4 research outputs found
A Digital Phenotypic Assessment in Neuro-Oncology (DANO): A Pilot Study on Sociability Changes in Patients Undergoing Treatment for Brain Malignancies
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A Digital Phenotypic Assessment in Neuro-Oncology (DANO): A Pilot Study on Sociability Changes in Patients Undergoing Treatment for Brain Malignancies †
by Francesca Siddi 1,2,*,Patrick Emedom-Nnamdi 3,Michael P. Catalino 4,Aakanksha Rana 1,5ORCID,Alessandro Boaro 1,2ORCID,Hassan Y. Dawood 1ORCID,Francesco Sala 2,Jukka-Pekka Onnela 3,‡ andTimothy R. Smith 1,‡
1
Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA 02115, USA
2
Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37129 Verona, Italy
3
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
4
Department of Neurosurgery, University of Virginia, Charlottesville, VA 22908, USA
5
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
†
Previous Presentations: This work was virtually presented as an oral poster presentation at the 2021 Annual Meeting of the European Association of Neurosurgical Societies (eEANS), Virtual Congress, 1–7 October 2021; EP13028.
‡
These authors contributed equally to this work.
Cancers 2025, 17(1), 139; https://doi.org/10.3390/cancers17010139
Submission received: 15 October 2024 / Revised: 24 December 2024 / Accepted: 3 January 2025 / Published: 4 January 2025
(This article belongs to the Special Issue Novel Diagnostic and Therapeutic Approaches in Diffuse Gliomas)
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Simple Summary
Nowadays, smartphones are the principal tool for interactions between people. Mobile health applications might be used to study the cognitive functions in the neuro-oncological population. Many brain tumor patients have cognitive challenges that have an impact on sociability. Digital phenotyping is able to characterize social and spatial dimensions of human behavior from mobile phone call records. The aim of this study was to start to explore this technology in brain cancer patients, focusing on sociability data. The results of this pilot study indicate that a digital assessment in neuro-oncology can be used to characterize and follow the social activity of patients’ lives. Changes in the patient’s social network relate to disease progression, suggesting a new tool to improve the complex evaluation of underserved brain cancer patients.
Abstract
Background: The digital phenotyping tool has great potential for the deep characterization of neurological and quality-of-life assessments in brain tumor patients. Phone communication activities (details on call and text use) can provide insight into the patients’ sociability. Methods: We prospectively collected digital-phenotyping data from six brain tumor patients. The data were collected using the Beiwe application installed on their personal smartphones. We constructed several daily sociability features from phone communication logs, including the number of incoming and outgoing text messages and calls, the length of messages and duration of calls, message reciprocity, the number of communication partners, and number of missed calls. We compared variability in these sociability features against those obtained from a control group, matched for age and sex, selected among patients with a herniated disc. Results: In brain tumor patients, phone-based communication appears to deteriorate with time, as evident in the trend for total outgoing minutes, total outgoing calls, and call out-degree. Conclusions: These measures indicate a possible decrease in sociability over time in brain tumor patients that may correlate with survival. This exploratory analysis suggests that a quantifiable digital sociability phenotype exists and is comparable for patients with different survival outcomes. Overall, assessing neurocognitive function using digital phenotyping appears promising
A systematic review of federated learning applications for biomedical data
No full textObjectives Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. Methods We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. Results 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. Conclusion Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code. Author summary Interest in machine learning as applied to challenges in medicine has seen an exponential rise over the past decade. A key issue in developing machine learning models is the availability of sufficient high-quality data. Another related issue is a requirement to validate a locally trained model on data from external sources. However, sharing sensitive biomedical and clinical data across different hospitals and research teams can be challenging due to concerns with data privacy and data stewardship. These issues have led to innovative new approaches for collaboratively training machine learning models without sharing raw data. One such method, termed ‘federated learning,’ enables investigators from different institutions to combine efforts by training a model locally on their own data, and sharing the parameters of the model with others to generate a central model. Here, we systematically review reports of successful deployments of federated learning applied to research problems involving biomedical data. We found that federated learning links research teams around the world and has been applied to modelling in such as oncology and radiology. Based on the trends we observed in the studies reviewed in our paper, we observe there are opportunities to expand and improve this innovative approach so global teams can continue to produce and validate high quality machine learning models
A comprehensive review of the development of nano-bio adsorbents for the separation of heavy metals from wastewater
Get PDFThe need for water is growing, and this has made wastewater treatment necessary. Although it requires an expensive material, the commercial adsorbent is widely utilized in the industry for the treatment of wastewater. The demand for a method that could successfully and safely remove heavy metal ions from contamination. The amount of wastewater has grown recently. The formation of low-cost substitutes that are economically feasible for the treatment of various types of wastewater has been the major goal of this review paper. Therefore, it is imperative to assess all potential sources of natural adsorbents with high-capacity adsorption at a reasonable cost. Despite financial limitations, cost-effective and efficient methods must be provided to control the wastewater treatment process. Adsorption is regularly employed because of its adaptability in both design and operation, reversibility, affordability, and favorable outcomes. However, it appears that choosing the right and environmentally sound adsorbents for heavy metal removal is becoming ever more important. This review makes an effort to give a thorough overview of various modified adsorbents, their efficiency, and nanobio composites. The nanobiomaterial has demonstrated remarkable attention for the reduction of these heavy metals from wastewater
Efficacy and adverse events profile of videolaryngoscopy in critically ill patients: subanalysis of the INTUBE study
No full textBackground: Tracheal intubation is a high-risk procedure in the critically ill, with increased intubation failure rates and a high risk of other adverse events. Videolaryngoscopy might improve intubation outcomes in this population, but evidence remains conflicting, and its impact on adverse event rates is debated. Methods: This is a subanalysis of a large international prospective cohort of critically ill patients (INTUBE Study) performed from 1 October 2018 to 31 July 2019 and involving 197 sites from 29 countries across five continents. Our primary aim was to determine the first-pass intubation success rates of videolaryngoscopy. Secondary aims were characterising (a) videolaryngoscopy use in the critically ill patient population and (b) the incidence of severe adverse effects compared with direct laryngoscopy. Results: Of 2916 patients, videolaryngoscopy was used in 500 patients (17.2%) and direct laryngoscopy in 2416 (82.8%). First-pass intubation success was higher with videolaryngoscopy compared with direct laryngoscopy (84% vs 79%, P=0.02). Patients undergoing videolaryngoscopy had a higher frequency of difficult airway predictors (60% vs 40%, P<0.001). In adjusted analyses, videolaryngoscopy increased the probability of first-pass intubation success, with an OR of 1.40 (95% confidence interval [CI] 1.05–1.87). Videolaryngoscopy was not significantly associated with risk of major adverse events (odds ratio 1.24, 95% CI 0.95–1.62) or cardiovascular events (odds ratio 0.78, 95% CI 0.60–1.02). Conclusions: In critically ill patients, videolaryngoscopy was associated with higher first-pass intubation success rates, despite being used in a population at higher risk of difficult airway management. Videolaryngoscopy was not associated with overall risk of major adverse events. Clinical trial registration: NCT03616054
