Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people ...choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.
Characterizing the prevalence and persistence of symptoms associated with COVID-19 infection following hospitalization and their impact is essential to planning post-acute community-based clinical ...services. This study seeks to identify persistent COVID-19 symptoms in patients 35 days post-hospitalization and their impact on quality of life, health, physical, mental, and psychosocial function.
This prospective cohort study used the PROMIS® Instruments to identify symptoms and quality of life parameters in consecutively enrolled patients between March 22 and April 16, 2020, in New Jersey. The 183 patients (median age 57 years; 61.5% male, 54.1% white) reported persistent symptoms at 35 days, including fatigue (55.0%), dyspnea (45.3%), muscular pain (51%), associated with a lower odds rating general health (41.5%, OR 0.093 95% CI: 0.026, 0.329, p = 0.0002), quality of life (39.8%; OR 0.116 95% CI: 0.038, 0.364, p = 0.0002), physical health (38.7%, OR 0.055 95% CI: 0.016, 0.193, p <0.0001), mental health (43.7%, OR 0.093 95% CI: 0.021, 0.418, p = 0.0019) and social active role (38.7%, OR 0.095 95% CI: 0.031, 0.291, p<0.0001), as very good/excellent, particularly adults aged 65 to 75 years (OR 8·666 95% CI: 2·216, 33·884, p = 0·0019).
COVID-19 symptoms commonly persist to 35 days, impacting quality of life, health, physical and mental function. Early post-acute evaluation of symptoms and their impact on function is necessary to plan community-based services.
Objective: To examine the relationship between hospital and emergency department (ED) occupancy, as indicators of hospital overcrowding, and mortality after emergency admission.
Design: Retrospective ...analysis of 62 495 probabilistically linked emergency hospital admissions and death records.
Setting: Three tertiary metropolitan hospitals between July 2000 and June 2003.
Participants: All patients 18 years or older whose first ED attendance resulted in hospital admission during the study period.
Main outcome measures: Deaths on days 2, 7 and 30 were evaluated against an Overcrowding Hazard Scale based on hospital and ED occupancy, after adjusting for age, diagnosis, referral source, urgency and mode of transport to hospital.
Results: There was a linear relationship between the Overcrowding Hazard Scale and deaths on Day 7 (r = 0.98; 95% CI, 0.79–1.00). An Overcrowding Hazard Scale > 2 was associated with an increased Day 2, Day 7 and Day 30 hazard ratio for death of 1.3 (95% CI, 1.1–1.6), 1.3 (95% CI, 1.2–1.5) and 1.2 (95% CI, 1.1–1.3), respectively. Deaths at 30 days associated with an Overcrowding Hazard Scale > 2 compared with one of < 3 were undifferentiated with respect to age, diagnosis, urgency, transport mode, referral source or hospital length of stay, but had longer ED durations of stay (risk ratio per hour of ED stay, 1.1; 95% CI, 1.1–1.1; P < 0.001) and longer physician waiting times (risk ratio per hour of ED wait, 1.2; 95% CI, 1.1–1.3; P = 0.01).
Conclusions: Hospital and ED overcrowding is associated with increased mortality. The Overcrowding Hazard Scale may be used to assess the hazard associated with hospital and ED overcrowding. Reducing overcrowding may improve outcomes for patients requiring emergency hospital admission.
We introduce two validated single (SH) and dual hormone (DH) mathematical models that represent an in-silico virtual patient population (VPP) for type 1 diabetes (T1D). The VPP can be used to ...evaluate automated insulin and glucagon delivery algorithms, so-called artificial pancreas (AP) algorithms that are currently being used to help people with T1D better manage their glucose levels. We present validation results comparing these virtual patients with true clinical patients undergoing AP control and demonstrate that the virtual patients behave similarly to people with T1D.
A single hormone virtual patient population (SH-VPP) was created that is comprised of eight differential equations that describe insulin kinetics, insulin dynamics and carbohydrate absorption. The parameters in this model that represent insulin sensitivity were statistically sampled from a normal distribution to create a population of virtual patients with different levels of insulin sensitivity. A dual hormone virtual patient population (DH-VPP) extended this SH-VPP by incorporating additional equations to represent glucagon kinetics and glucagon dynamics. The DH-VPP is comprised of thirteen differential equations and a parameter representing glucagon sensitivity, which was statistically sampled from a normal distribution to create virtual patients with different levels of glucagon sensitivity. We evaluated the SH-VPP and DH-VPP on a clinical data set of 20 people with T1D who participated in a 3.5-day outpatient AP study. Twenty virtual patients were matched with the 20 clinical patients by total daily insulin requirements and body weight. The identical meals given during the AP study were given to the virtual patients and the identical AP control algorithm that was used to control the glucose of the virtual patients was used on the clinical patients. We compared percent time in target range (70-180 mg/dL), time in hypoglycemia (<70 mg/dL) and time in hyperglycemia (>180 mg/dL) for both the virtual patients and the actual patients.
