapid increase in incidence and DALYs indicates a need for strengthened prevention and management strategies, especially interventions targeting younger age groups. High BMI and environmental pollution emerge as primary risk factors and should be prioritized in future interventions.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0210"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11651706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis.","authors":"Siqi Li, Di Miao, Qiming Wu, Chuan Hong, Danny D'Agostino, Xin Li, Yilin Ning, Yuqing Shang, Ziwen Wang, Molei Liu, Huazhu Fu, Marcus Eng Hock Ong, Hamed Haddadi, Nan Liu","doi":"10.34133/hds.0196","DOIUrl":"10.34133/hds.0196","url":null,"abstract":"<p><p><b>Background:</b> Federated learning (FL) holds promise for safeguarding data privacy in healthcare collaborations. While the term \"FL\" was originally coined by the engineering community, the statistical field has also developed privacy-preserving algorithms, though these are less recognized. Our goal was to bridge this gap with the first comprehensive comparison of FL frameworks from both domains. <b>Methods:</b> We assessed 7 FL frameworks, encompassing both engineering-based and statistical FL algorithms, and compared them against local and centralized modeling of logistic regression and least absolute shrinkage and selection operator (Lasso). Our evaluation utilized both simulated data and real-world emergency department data, focusing on comparing both estimated model coefficients and the performance of model predictions. <b>Results:</b> The findings reveal that statistical FL algorithms produce much less biased estimates of model coefficients. Conversely, engineering-based methods can yield models with slightly better prediction performance, occasionally outperforming both centralized and statistical FL models. <b>Conclusion:</b> This study underscores the relative strengths and weaknesses of both types of methods, providing recommendations for their selection based on distinct study characteristics. Furthermore, we emphasize the critical need to raise awareness of and integrate these methods into future applications of FL within the healthcare domain.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0196"},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Meta-Model for Predicting the Likelihood of Receiving Blood Transfusion in Non-traumatic Intensive Care Unit Patients.","authors":"Alireza Rafiei, Ronald Moore, Tilendra Choudhary, Curtis Marshall, Geoffrey Smith, John D Roback, Ravi M Patel, Cassandra D Josephson, Rishikesan Kamaleswaran","doi":"10.34133/hds.0197","DOIUrl":"10.34133/hds.0197","url":null,"abstract":"<p><p><b>Background:</b> Blood transfusions, crucial in managing anemia and coagulopathy in intensive care unit (ICU) settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily targeted a particular patient demographic with unique medical conditions and focused on a single type of blood transfusion. This study aims to develop an advanced machine learning-based model to predict the probability of transfusion necessity over the next 24 h for a diverse range of non-traumatic ICU patients. <b>Methods:</b> We conducted a retrospective cohort study on 72,072 non-traumatic adult ICU patients admitted to a high-volume US metropolitan academic hospital between 2016 and 2020. We developed a meta-learner and various machine learning models to serve as predictors, training them annually with 4-year data and evaluating on the fifth, unseen year, iteratively over 5 years. <b>Results:</b> The experimental results revealed that the meta-model surpasses the other models in different development scenarios. It achieved notable performance metrics, including an area under the receiver operating characteristic curve of 0.97, an accuracy rate of 0.93, and an F1 score of 0.89 in the best scenario. <b>Conclusion:</b> This study pioneers the use of machine learning models for predicting the likelihood of blood transfusion receipt in a diverse cohort of critically ill patients. The findings of this evaluation confirm that our model not only effectively predicts transfusion reception but also identifies key biomarkers for making transfusion decisions.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0197"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}