scholarly journals Machine learning in neurosurgery: a global survey

2020 ◽  
Vol 162 (12) ◽  
pp. 3081-3091 ◽  
Author(s):  
Victor E. Staartjes ◽  
Vittorio Stumpo ◽  
Julius M. Kernbach ◽  
Anita M. Klukowska ◽  
Pravesh S. Gadjradj ◽  
...  
Keyword(s):  
Machine Learning ◽  
North America ◽  
Online Survey ◽  
Response Rate ◽  
Future Studies ◽  
Factors Associated ◽  
Technological Advances ◽  
Academic Settings ◽  
Comprehensive Survey ◽  
Potential Benefits

Abstract Background Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use. Methods The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS). Results Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging. Conclusions This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations.

scholarly journals Global adoption of robotic technology into neurosurgical practice and research

2020 ◽  
Author(s):  
Vittorio Stumpo ◽  
Victor E. Staartjes ◽  
Anita M. Klukowska ◽  
Aida Kafai Golahmadi ◽  
Pravesh S. Gadjradj ◽  
...  
Keyword(s):  
North America ◽  
European Association ◽  
Online Survey ◽  
Response Rate ◽  
Female Gender ◽  
Clinical Use ◽  
Robotic Technology ◽  
Factors Associated ◽  
Potential Benefits ◽  
Robotic Applications

AbstractRecent technological advancements have led to the development and implementation of robotic surgery in several specialties, including neurosurgery. Our aim was to carry out a worldwide survey among neurosurgeons to assess the adoption of and attitude toward robotic technology in the neurosurgical operating room and to identify factors associated with use of robotic technology. The online survey was made up of nine or ten compulsory questions and was distributed via the European Association of the Neurosurgical Societies (EANS) and the Congress of Neurological Surgeons (CNS) in February and March 2018. From a total of 7280 neurosurgeons who were sent the survey, we received 406 answers, corresponding to a response rate of 5.6%, mostly from Europe and North America. Overall, 197 neurosurgeons (48.5%) reported having used robotic technology in clinical practice. The highest rates of adoption of robotics were observed for Europe (54%) and North America (51%). Apart from geographical region, only age under 30, female gender, and absence of a non-academic setting were significantly associated with clinical use of robotics. The Mazor family (32%) and ROSA (26%) robots were most commonly reported among robot users. Our study provides a worldwide overview of neurosurgical adoption of robotic technology. Almost half of the surveyed neurosurgeons reported having clinical experience with at least one robotic system. Ongoing and future trials should aim to clarify superiority or non-inferiority of neurosurgical robotic applications and balance these potential benefits with considerations on acquisition and maintenance costs.

scholarly journals Factors Influencing The Readiness Of Government Financial Personnel In Migrating Towards Accrual Accounting

2019 ◽  
Vol 8 (5C) ◽  
pp. 60-66
Keyword(s):  
Job Satisfaction ◽  
Social Relationships ◽  
Response Rate ◽  
Migration Process ◽  
Accrual Accounting ◽  
Future Studies ◽  
Factors Associated ◽  
Factors Influencing ◽  
Ministry Of Finance ◽  
The Government

Jordan’s migration towards accrual accounting is aimed at to be completed by year 2021, being as one of its government’s fiscal reform steps. However, this process is not progressing well within the government of Jordan due to the lack of improvement in performance. It is important to prepare the government’s financial personnel for the migration in order to ensure a successful migration process. Moreover, individual readiness factors associated with the acceptance or rejection of migration towards accrual accounting should be considered before the actual migration process takes place. Thus, this research aims to address the influence of social relationships at the workplace and job satisfaction on the readiness of Jordanian government financial personnel to determine their reaction regarding the migration towards accrual accounting. Following the quantitative method, the researcher distributed 375 questionnaires to the government financial personnel working in the Jordanian Ministry of Finance (JMOF), and a total of 331 questionnaires were returned, hence achieving a 88% response rate. The results of the research indicate a positive significant association between social relationships at the workplace/ job satisfaction and the readiness of Jordanian government financial personnel to migrate towards accrual accounting. Several limitations and potential areas for future studies are discussed in the last section of the paper

scholarly journals Demand in Pediatric Dentistry for Sedation and General Anesthesia by Dentist Anesthesiologists: A Survey of Directors of Dentist Anesthesiologist and Pediatric Dentistry Residencies

