Health data science最新文献

{"title":"Building a Machine Learning-based Ambulance Dispatch Triage Model for Emergency Medical Services.","authors":"Han Wang, Qin Xiang Ng, Shalini Arulanandam, Colin Tan, Marcus E H Ong, Mengling Feng","doi":"10.34133/hds.0008","DOIUrl":"10.34133/hds.0008","url":null,"abstract":"<p><strong>Background: </strong>In charge of dispatching the ambulances, Emergency Medical Services (EMS) call center specialists often have difficulty deciding the acuity of a case given the information they can gather within a limited time. Although there are protocols to guide their decision-making, observed performance can still lack sensitivity and specificity. Machine learning models have been known to capture complex relationships that are subtle, and well-trained data models can yield accurate predictions in a split of a second.</p><p><strong>Methods: </strong>In this study, we proposed a proof-of-concept approach to construct a machine learning model to better predict the acuity of emergency cases. We used more than 360,000 structured emergency call center records of cases received by the national emergency call center in Singapore from 2018 to 2020. Features were created using call records, and multiple machine learning models were trained.</p><p><strong>Results: </strong>A Random Forest model achieved the best performance, reducing the over-triage rate by an absolute margin of 15% compared to the call center specialists while maintaining a similar level of under-triage rate.</p><p><strong>Conclusions: </strong>The model has the potential to be deployed as a decision support tool for dispatchers alongside current protocols to optimize ambulance dispatch triage and the utilization of emergency ambulance resources.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"0008"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880163/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47990713","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 Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.","authors":"Shuaitong Zhang, Wei Mu, Di Dong, Jingwei Wei, Mengjie Fang, Lizhi Shao, Yu Zhou, Bingxi He, Song Zhang, Zhenyu Liu, Jianhua Liu, Jie Tian","doi":"10.34133/hds.0005","DOIUrl":"10.34133/hds.0005","url":null,"abstract":"<p><strong>Importance: </strong>Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.</p><p><strong>Highlights: </strong>We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.</p><p><strong>Conclusion: </strong>AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"0005"},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10877701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42646773","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":"Current Trends and Challenges in Drug-likeness Prediction: Are They Generalizable and Interpretable?","authors":"Wenyu Zhu, Yanxing Wang, Yan Niu, Liangren Zhang, Zhenming Liu","doi":"10.34133/hds.0098","DOIUrl":"https://doi.org/10.34133/hds.0098","url":null,"abstract":"Importance : Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials, and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential. To this end, a robust drug-likeness prediction method is indispensable. Various approaches, including discriminative rules, statistical models, and machine learning models, have been developed to predict drug-likeness based on physiochemical properties and structural features. Notably, recent advancements in novel deep learning techniques have significantly advanced drug-likeness prediction, especially in classification performance. Highlights : In this review, we addressed the evolving landscape of drug-likeness prediction, with emphasis on methods employing novel deep learning techniques, and highlighted the current challenges in drug-likeness prediction, specifically regarding the aspects of generalization and interpretability. Moreover, we explored potential remedies and outlined promising avenues for future research. Conclusion : Despite the hurdles of generalization and interpretability, novel deep learning techniques have great potential in drug-likeness prediction and are worthy of further research efforts.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135104665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plug-in Models: A Promising Direction for Molecular Generation","authors":"Ningfeng Liu, Hongwei Jin, Liangren Zhang, Zhenming Liu","doi":"10.34133/hds.0092","DOIUrl":"https://doi.org/10.34133/hds.0092","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135843241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationships of Residential Distance to Major Traffic Roads with Dementia Incidence and Brain Structure Measures: Mediation Role of Air Pollution","authors":"Chenglong Li, Darui Gao, Yutong Samuel Cai, Jie Liang, Yongqian Wang, Yang Pan, Wenya Zhang, Fanfan Zheng, Wuxiang Xie","doi":"10.34133/hds.0091","DOIUrl":"https://doi.org/10.34133/hds.0091","url":null,"abstract":"Background: Uncertainty exists