The present study focused on identifying risk factors for early readmission of patients discharged from an urban community hospital. Retrospective chart reviews were conducted on 207 consecutive inpatient psychiatric admissions that included patients who were readmitted within 15 days, within 3 to 6 months, and not admitted for at least 12 months post-discharge. Findings indicated that a diagnosis of schizophrenia/schizoaffective disorder (OR = 18; 95% CI 2.70-117.7; p < 0.05), history of alcohol abuse (OR = 9; 95% CI 1.80-40.60; p < 0.05), number of previous psychiatric hospitalizations (OR = 2; 95% CI 1.28-3.73; p < 0.05), and type of residence at initial admission (e.g., homeless, OR = 29; 95% CI 3.99-217; p < 0.05) were significant risk factors for early readmission, where OR compares readmission group 1 versus group 3 in the multinomial logistic regression. Initial positive urine drug screen, history of drug abuse or incarceration, and legal status at initial admission did not predict early readmission. Reducing the risk factors associated with psychiatric readmissions has the potential to lead to the identification and development of preventative intervention strategies that can significantly improve patient safety, quality of care, well-being, and contain health care expenditures.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

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The braided multielectrode probe (BMEP) is an ultrafine microwire bundle interwoven into a precise tubular braided structure, which is designed to be used as an invasive neural probe consisting of multiple microelectrodes for electrophysiological neural recording and stimulation. Significant advantages of BMEPs include highly flexible mechanical properties leading to decreased immune responses after chronic implantation in neural tissue and dense recording/stimulation sites (24 channels) within the 100–200 μm diameter. In addition, because BMEPs can be manufactured using various materials in any size and shape without length limitations, they could be expanded to applications in deep central nervous system (CNS) regions as well as peripheral nervous system (PNS) in larger animals and humans. Finally, the 3D topology of wires supports combinatoric rearrangements of wires within braids, and potential neural yield increases. With the newly developed next generation micro braiding machine, we can manufacture more precise and complex microbraid structures. In this article, we describe the new machine and methods, and tests of simulated combinatoric separation methods. We propose various promising BMEP designs and the potential modifications to these designs to create probes suitable for various applications for future neuroprostheses.