2012 ◽  
Vol 59 (1) ◽  
pp. 3-11 ◽  
Author(s):  
C. Gray Hicks ◽  
James E. Jones ◽  
Mark A. Saxen ◽  
Gerardo Maupome ◽  
Brian J. Sanders ◽  
...  
Keyword(s):  
North America ◽  
General Anesthesia ◽  
Training Program ◽  
Online Survey ◽  
Response Rate ◽  
Training Programs ◽  
Deep Sedation ◽  
Program Directors ◽  
Pediatric Dentistry ◽  
Pediatric Dentists

This study describes what training programs in pediatric dentistry and dental anesthesiology are doing to meet future needs for deep sedation/general anesthesia services required for pediatric dentistry. Residency directors from 10 dental anesthesiology training programs in North America and 79 directors from pediatric dentistry training programs in North America were asked to answer an 18-item and 22-item online survey, respectively, through an online survey tool. The response rate for the 10 anesthesiology training program directors was 9 of 10 or 90%. The response rate for the 79 pediatric dentistry training program directors was 46 of 79 or 58%. Thirty-seven percent of pediatric dentistry programs use clinic-based deep sedation/general anesthesia for dental treatment in addition to hospital-based deep sedation/general anesthesia. Eighty-eight percent of those programs use dentist anesthesiologists for administration of deep sedation/general anesthesia in a clinic-based setting. Pediatric dentistry residency directors perceive a future change in the need for deep sedation/general anesthesia services provided by dentist anesthesiologists to pediatric dentists: 64% anticipate an increase in need for dentist anesthesiologist services, while 36% anticipate no change. Dental anesthesiology directors compared to 2, 5, and 10 years ago have seen an increase in the requests for dentist anesthesiologist services by pediatric dentists reported by 56% of respondents (past 2 years), 63% of respondents (past 5 years), and 88% of respondents (past 10 years), respectively. Predicting the future need of dentist anesthesiologists is an uncertain task, but these results show pediatric dentistry directors and dental anesthesiology directors are considering the need, and they recognize a trend of increased need for dentist anesthesiologist services over the past decade.

scholarly journals Patients’, pharmacists’, and prescribers’ attitude toward using blockchain and machine learning in a proposed ePrescription system: online survey

JAMIA Open ◽  
2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Bader Aldughayfiq ◽  
Srinivas Sampalli
Keyword(s):  
Machine Learning ◽  
Patient Group ◽  
Positive Attitude ◽  
Online Survey ◽  
User Study ◽  
Security And Privacy ◽  
Blockchain Technology ◽  
Positive Attitudes ◽  
Potential Benefits ◽  
Study Population

Abstract Objective To evaluate the attitudes of the parties involved in the system toward the new features and measure the potential benefits of introducing the use of blockchain and machine learning (ML) to strengthen the in-place methods for safely prescribing medication. The proposed blockchain will strengthen the security and privacy of the patient’s prescription information shared in the network. Once the ePrescription is submitted, it is only available in read-only mode. This will ensure there is no alteration to the ePrescription information after submission. In addition, the blockchain will provide an improved tracking mechanism to ensure the originality of the ePrescription and that a prescriber can only submit an ePrescription with the patient’s authorization. Lastly, before submitting an ePrescription, an ML algorithm will be used to detect any anomalies (eg, missing fields, misplaced information, or wrong dosage) in the ePrescription to ensure the safety of the prescribed medication for the patient. Methods The survey contains questions about the features introduced in the proposed ePrescription system to evaluate the security, privacy, reliability, and availability of the ePrescription information in the system. The study population is comprised of 284 respondents in the patient group, 39 respondents in the pharmacist group, and 27 respondents in the prescriber group, all of whom met the inclusion criteria. The response rate was 80% (226/284) in the patient group, 87% (34/39) in the pharmacist group, and 96% (26/27) in the prescriber group. Key Findings The vast majority of the respondents in all groups had a positive attitude toward the proposed ePrescription system’s security and privacy using blockchain technology, with 72% (163/226) in the patient group, 70.5% (24/34) in the pharmacist group, and 73% (19/26) in the prescriber group. Moreover, the majority of the respondents in the pharmacist (70%, 24/34) and prescriber (85%, 22/26) groups had a positive attitude toward using ML algorithms to generate alerts regarding prescribed medication to enhance the safety of medication prescribing and prevent medication errors. Conclusion Our survey showed that a vast majority of respondents in all groups had positive attitudes toward using blockchain and ML algorithms to safely prescribe medications. However, a need for minor improvements regarding the proposed features was identified, and a post-implementation user study is needed to evaluate the proposed ePrescription system in depth.

