Machine Learning for Burns Clinical Care: Opportunities & Challenges

Burns ◽  
2022 ◽  
Author(s):  
Mohammadreza Mobayen ◽  
Mohammad Javad Ghazanfari ◽  
Alireza Feizkhah ◽  
Amir Emami Zeydi ◽  
Samad Karkhah
2020 ◽  
Vol 15 ◽  
Author(s):  
Thomas D Heseltine ◽  
Scott W Murray ◽  
Balazs Ruzsics ◽  
Michael Fisher

Recent rapid technological advancements in cardiac CT have improved image quality and reduced radiation exposure to patients. Furthermore, key insights from large cohort trials have helped delineate cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. The advent of CT-derived fractional flow reserve promises to establish an anatomical and functional test within one modality. Recent data examining the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes have been published. In addition, machine learning is a concept that is being increasingly applied to diagnostic medicine. Over the coming decade, machine learning will begin to be integrated into cardiac CT, and will potentially make a tangible difference to how this modality evolves. The authors have performed an extensive literature review and comprehensive analysis of the recent advances in cardiac CT. They review how recent advances currently impact on clinical care and potential future directions for this imaging modality.


2020 ◽  
Author(s):  
Sandeep Reddy ◽  
Sonia Allan ◽  
Simon Coghlan ◽  
Paul Cooper

The re-emergence of artificial intelligence (AI) in popular discourse and its application in medicine, especially via machine learning (ML) algorithms, has excited interest from policymakers and clinicians alike. The use of AI in clinical care in both developed and developing countries is no longer a question of ‘if?’ but ‘when?’. This creates a pressing need not only for sound ethical guidelines but also for robust governance frameworks to regulate AI in medicine around the world. In this article, we discuss what components need to be considered in developing these governance frameworks and who should lead this worldwide effort?


2021 ◽  
Author(s):  
Wael Abdelkader ◽  
Tamara Navarro ◽  
Rick Parrish ◽  
Chris Cotoi ◽  
Federico Germini ◽  
...  

BACKGROUND The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE To summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS Applying machine learning to distinguish studies with strong evidence for clinical care has the potential to decrease the workload of manually identifying these. The evidence base is active and evolving. Reported methods were variable across the studies but focused on supervised machine learning approaches. Performance may improve by applying more sophisticated approaches such as active learning, auto-machine learning, and unsupervised machine learning approaches.


2020 ◽  
Vol 29 (01) ◽  
pp. 235-242
Author(s):  
Ashley C. Griffin ◽  
Umit Topaloglu ◽  
Sean Davis ◽  
Arlene E. Chung

Objectives: Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We also highlight the ethical implications and future opportunities within each area. Methods: A search was conducted over a three-year period in two electronic databases (PubMed, Google Scholar) to identify peer-reviewed articles and conference proceedings. Search terms included variations of the following: neoplasms[MeSH], informatics[MeSH], cancer, oncology, clinical cancer informatics, medical cancer informatics. The search returned too many articles for practical review (23,994 from PubMed and 23,100 from Google Scholar). Thus, we conducted searches of key PubMed-indexed informatics journals and proceedings. We further limited our search to manuscripts that demonstrated a clear focus on clinical or translational cancer informatics. Manuscripts were then selected based on their methodological rigor, scientific impact, innovation, and contribution towards cancer informatics as a field or on their impact on cancer care and research. Results: Key developments and opportunities in cancer informatics research in the areas of digital health, machine learning, and precision oncology were summarized. Conclusion: While there are numerous innovations in the field of cancer informatics to advance prevention and clinical care, considerable challenges remain related to data sharing and privacy, digital accessibility, and algorithm biases and interpretation. The implementation and application of these findings in cancer care necessitates further consideration and research.


2020 ◽  
Author(s):  
Chethan Sarabu ◽  
Sandra Steyaert ◽  
Nirav Shah

Environmental allergies cause significant morbidity across a wide range of demographic groups. This morbidity could be mitigated through individualized predictive models capable of guiding personalized preventive measures. We developed a predictive model by integrating smartphone sensor data with symptom diaries maintained by patients. The machine learning model was found to be highly predictive, with an accuracy of 0.801. Such models based on real-world data can guide clinical care for patients and providers, reduce the economic burden of uncontrolled allergies, and set the stage for subsequent research pursuing allergy prediction and prevention. Moreover, this study offers proof-of-principle regarding the feasibility of building clinically useful predictive models from 'messy,' participant derived real-world data.


