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1. “Going Rogue”: Re-coding Resistance with Type 1 Diabetes

   https://doi.org/10.14361/dcs-2018-0208  

   Gottlieb, Samantha D.; Cluck, Jonathan (December 2018, Digital Culture & Society)

   null (Ed.)

   Abstract This paper explores our collaborative STS and anthropological project with type 1 diabetes (T1D) hardware “hacking” communities, whose work focuses on reverse-engineering and extracting data from medical devices such as insulin pumps and continuous glucose monitoring systems (CGMS) to create do-it-yourself artificial pancreas systems (APS). Rather than using these devices within their prescriptive and prescribed purposes (surveillance and treatment monitoring), these “hackers” repurpose, reinterpret, and redirect of the possibilities of medical surveillance data in order to reshape their own treatment. Through “deliberate non-compliance” (Scibilia 2017) with cliniciandeveloped treatment guidelines, T1D device hackers deliberatively engage with clinicians’ conceptions and formulations of what constitutes “good treatment” and empower themselves in discussions about the effectiveness of treatment guidelines. Their non-compliance is, however, neither negligence, as implied by the medical category of patients who fail to comply with clinical orders, nor ignorance, but a productive and creative response to their embodied expertise, living with a chronic and potentially deadly condition. Our interlocutors’ explicit connections with the free and open source software principles suggests the formation of a “recursive public” (Kelty 2008) in diabetes research and care practices, from a patient-centered “medical model” to a diverse and divergent patient-led model. The philosophical and ethical underpinnings of the open source and collaborative strategies these patients draw upon radically reshape the principles that drive the commercial health industry and government regulatory structures. 

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2. 307 Machine learning identification of diabetic foot ulcer severity to reduce amputation risk

   https://doi.org/10.1017/cts.2023.360  

   Flores, Mario; Paniagua, Karla; Jin, Rivera Yufang (April 2023, Journal of Clinical and Translational Science)

   OBJECTIVES/GOALS: Target: Computationally identify the markers of ulcer severity and risk of amputation from datasets that include demographics data, clinical, laboratory data, and medical history over 6000 patients. METHODS/STUDY POPULATION: In this study we will use tables of demographics such as age, gender, and ethnicity/race. Inspired by previous research we’ll include wound age (duration in days), wound size, number of concurrent wounds of any etiology, evidence of bioburden/infection, Wagner grade, being non ambulatory, renal dialysis, renal transplant, peripheral vascular disease, and patient hospitalization. Another table will include laboratory vital signs to include physiological variables such as height, weight, body mass index, pulse rate, blood pressure, respiratory rate, and temperature. We’ll include also social data like smoking status, socio-economic status, housing condition. RESULTS/ANTICIPATED RESULTS: Our project aligns with previous efforts to identify high risk Diabetic Foot Ulcer individuals but also takes a different perspective by collecting and marking clinical data from a subset of patients (e.g., severity, Hispanic versus non-Hispanic) and computationally process these data to provide a tool that can identify DFU severity and high-risk patients. We will obtain samples from Hispanics and non-Hispanics because these two groups are likely to have significant differences in the progression of ulcer severity. The rationale is that by comparing these two groups, we will assess and study the factors that are differentially present. It is our expectation that the proposed project will provide an easy-to-use tool for DFU progression and risk of amputation and contribute to identify high-risk individuals. DISCUSSION/SIGNIFICANCE: Diabetes prevalence estimates in Bexar County, TX exceeds national estimates (15.5% vs. 11.3%) and diagnosed cases are higher among Hispanic adults (13.4%) compared to their non-Hispanic white counterparts (9.5%). Late identification of severe foot ulcers minimizes the likelihood of reducing amputation risk. 

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3. Deep neural networks for wearable sensor-based activity recognition in Parkinson’s disease: investigating generalizability and model complexity

   https://doi.org/10.1186/s12938-024-01214-2  

   Davidashvilly, Shelly; Cardei, Maria; Hssayeni, Murtadha; Chi, Christopher; Ghoraani, Behnaz (February 2024, BioMedical Engineering OnLine)

   Abstract BackgroundThe research gap addressed in this study is the applicability of deep neural network (NN) models on wearable sensor data to recognize different activities performed by patients with Parkinson’s Disease (PwPD) and the generalizability of these models to PwPD using labeled healthy data. MethodsThe experiments were carried out utilizing three datasets containing wearable motion sensor readings on common activities of daily living. The collected readings were from two accelerometer sensors. PAMAP2 and MHEALTH are publicly available datasets collected from 10 and 9 healthy, young subjects, respectively. A private dataset of a similar nature collected from 14 PwPD patients was utilized as well. Deep NN models were implemented with varying levels of complexity to investigate the impact of data augmentation, manual axis reorientation, model complexity, and domain adaptation on activity recognition performance. ResultsA moderately complex model trained on the augmented PAMAP2 dataset and adapted to the Parkinson domain using domain adaptation achieved the best activity recognition performance with an accuracy of 73.02%, which was significantly higher than the accuracy of 63% reported in previous studies. The model’s F1 score of 49.79% significantly improved compared to the best cross-testing of 33.66% F1 score with only data augmentation and 2.88% F1 score without data augmentation or domain adaptation. ConclusionThese findings suggest that deep NN models originating on healthy data have the potential to recognize activities performed by PwPD accurately and that data augmentation and domain adaptation can improve the generalizability of models in the healthy-to-PwPD transfer scenario. The simple/moderately complex architectures tested in this study could generalize better to the PwPD domain when trained on a healthy dataset compared to the most complex architectures used. The findings of this study could contribute to the development of accurate wearable-based activity monitoring solutions for PwPD, improving clinical decision-making and patient outcomes based on patient activity levels. 

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4. Concerns in the Blurred Divisions Between Medical and Consumer Neurotechnology

   https://doi.org/10.1109/JSYST.2020.3032609  

   Paek, Andrew Y.; Brantley, Justin A.; Evans, Barbara J.; Contreras-Vidal, Jose L. (December 2020, IEEE Systems Journal)

   null (Ed.)

   Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical uses. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement. 

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5. Sharing personal ECG time-series data privately

   https://doi.org/10.1093/jamia/ocac047  

   Bonomi, Luca; Wu, Zeyun; Fan, Liyue (April 2022, Journal of the American Medical Informatics Association)

   Abstract ObjectiveEmerging technologies (eg, wearable devices) have made it possible to collect data directly from individuals (eg, time-series), providing new insights on the health and well-being of individual patients. Broadening the access to these data would facilitate the integration with existing data sources (eg, clinical and genomic data) and advance medical research. Compared to traditional health data, these data are collected directly from individuals, are highly unique and provide fine-grained information, posing new privacy challenges. In this work, we study the applicability of a novel privacy model to enable individual-level time-series data sharing while maintaining the usability for data analytics. Methods and materialsWe propose a privacy-protecting method for sharing individual-level electrocardiography (ECG) time-series data, which leverages dimensional reduction technique and random sampling to achieve provable privacy protection. We show that our solution provides strong privacy protection against an informed adversarial model while enabling useful aggregate-level analysis. ResultsWe conduct our evaluations on 2 real-world ECG datasets. Our empirical results show that the privacy risk is significantly reduced after sanitization while the data usability is retained for a variety of clinical tasks (eg, predictive modeling and clustering). DiscussionOur study investigates the privacy risk in sharing individual-level ECG time-series data. We demonstrate that individual-level data can be highly unique, requiring new privacy solutions to protect data contributors. ConclusionThe results suggest our proposed privacy-protection method provides strong privacy protections while preserving the usefulness of the data. 

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