scholarly journals TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning

2019 ◽  
Author(s):  
Fred Hohman ◽  
Arjun Srinivasan ◽  
Steven M. Drucker
Keyword(s):  
Machine Learning ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Prototype System ◽  
Future Studies ◽  
Interpretable Machine Learning ◽  
User Expectations ◽  
Potential Benefits ◽  
Hard Problems ◽  
Key Aspects

While machine learning (ML) continues to find success in solving previously-thought hard problems, interpreting and exploring ML models remains challenging. Recent work has shown that visualizations are a powerful tool to aid debugging, analyzing, and interpreting ML models. However, depending on the complexity of the model (e.g., number of features), interpreting these visualizations can be difficult and may require additional expertise. Alternatively, textual descriptions, or verbalizations, can be a simple, yet effective way to communicate or summarize key aspects about a model, such as the overall trend in a model’s predictions or comparisons between pairs of data instances. With the potential benefits of visualizations and verbalizations in mind, we explore how the two can be combined to aid ML interpretability. Specifically, we present a prototype system, TeleGam, that demonstrates how visualizations and verbalizations can collectively support interactive exploration of ML models, for example, generalized additive models (GAMs). We describe TeleGam’s interface and underlying heuristics to generate the verbalizations. We conclude by discussing how TeleGam can serve as a platform to conduct future studies for understanding user expectations and designing novel interfaces for interpretable ML.

Survey of Spanish Parents of Children Who Are Deaf or Hard of Hearing: Decision-Making Factors Associated With Communication Modality and Bilingualism

2013 ◽  
Vol 22 (1) ◽  
pp. 105-119 ◽  
Author(s):  
Mark Guiberson
Keyword(s):  
Hard Of Hearing ◽  
Online Survey ◽  
The United States ◽  
Spoken Language ◽  
Communication Modality ◽  
Future Studies ◽  
Raising Children ◽  
Factors Associated ◽  
Decision Making Factors ◽  
Do So

Purpose The purpose of the present study was (a) to describe factors and trends associated with Spanish parents' choice of communication modality and spoken-language bilingualism for children who are deaf or hard of hearing (DHH) and (b) to identify if bilingual variables predict children's bilingual status in a country where bilingualism is common. Method Seventy-one Spanish parents of children who are DHH completed an online survey that included questions about demographics, family and professional involvement and support, accessibility to information and services, and bilingual background and beliefs. Analyses were completed to describe groups and to examine how variables were associated with parents' decisions. Results Thirty-eight percent of parents chose to raise their children to be spoken-language bilingual. Most parents indicated that they believed being bilingual was beneficial for their children and that children who are DHH are capable of becoming bilingual in spoken languages. Parent's bilingual score, beliefs about raising children who are DHH bilingually, and encouragement to do so, were significantly associated with children's bilingual status. Conclusion In communities where bilingualism is common, bilingual parents will often choose to raise children who are DHH bilingual in spoken languages. Implications for practice and future studies in the United States are provided.

Knowledge, Perceptions and Practices on Modified Release Tablets among Prescribers: A Preliminary Study

2020 ◽  
Vol 01 (01) ◽  
pp. 05-14
Author(s):  
M.G.K.M. Fernando ◽  
K.I.J. Priyadarshi ◽  
L.G.T. Shanika ◽  
N.R. Samaranayake
Keyword(s):  
Online Survey ◽  
Product Information ◽  
Response Rate ◽  
Immediate Release ◽  
State Universities ◽  
Modified Release ◽  
Validated Questionnaire ◽  
Therapeutic Outcomes ◽  
Medical Faculties ◽  
Enteric Coated

Introduction: Modified release tablets (MRTs) are developed to achieve different therapeutic outcomes and are frequently prescribed. This study aims to evaluate the knowledge, perceptions and practices on using MRTs among a selected cohort of prescribers. Methods: A self administered online survey was conducted using a pre-validated questionnaire, prepared in-house to assess knowledge, perceptions and practices on using MRTs, among academics with an MBBS degree in medical faculties of State universities in Sri Lanka. Results: The response rate was 15.5% among 375 prescribers. Most were females (53.4%) and were 46-55 years (29.3%). Over 50% correctly expanded abbreviations related to MRTs. Most defined enteric coated (87.9%) and targeted release (77.6%) forms accurately. However, 87.0% mixed-up definitions of sustained release with controlled release. Most believed that inability to split tablets (70.7%) and high cost (70.7%), as disadvantages of MRTs. Nearly half did not identify the risk of dose dumping (53.5%) and inflexible dosing schedule (44.8%) as disadvantages. For frequency of administering MRTs, 86.2% referred the product information leaflet (PIL) while 29.0% depended on the frequency of the corresponding immediate release tablet. Most (79.3%) prescribed MRTs to increase patient compliance while 12.1% prescribed them to reduce cost. When problems regarding MRTs were encountered, most referred PILs (81.0%) and clarified with experts (75.9%). Conclusions: Although the response rate was low, a clear gap in knowledge, perceptions and practices on using MRTs were identified among prescribers who responded. Interventions are needed to improve the knowledge, perceptions, and practices on using MRTs among prescribers.

