Health Care Science最新文献

{"title":"Development trends of etiological research contents and methods of noncommunicable diseases","authors":"Dafang Chen,&nbsp;Yujia Ma,&nbsp;Han Xiao,&nbsp;Zeyu Yan","doi":"10.1002/hcs2.69","DOIUrl":"https://doi.org/10.1002/hcs2.69","url":null,"abstract":"<p>Noncommunicable diseases (NCDs) are a significant public concern, greatly impacting the economic and social development in China. In 2019, NCDs accounted for a staggering 88.5% of total deaths in China, with cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes—the four major chronic diseases—contributing to a premature mortality rate of 16.5% [<span>1</span>]. The complexity of NCDs arises from the involvement of multiple genetic and environmental factors that interact in intricate ways. The complexity is characterized by a multitude of interactions among genes, proteins, and metabolic pathways throughout the various stages of life. Furthermore, these interactions demonstrate time-dependent specificity during the different phases of the life course. Prior research on the etiology of NCDs tended to focus on “specificity,” which overlooked the concept of “universality.” Studies are often conducted from one risk factor, one disease, or one dimension, leading to an insufficient understanding of NCD etiology and less than satisfactory outcomes in prevention and control efforts. Therefore, the aim of this review is to highlight and propose a new trend in NCD etiology research, considering the research focus and research methodology.</p><p>The relationships among NCDs are intricate, and patients often show distinct patterns of multiple diseases, reflecting population heterogeneity in comorbidity. The study of comorbidity patterns among populations affected by NCDs can offer valuable insights for developing effective prevention and management strategies. In a retrospective study by Jansana et al. [<span>2</span>] using electronic health records, five multimorbidity clusters were identified among breast cancer survivors in Spain; notably, the “musculoskeletal and cardiovascular disease” pattern showed a significantly higher risk of mortality than other NCDs. Advancements in computational science contribute to the emergence of network analysis based on graph theory as a powerful tool for understanding the complexity of comorbidity from a holistic and systemic perspective. Graph theory in network analysis facilitates the construction of comorbidity networks in which disease status is represented as nodes and risk associations are shown as edges, thereby visualizing the co-occurrence of diseases in a concise and intuitive manner. Such topological approaches enable the prioritization of disease severity and identification of the core disease within a comorbidity network. Furthermore, network clustering techniques have been applied to identify specific comorbidity patterns in NCDs. However, cautiousness in interpreting the identified patterns is essential because some network topology indexes may lack practical significance. The challenge in interpreting the identified patterns can be addressed by considering association rules. Typically, association rule mining is used to identify comorbidity patterns, and network analysis is used to","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 5","pages":"352-357"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.69","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68180142","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":"Inception of the Indian Digital Health Mission: Connecting…the…Dots","authors":"Gerard Marshall Raj,&nbsp;Sathian Dananjayan,&nbsp;Neeraj Agarwal","doi":"10.1002/hcs2.67","DOIUrl":"https://doi.org/10.1002/hcs2.67","url":null,"abstract":"<p>The purpose of the National Digital Health Mission (or more precisely, the Ayushman Bharat Digital Mission) is to promote and facilitate the evolution of the National Digital Health Ecosystem in India. The Health Facility Registry, the Healthcare Professionals Registry, and the Unified Health Interface are the major components of the proposed system—which is intended to be a co-operative federated architecture with optimal interoperability provision coupled with authorized access.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 5","pages":"345-351"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.67","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68180125","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":"China's aging population: A review of living arrangement, intergenerational support, and wellbeing","authors":"Litao Zhao","doi":"10.1002/hcs2.64","DOIUrl":"https://doi.org/10.1002/hcs2.64","url":null,"abstract":"<p>China's rapid population aging and remarkable family-level changes have raised concerns about the weakening of its family-based elderly care. The last decade indeed has seen a clear departure from multigenerational living to alternative living arrangements such as living with spouse only and solo living. However, ample evidence suggests that Chinese families have demonstrated considerable resilience amidst profound sociodemographic changes. This review article highlights the importance of government–society cooperation in meeting the social challenges of population aging. A key factor is the persistient filial piety norms, which enable children living far or close, migrant or nonmigrant, to rearrange financial, instrumental, and emotional support to aging parents. Equally important is the step-in of the government to share elderly care responsibilities, provide support through deepening pension and healthcare reforms, and implement the active and healthy aging agenda. How the two factors play out over the next decade and beyond will have profound implications on the living arrangement, intergenerational support, and wellbeing of older adults in China.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 5","pages":"317-327"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.64","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68180123","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":"Financial burden of seeking diabetes mellitus care in India: Evidence from a Nationally Representative Sample Survey","authors":"Mehak Nanda,&nbsp;Rajesh Sharma","doi":"10.1002/hcs2.65","DOIUrl":"https://doi.org/10.1002/hcs2.65","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Diabetes mellitus (DM) is a major public health concern in India, and entails a severe burden in terms of disability, death, and economic cost. This study examined the out-of-pocket health expenditure (OOPE) and financial burden associated with DM care in India.