regarding the operating pathways between near-roadway exposure and dementia incidence. We intend to examine relationships between proximity to major roadways with dementia incidence and brain MRI structure measures, and potential mediation roles of air and noise pollution. Methods: The cohort study was based on the UK Biobank. Baseline survey was conducted from 2006 to 2010, with linkage to electronic health records conducted for follow-up. Residential distance to major roadways was ascertained residential address postcode. A land use regression model was applied for estimating traffic-related air pollution at residence. Dementia incidence was ascertained using national administrative databases. Brain MRI measures were derived as image-derived phenotypes, including total brain, white matter, gray matter, and peripheral cortical gray matter. Results: We included 460,901 participants [mean (SD) age: 57.1 (8.1) years; men: 45.7%]. Compared with individuals living &gt;1,000 m from major traffic roads, living ≤1,000 m was associated with a 13% to 14% higher dementia risk, accounting for 10% of dementia cases. Observed association between residential distance and dementia was substantially mediated by traffic-related air pollution, mainly nitrogen dioxide (proportion mediated: 63.6%; 95% CI, 27.0 to 89.2%) and PM 2.5 (60.9%, 26.8 to 87.0%). The shorter residential distance was associated with smaller volumes of brain structures, which was also mediated by traffic-related air pollutants. No significant mediation role was observed of noise pollution. Conclusions: The shorter residential distance to major roads was associated with elevated dementia incidence and smaller brain structure volumes, which was mainly mediated by traffic-related air pollution.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135699128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"#ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning.","authors":"Abeed Sarker, Sahithi Lakamana, Yuting Guo, Yao Ge, Abimbola Leslie, Omolola Okunromade, Elena Gonzalez-Polledo, Jeanmarie Perrone, Anne Marie McKenzie-Brown","doi":"10.34133/hds.0078","DOIUrl":"10.34133/hds.0078","url":null,"abstract":"<p><strong>Background: </strong>Due to the high burden of chronic pain, and the detrimental public health consequences of its treatment with opioids, there is a high-priority need to identify effective alternative therapies. Social media is a potentially valuable resource for knowledge about self-reported therapies by chronic pain sufferers.</p><p><strong>Methods: </strong>We attempted to (a) verify the presence of large-scale chronic pain-related chatter on Twitter, (b) develop natural language processing and machine learning methods for automatically detecting self-disclosures, (c) collect longitudinal data posted by them, and (d) semiautomatically analyze the types of chronic pain-related information reported by them. We collected data using chronic pain-related hashtags and keywords and manually annotated 4,998 posts to indicate if they were self-reports of chronic pain experiences. We trained and evaluated several state-of-the-art supervised text classification models and deployed the best-performing classifier. We collected all publicly available posts from detected cohort members and conducted manual and natural language processing-driven descriptive analyses.</p><p><strong>Results: </strong>Interannotator agreement for the binary annotation was 0.82 (Cohen's kappa). The RoBERTa model performed best (F₁ score: 0.84; 95% confidence interval: 0.80 to 0.89), and we used this model to classify all collected unlabeled posts. We discovered 22,795 self-reported chronic pain sufferers and collected over 3 million of their past posts. Further analyses revealed information about, but not limited to, alternative treatments, patient sentiments about treatments, side effects, and self-management strategies.</p><p><strong>Conclusion: </strong>Our social media based approach will result in an automatically growing large cohort over time, and the data can be leveraged to identify effective opioid-alternative therapies for diverse chronic pain types.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10852024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47203150","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":"Impact of COVID-19 Prevention and Control on the Influenza Epidemic in China: A Time Series Study.","authors":"Zirui Guo, Li Zhang, Jue Liu, Min Liu","doi":"10.34133/2022/9830159","DOIUrl":"10.34133/2022/9830159","url":null,"abstract":"<p><p><i>Background</i>. COVID-19 prevention and control measures might affect influenza epidemic in China since the nonpharmaceutical interventions (NPIs) and behavioral changes contain transmission of both SARS-CoV-2 and influenza virus. We aimed to explore the impact of COVID-19 prevention and control measures on influenza using data from the National Influenza Surveillance Network.