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

scholarly journals Assessing the utility and attitudes toward molecular testing in neuro-oncology: a survey of the Society for Neuro-Oncology members

2021 ◽  
Author(s):  
Shannon Fortin Ensign ◽  
Maya Hrachova ◽  
Susan Chang ◽  
Maciej M Mrugala
Keyword(s):  
Online Survey ◽  
Clinical Decision Making ◽  
Unmet Needs ◽  
Practice Patterns ◽  
Response Rate ◽  
Clinical Decision ◽  
Molecular Testing ◽  
Newly Diagnosed ◽  
Tumor Boards ◽  
Oncology Practice

Abstract Background Molecular testing (MT) is utilized in neuro-oncology with increasing frequency. The aim of this study was to determine clinical practice patterns to acquire this information, interpret and utilize MT for patient care, and identify unmet needs in the practical clinical application of MT. Methods We conducted a voluntary online survey of providers within the Society for Neuro-Oncology (SNO) membership database between March and April 2019. Results We received 152 responses out of 2022 SNO members (7.5% of membership). 88.8% of respondents routinely order MT for newly diagnosed gliomas. Of those who do not, testing is preferentially performed in younger patients or those with midline tumors. 82.8% use MT in recurrent gliomas. Other common indications included: metastatic tumors, meningioma, and medulloblastoma. Many providers utilize more than one resource (36.0%), most frequently using in-house (41.8%) over commercially available panels. 78.1% used the results for clinical decision-making, with BRAF, EGFR, ALK, and H3K27 mutations most commonly directing treatment decisions. Approximately, half (48.5%) of respondents have molecular tumor boards at their institutions. Respondents would like to see SNO-endorsed guidelines on MT, organized lists of targeted agents available for specific mutations, a database of targetable mutations and clinical trials, and more educational programs on MT. Conclusion This survey was marked by several limitations including response rate and interpretation of MT. Among respondents, there is routine use of MT in Neuro-Oncology, however, there remains a need for increased guidance for providers to effectively incorporate the expanding genomic data resulting from MT into daily Neuro-Oncology practice.

Non-Pediatric Nurses’ Willingness to Provide Care to Pediatric Patients during a Disaster: An Assessment of Pediatric Surge Capacity in Four Midwestern Hospitals

2021 ◽  
pp. 1-6
Author(s):  
Terri Rebmann ◽  
Rachel L. Charney ◽  
Rebecca L. Eschmann ◽  
M. Colleen Fitzpatrick
Keyword(s):  
Pediatric Patients ◽  
Response Rate ◽  
Mass Casualty ◽  
Pediatric Care ◽  
Pediatric Nurses ◽  
Surge Capacity ◽  
Factors Associated ◽  
Pediatric Hospitals ◽  
Survey Response Rate ◽  
Mass Casualty Event

Abstract Objective: To assess non-pediatric nurses’ willingness to provide care to pediatric patients during a mass casualty event (MCE). Methods: Nurses from 4 non-pediatric hospitals in a major metropolitan Midwestern region were surveyed in the fall of 2018. Participants were asked about their willingness to provide MCE pediatric care. Hierarchical logistical regression was used to describe factors associated with nurses’ willingness to provide MCE pediatric care. Results: In total, 313 nurses were approached and 289 completed a survey (response rate = 92%). A quarter (25.3%, n = 73) would be willing to provide MCE care to a child of any age; 12% (n = 35) would provide care only to newborns in the labor and delivery area, and 16.6% (n = 48) would only provide care to adults. Predictors of willingness to provide care to a patient of any age during an MCE included providing care to the youngest-age children during routine duties, reporting confidence in calculating doses and administering pediatric medications, working in the emergency department, being currently or previously certified in PALS, and having access to pediatric-sized equipment in the unit or hospital. Conclusion: Pediatric surge capacity is lacking among nurses. Increasing nurses’ pediatric care self-efficacy could improve pediatric surge capacity and minimize morbidity and mortality during MCEs.

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