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study used data from the latest round of the National Sample Survey on health, which covered 555,115 individuals from 113,823 households in India. In the present study, data of 1216 individuals who sought inpatient treatment and 6527 individuals who sought outpatient care for DM were analysed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In India, 10.04 per 1000 persons reported having DM during the last 15 days before the survey date, varying from 6.94/1000 in rural areas to 17.45/1000 in urban areas. Nearly 38% of Indian households with diabetic members experienced catastrophic health expenditure (at the 10% threshold) and approximately 10% of DM-affected households were pushed below the poverty line because of OOPE, irrespective of the type of care sought. 48.5% of households used distressed sources to finance the inpatient costs of DM. Medicines constituted one of the largest proportion of total health expenditure, regardless of the type of care sought or type of healthcare facility visited. The average monthly OOPE was over 4.5-fold and 2.5-fold higher for households who sought inpatient and outpatient care, respectively, from private health facilities, compared with those treated at public facilities. Notably, the financial burden was more severe for households residing in rural areas, those in lower economic quintiles, those belonging to marginalised social groups, and those using private health facilities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The burden of DM and its associated financial ramifications necessitate policy measures, such as prioritising health promotion and disease prevention strategies, strengthening public healthcare facilities, improved regulation of private healthcare providers, and bringing outpatient services under the purview of health insurance, to manage the diabetes epidemic and mitigate its financial impact.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 5","pages":"291-305"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.65","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68179731","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":"Challenges and opportunities of big data analytics in healthcare","authors":"Priyanshi Goyal,&nbsp;Rishabha Malviya","doi":"10.1002/hcs2.66","DOIUrl":"https://doi.org/10.1002/hcs2.66","url":null,"abstract":"<p>Data science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. The healthcare industry constantly creates large, important databases on patient demographics, treatment plans, results of medical exams, insurance coverage, and more. The data that IoT (Internet of Things) devices collect is of interest to data scientists. Data science can help with the healthcare industry's massive amounts of disparate, structured, and unstructured data by processing, managing, analyzing, and integrating it. To get reliable findings from this data, proper management and analysis are essential. This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. The article discusses the advantages and disadvantages of using big data analytics (BDA) in the medical industry. The insights offered by BDA, which can also aid in making strategic decisions, can assist the healthcare system.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 5","pages":"328-338"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.66","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68179732","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":"COVID-19 retreats and world recovers: A silver lining in the dark cloud","authors":"Amol Chhatrapati Bisen,&nbsp;Sristi Agrawal,&nbsp;Sachin Nashik Sanap,&nbsp;Heamanth Ganesan Ravi Kumar,&nbsp;Nelam Kumar,&nbsp;Rajdeep Gupta,&nbsp;Rabi Sankar Bhatta","doi":"10.1002/hcs2.57","DOIUrl":"https://doi.org/10.1002/hcs2.57","url":null,"abstract":"<p>The coronavirus disease (COVID-19), which the World Health Organization classified as the Sixth Public Health Emergency Of International Concern (PHEIC) on January 30, 2020, is no longer a PHEIC. Millions were affected due to unawareness. The increase in fatalities and shortage of medicine was the first outrage of COVID-19. As per the Johns Hopkins COVID-19 resource center database, it was observed that the disease has spread dynamically across 200+ nations worldwide affecting more than 600 million people from 2019 to 2023, and over thousands of people were victimized regularly at a 2% mortality rate (approx.). In the midway, the mutant variants of concern like omicron, and delta have also created havoc and caused significant impact on public health, global economy, and lifestyle. Since 2019, 3 years now passed and the dynamic disease statistics seem decelerated; moreover, the prevalence of COVID-19 is also fading. The Johns Hopkins resource center has also stopped recording the data of the global pandemic recently from March 10, 2023. Hence, based on the facts, we are presenting a concise report on the pandemic from 2019 to 2023, which includes a brief discussion of the global pandemic. We have highlighted global epidemiology, emphasizing the Indian COVID scenario, vaccination across the globe, and the psychosocial and geopolitical consequences of COVID-19 with a brief background to pathology, clinical management, and the worldwide response against triage. A lot has changed and still needs to change after three tough years of COVID-19. Even though science has progressed and advanced research in medicine is pointing toward future generations, there is no standard care supplied for COVID-19-like calamities. COVID-19 cases might have declined but its influence on the society is still stagnant. This COVID experience has taught us that, despite our bleak beginnings, there is always hope for the future and that we must act with foresight to improve things for future generations.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"264-285"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.57","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50124485","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":"Factors underlying burnout among rural village physicians in Southwestern China","authors":"Xingyue Zhu,&nbsp;Yang Chen,&nbsp;Xingjiang Liao","doi":"10.1002/hcs2.62","DOIUrl":"https://doi.org/10.1002/hcs2.62","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Primary healthcare doctors in China often experience problems with occupational burnout, a condition known to relate to high job stress and low wages. In China, many medical alliances have recently been established in rural areas, where village physicians work as healthcare gatekeepers. However, burnout in village physicians in the context of medical alliances remains underresearched.