Exploring the relationship between the Engineering and Physical Sciences and the Health and Life Sciences by advanced bibliometric methods

We investigate the extent to which advances in the health and life sciences (HLS) are dependent on research in the engineering and physical sciences (EPS), particularly physics, chemistry, mathematics, and engineering. The analysis combines two different bibliometric approaches. The first approach to analyze the 'EPS-HLS interface' is based on term map visualizations of HLS research fields. We consider 16 clinical fields and five life science fields. On the basis of expert judgment, EPS research in these fields is studied by identifying EPS-related terms in the term maps. In the second approach, a large-scale citation-based network analysis is applied to publications from all fields of science. We work with about 22,000 clusters of publications, each representing a topic in the scientific literature. Citation relations are used to identify topics at the EPS-HLS interface. The two approaches complement each other. The advantages of working with textual data compensate for the limitations of working with citation relations and the other way around. An important advantage of working with textual data is in the in-depth qualitative insights it provides. Working with citation relations, on the other hand, yields many relevant quantitative statistics. We find that EPS research contributes to HLS developments mainly in the following five ways: new materials and their properties; chemical methods for analysis and molecular synthesis; imaging of parts of the body as well as of biomaterial surfaces; medical engineering mainly related to imaging, radiation therapy, signal processing technology, and other medical instrumentation; mathematical and statistical methods for data analysis. In our analysis, about 10% of all EPS and HLS publications are classified as being at the EPS-HLS interface. This percentage has remained more or less constant during the past decade

Patient Decision Making in Vestibular Schwannoma: A Survey of the Acoustic Neuroma Association.

Objective To assess the decision-making process of patients with vestibular schwannoma (VS). Study Design Patients with VS completed a voluntary survey over a 3-month period. Setting Surveys were distributed online through email, Facebook, and member website. Subjects and Methods All patients had a diagnosis of VS and were members of the Acoustic Neuroma Association (ANA). A total of 789 patients completed the online survey. Results Of the 789 participants, 474 (60%) cited physician recommendation as a significant influential factor in deciding treatment. In our sample, 629 (80%) saw multiple VS specialists and 410 (52%) sought second opinions within the same specialty. Of those who received multiple consults, 242 (59%) of patients reported receiving different opinions regarding treatment. Those undergoing observation spent significantly less time with the physician (41 minutes) compared to surgery (68 minutes) and radiation (60 minutes) patients ( P < .001). A total of 32 (4%) patients stated the physician alone made the decision for treatment, and 29 (4%) felt they did not understand all possible treatment options before final decision was made. Of the 414 patients who underwent surgery, 66 (16%) felt they were pressured by the surgeon to choose surgical treatment. Conclusion Deciding on a proper VS treatment for patients can be complicated and dependent on numerous clinical and individual factors. It is clear that many patients find it important to seek second opinions from other specialties. Moreover, second opinions within the same specialty are common, and the number of neurotologists consulted correlated with higher decision satisfaction

Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM) 201

Investigating risk factors and predicting complications in deep brain stimulation surgery with machine learning algorithms

Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to (1) investigate preoperative clinical risk factors, and (2) build machine learning models to predict adverse outcomes. Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n=501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity and accuracy. Results: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (OR=0.44, confidence interval [CI]=0.25-0.78), BMI (OR=0.94,CI=0.89-0.99) and diabetes (OR=2.33,CI=1.18-4.60). Patients with diabetes were almost three times more likely to return to the operating room (OR=2.78,CI=1.31-5.88). Patients with a history of smoking were four times more likely to experience postoperative infection (OR=4.20,CI=1.21-14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC=0.86), a complication within 12 months (AUC=0.91), return to the operating room (AUC=0.88) and infection (AUC=0.97). Age, BMI, procedure side, gender and a diagnosis of Parkinson’s disease were influential features. Conclusions: Multiple significant complication risk factors were identified and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms

© 2019 Elsevier Inc. Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes. Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n = 501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. Results: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (odds ratio [OR] = 0.44, confidence interval [CI] = 0.25–0.78), body mass index (OR = 0.94, CI = 0.89–0.99), and diabetes (OR = 2.33, CI = 1.18–4.60). Patients with diabetes were almost 3× more likely to return to the operating room (OR = 2.78, CI = 1.31–5.88). Patients with a history of smoking were 4× more likely to experience postoperative infection (OR = 4.20, CI = 1.21–14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC = 0.86), a complication within 12 months (AUC = 0.91), return to the operating room (AUC = 0.88), and infection (AUC = 0.97). Age, body mass index, procedure side, gender, and a diagnosis of Parkinson disease were influential features. Conclusions: Multiple significant complication risk factors were identified, and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery

Determinants of Theory of Mind performance in Alzheimer’s disease: A data-mining study

Whether theory of mind (ToM) is preserved in Alzheimer’s disease (AD) remains a controversial subject. Recent studies have showed that performance on some ToM tests might be altered in AD, though to a lesser extent than in behavioural-variant Frontotemporal Dementia (bvFTD). It is however, unclear if this reflects a genuine impairment of ToM or a deficit secondary to the general cognitive decline observed in AD. Aiming to investigate the cognitive determinants of ToM performance in AD, a data-mining study was conducted in 29 AD patients then replicated in an independent age-matched group of 19 AD patients to perform an independent replication of the results. 44 bvFTD patients were included as a comparison group. All patients had an extensive neuropsychological examination. Hierarchical clustering analyses showed that ToM performance clustered with measures of executive functioning in AD. ToM performance was also specifically correlated with the executive component extracted from a principal component analysis. In a final step, automated linear modelling conducted to determine the predictors of ToM performance showed that 48.8% of ToM performance was significantly predicted by executive measures. Similar findings across analyses were observed in the independent group of AD patients, thereby replicating our results. Conversely, ToM impairments in bvFTD appeared independent of other cognitive impairments. These results suggest that difficulties of AD patients on ToM tests do not reflect a genuine ToM deficit, rather mediated by general (and particularly executive) cognitive decline. They also suggest that executive functioning has a key role in mental state attribution, which support interacting models of ToM functioning. Finally, our study highlights the relevancy of data-mining statistical approaches in clinical and cognitive neurosciences