<i>Methods</i>. The percentage of influenza-like illness (ILI%) in southern and northern China from 2010 to 2022 was collected from the National Influenza Surveillance Network. Weekly ILI% observed value from 2010 to 2019 was used to calculate estimated annual percentage change (EAPC) of ILI% with 95% confidence intervals (CIs). Time series analysis was applied to estimate weekly ILI% predicted values in 2020/2021 and 2021/2022 season. Impact index was used to explore the impact of COVID-19 prevention and control on influenza during nonpharmaceutical intervention and vaccination stages.<i>Results</i>. China influenza activity was affected by the COVID-19 pandemic and different prevention and control measures during 2020-2022. In 2020/2021 season, weekly ILI% observed value in both southern and northern China was at a low epidemic level, and there was no obvious epidemic peak in winter and spring. In 2021/2022 season, weekly ILI% observed value in southern and northern China showed a small peak in summer and epidemic peak in winter and spring. The weekly ILI% observed value was generally lower than the predicted value in southern and northern China during 2020-2022. The median of impact index of weekly ILI% was 15.11% in north and 22.37% in south in 2020/2021 season and decreased significantly to 2.20% in north and 3.89% in south in 2021/2022 season.<i>Conclusion</i>. In summary, there was a significant decrease in reported ILI in China during the 2020-2022 COVID-19 pandemic, particularly in winter and spring. Reduction of influenza virus infection might relate to everyday Chinese public health COVID-19 interventions. The confirmation of this relationship depends on future studies.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9830159"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44046970","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":"Research Trends and Emerging Hotspots of Lung Cancer Surgery during 2012-2021: A 10-Year Bibliometric and Network Analysis.","authors":"Jingyi Wu, Chenlu Bao, Ganwei Liu, Shushi Meng, Yunwei Lu, Pengfei Li, Jian Zhou","doi":"10.34133/2022/9797842","DOIUrl":"10.34133/2022/9797842","url":null,"abstract":"<p><p><i>Background</i>. Lung cancer remains the leading cause of death because of cancer globally in the past years. To inspire researchers with new targets and path-breaking directions for lung cancer research, this study is aimed at exploring the research trends and emerging hotspots in the lung cancer surgery literature in the recent decade.<i>Methods</i>. This cross-sectional study combined bibliometric and network analysis techniques to undertake a quantitative analysis of lung cancer surgery literature. Dimensions database was searched using keywords in a 10-year period (2012-2021). Publications were characterized by publication year, research countries, field citation ratio, cooperation status, research area, and emerging hotspots.<i>Results</i>. Overall, global scholarly outputs of lung cancer surgery had almost doubled during the recent decade, with China, Japan, and the United States leading the way, while Denmark and Belgium predominated in terms of scientific influence. Network analysis showed that international cooperation accounted for a relatively small portion in lung cancer surgery research, and the United States, China, and Europe were the prominent centers of international cooperation network. In the recent decade, research of lung cancer surgery majored in prevention, biomedical imaging, rehabilitation, and genetics, and the emerging research hotspots transformed into immunotherapy. Research on immunotherapy showed a considerable increase in scientific influence in the latest year.<i>Conclusions</i>. The study findings are expected to provide researchers and policymakers with interesting insights into the changing trends of lung cancer surgery research and further generate evidence to support decision-making in improving prognosis for patients with lung cancer.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9797842"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48470813","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":"Clinician Data Scientists-Preparing for the Future of Medicine in the Digital World.","authors":"Fulin Wang, Lin Ma, Georgina Moulton, Mai Wang, Luxia Zhang","doi":"10.34133/2022/9832564","DOIUrl":"10.34133/2022/9832564","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9832564"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42051556","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":"Health Data Sharing Platforms: Serving Researchers through Provision of Access to High-Quality Data for Reuse.","authors":"Rebecca Li, Nina Hill, Catherine D'Arcy, Amrutha Baskaran, Patricia Bradford","doi":"10.34133/2022/9768384","DOIUrl":"10.34133/2022/9768384","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9768384"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42312100","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}