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This cross-sectional survey was conducted among 100 village physicians practicing at village clinics in Qiandongnan prefecture, Guizhou province, China. An online questionnaire was distributed to assess physicians' demographic characteristics and work situations. Burnout was measured using the Oldenburg Burnout Inventory (validated Chinese version). A multivariate linear model with stepwise procedure was used to estimate the effects of factors of interest on burnout, focusing particularly on actions within the medical alliance that involved respondents' clinics, such as training and support for village physicians provided by higher-level facilities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The overall response rate was 79%. The mean burnout score was 38.09 (standard deviation, 4.55; range, 25–47). The multivariate analysis showed that fewer working years and too much farming work were significantly related to exacerbation of burnout. Greater medical services in the total workload and greater support from higher-level facilities were associated with burnout alleviation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Close connections and interactions across medical alliance member facilities could facilitate reduction in burnout for village physicians practicing as primary care gatekeepers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"233-241"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.62","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50154571","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":"Large language models in health care: Development, applications, and challenges","authors":"Rui Yang,&nbsp;Ting Fang Tan,&nbsp;Wei Lu,&nbsp;Arun James Thirunavukarasu,&nbsp;Daniel Shu Wei Ting,&nbsp;Nan Liu","doi":"10.1002/hcs2.61","DOIUrl":"https://doi.org/10.1002/hcs2.61","url":null,"abstract":"<p>Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre-training. In the last 3 years, these domain-specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"255-263"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.61","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50119025","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":"Differential effectiveness of COVID-19 health behaviors: The role of mental health conditions in mask-wearing, social distancing, and hygiene practice","authors":"Yusen Zhai,&nbsp;Xue Du","doi":"10.1002/hcs2.60","DOIUrl":"https://doi.org/10.1002/hcs2.60","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Mental health conditions are known to increase susceptibility to infectious diseases, including coronavirus disease 2019 (COVID-19). Health behaviors play a crucial role in mitigating this susceptibility. We aim to examine the differential effectiveness of COVID-19 preventive health behaviors among individuals, considering the presence or absence of specific mental health disorders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Multivariable logistic regression with interaction terms was performed to examine whether associations between adherence to health behaviors and COVID-19 infection were conditional on depression, anxiety, or eating disorders in a national sample of adults (<i>N</i> = 61,891) from 140 US universities, 2020–2021.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Adjusting for age, race/ethnicity, and gender/sex, the effectiveness of mask-wearing was significant and comparable among individuals with and without depression, anxiety, or eating disorders. Social distancing provided significantly less protection among individuals with depression, anxiety, or eating disorders. Hygiene practice provided significantly less protection among individuals with anxiety.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Mask-wearing is robustly effective in the prevention of COVID-19 among individuals. However, social distancing and hygiene practice provide less significant protection among individuals with certain mental health conditions, suggesting the importance of prioritizing these individuals for additional preventive measures (e.g., vaccines targeting variants) and mitigation strategies (e.g., financial assistance, targeted mental health care, health education).</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"286-290"},"PeriodicalIF":0.0,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.60","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50151333","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":"Predicting venous thromboembolism (VTE) risk in cancer patients using machine learning","authors":"Samir Khan Townsley,&nbsp;Debraj Basu,&nbsp;Jayneel Vora,&nbsp;Ted Wun,&nbsp;Chen-Nee Chuah,&nbsp;Prabhu R. V. Shankar","doi":"10.1002/hcs2.55","DOIUrl":"https://doi.org/10.1002/hcs2.55","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The association between cancer and venous thromboembolism (VTE) is well-established with cancer patients accounting for approximately 20% of all VTE incidents. In this paper, we have performed a comparison of machine learning (ML) methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population, identified important features (clinical biomarkers) for ML model predictions, and examined how different approaches to reducing the number of features used in the model impact model performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We have developed an ML pipeline including three separate feature selection processes and applied it to routine patient care data from the electronic health records of 1910 cancer patients at the University of California Davis Medical Center.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our ML-based prediction model achieved an area under the receiver operating characteristic curve of 0.778 ± 0.006 (mean ± SD) when trained on a set of 15 features. This result is comparable with the model performance when trained on all features in our feature pool [0.779 ± 0.006 (mean ± SD) with 29 features]. Our result surpasses the most validated clinical scoring system for VTE risk assessment in cancer patients by 16.1%. We additionally found cancer stage information to be a useful predictor after all performed feature selection processes despite not being used in existing score-based approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>From these findings, we observe that ML can offer new insights and a significant improvement over the most validated clinical VTE risk scoring systems in cancer patients. The results of this study also allowed us to draw insight into our feature pool and identify the features that could have the most utility in the context of developing an efficient ML classifier. While a model trained on our entire feature pool of 29 features significantly outperformed the traditionally used clinical scoring system, we were able to achieve an equivalent performance using a subset of only 15 features through strategic feature selection methods. These results are encouraging for potential applications of ML to predicting cancer-associated VTE in clinical settings such as in bedside decision support systems where feature availability may be limited.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 4","pages":"205-222"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.55","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150385","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}