10.2196/15182 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e15182 ◽  
Author(s):  
Mark P Sendak ◽  
William Ratliff ◽  
Dina Sarro ◽  
Elizabeth Alderton ◽  
Joseph Futoma ◽  
...  

Background Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. Objective This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. Methods In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. Results Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. Conclusions Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.


2018 ◽  
Author(s):  
Samantha J Teague ◽  
Adrian BR Shatte

BACKGROUND Fathers’ experiences across the transition to parenthood are underreported in the literature. Social media offers the potential to capture fathers’ experiences in real time and at scale while also removing the barriers that fathers typically face in participating in research and clinical care. OBJECTIVE This study aimed to assess the feasibility of using social media data to map the discussion topics of fathers across the fatherhood transition. METHODS Discussion threads from two Web-based parenting communities, r/Daddit and r/PreDaddit from the social media platform Reddit, were collected over a 2-week period, resulting in 1980 discussion threads contributed to by 5853 unique users. An unsupervised machine learning algorithm was then implemented to group discussion threads into topics within each community and across a combined collection of all discussion threads. RESULTS Results demonstrated that men use Web-based communities to share the joys and challenges of the fatherhood experience. Minimal overlap in discussions was found between the 2 communities, indicating that distinct conversations are held on each forum. A range of social support techniques was demonstrated, with conversations characterized by encouragement, humor, and experience-based advice. CONCLUSIONS This study demonstrates that rich data on fathers’ experiences can be sourced from social media and analyzed rapidly using automated techniques, providing an additional tool for researchers exploring fatherhood.


2020 ◽  
Author(s):  
Alaa Abd-Alrazaq ◽  
Jens Schneider ◽  
Borbala Mifsud ◽  
Tanvir Alam ◽  
Mowafa Househ ◽  
...  

BACKGROUND Shortly after the emergence of the novel coronavirus disease (COVID-19), researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccination. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (e.g., scoping review and systematic reviews) for such a large domain area is challenging. OBJECTIVE We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. METHODS We used the COVID-19 Open Research Dataset (CORD-19) that consists of large number of articles related to all coronaviruses. We used machine learning method to analyze most relevant COVID-19 related articles and extracted most prominent topics. Specifically, we used clustering algorithm to group articles based on similarity of their abstracts to identify the research hotspots and current research directions. We have made our software accessible to the community via GitHub. RESULTS Of the 196,630 publications retrieved from the database, we included 28,904 in the analysis. The mean number of weekly publications was 990 (SD=789.3). The country that published the highest number of articles was China (n=2,950). The largest number of documents was published in BioRxiv. Lei Liu affiliated in the Southern University of Science and Technology in China published the highest number of documents (n=46). Based on titles and abstracts alone, we were able to identify 1,515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, 362 randomized control trials. We identified 19 different topics addressed by the included studies. The most dominant topic was public health response followed by clinical care practices during COVID-19, its clinical characteristics and risk factors, and epidemic models for its spread. CONCLUSIONS We provided an overview of the COVID-19 literature and identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs, and recognize leading COVID-19 researchers, institutes, countries, and publishers. This study showed that an AI-based bibliometric analysis has the potential to rapidly explore large corpora of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth related literature to help clinicians, administrators and policy makers to have a holistic view of the literature and be able to categorize the different topics of existing research for further analysis. It can be further scaled, for instance in time, to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.


2021 ◽  
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha

UNSTRUCTURED The rapid development of machine learning (ML) applications in healthcare promises to transform the landscape of healthcare. In order for ML advancements to be effectively utilized in clinical care, it is necessary for the medical workforce to be prepared to handle these changes. As physicians in training are exposed to a wide breadth of clinical tools during medical school, this offers an ideal opportunity to introduce ML concepts. A foundational understanding of ML will not only be practically useful for clinicians, but will also address ethical concerns for clinical decision making. While select medical schools have made effort to integrate ML didactics and practice into their curriculum, we argue that foundational ML principles should be taught to broadly to medical students across the country.


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