The subjects in the SH-VPP performed similarly vs. the actual patients (time in range: 78.1 ± 5.1% vs. 74.3 ± 8.1%, p = 0.11; time in hypoglycemia: 3.4 ± 1.3% vs. 2.8 ± 1.7%, p = 0.23). The subjects in the DH-VPP also performed similarly vs. the actual patients (time in range: 75.6 ± 5.5% vs. 71.9 ± 10.9%, p = 0.13; time in hypoglycemia: 0.9 ± 0.8% vs. 1.3 ± 1%, p = 0.19). While the VPPs tended to over-estimate the time in range relative to actual patients, the difference was not statistically significant.
We have verified that a SH-VPP and a DH-VPP performed comparably with actual patients undergoing AP control using an identical control algorithm. The SH-VPP and DH-VPP may be used as a simulator for pre-evaluation of T1D control algorithms.
The brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterone’s ...impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterone’s widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded—and perhaps driven—by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brain’s functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain.
•Intrinsic fluctuations in sex hormones shape the brain’s functional architecture.•Estradiol facilitates tighter coherence within whole-brain functional networks.•Progesterone has the opposite, reductive effect.•Ovulation (via estradiol) modulates variation in topological network states.•Effects are pronounced in network hubs densely populated with estrogen receptors.
The present study aims at monitoring and classifying the multi-variant wear behavior of sliding bearings. For this purpose, acoustic emission (AE) technique was applied to a test rig for sliding ...bearings. AE signals were evaluated with machine learning methods in order to detect anomalies from a hydrodynamic bearing operation. Furthermore, a deep learning approach based on convolutional neural networks was used for multi-class classification into three different wear failure modes, namely running-in, inadequate lubrication and particle-contaminated oil. A high accuracy and high sensitivity have been achieved in the detection and classification of three-body abrasion due to particle contamination. In the cases of running-in and inadequate lubrication, the incubation period during the onset of inadequate lubrication is sometimes mistaken for running-in and vice-versa, which reduces the overall accuracy of the classification.
•Fault detection in sliding bearings on the basis of acoustic emission signals.•Acoustic emission signal analysis with continuous wavelet transform.•Multi-class classification with a convolutional neural network.•Method to classify multi-variant wear mechanisms in sliding bearings.
Driven by the potential applications of sliding bearings in planetary gearboxes for wind turbines, the wear prognosis of heavy loaded sliding bearings under low rotational speeds is an important ...aspect. The aims of this study are to identify an adequate condition monitoring technique and demonstrate the potential of data-driven wear monitoring for scenarios, where transient wear data for data-driven monitoring is not available.
In a first step, Acoustic Emission (AE) technique has been applied to a special test rig for planetary gearbox sliding bearings. It was demonstrated that AE can be used to distinguish between wear-critical mixed friction and hydrodynamic regime. In a second step, a data-driven method for wear monitoring was developed and applied to a component test rig for sliding bearings. For validation and generation of condition monitoring data, sliding bearings from the same bronze material were subjected to steady speed conditions in mixed lubrication regime with different loads as well as runtime. The results from these experiments and results from validated physical wear simulations are the input parameters for the proposed data-driven approach. The wear prognosis is performed with recurrent neural networks, which can predict the transient degradation. With the developed data-driven approach to wear monitoring, a good accuracy can be achieved that is capable of real-time wear monitoring.
•Suitability of AE to efficiently detect mixed friction conditions in gearbox sliding bearings.•Data-driven wear monitoring approach for the reliability assessment of sliding bearings.•Use of validated physical wear simulations for the training of a machine learning model.
Rapid, flexible reconfiguration of connections across brain regions is thought to underlie successful cognitive control. Two intrinsic networks in particular, the cingulo-opercular (CO) and ...fronto-parietal (FP), are thought to underlie two operations critical for cognitive control: task-set maintenance/tonic alertness and adaptive, trial-by-trial updating. Using functional magnetic resonance imaging, we directly tested whether the functional connectivity of the CO and FP networks was related to cognitive demands and behavior. We focused on working memory because of evidence that during working memory tasks the entire brain becomes more integrated. When specifically probing the CO and FP cognitive control networks, we found that individual regions of both intrinsic networks were active during working memory and, as expected, integration across the two networks increased during task blocks that required cognitive control. Crucially, increased integration between each of the cognitive control networks and a task-related, non-cognitive control network (the hand somatosensory-motor network; SM) was related to increased accuracy. This implies that dynamic reconfiguration of the CO and FP networks so as to increase their inter-network communication underlies successful working memory.
Objective:To develop evidence-based recommendations for the management of systemic glucocorticoid (GC) therapy in rheumatic diseases.Methods:The multidisciplinary guideline development group from 11 ...European countries, Canada and the USA consisted of 15 rheumatologists, 1 internist, 1 rheumatologist–epidemiologist, 1 health professional, 1 patient and 1 research fellow. The Delphi method was used to agree on 10 key propositions related to the safe use of GCs. A systematic literature search of PUBMED, EMBASE, CINAHL, and Cochrane Library was then used to identify the best available research evidence to support each of the 10 propositions. The strength of recommendation was given according to research evidence, clinical expertise and perceived patient preference.Results:The 10 propositions were generated through three Delphi rounds and included patient education, risk factors, adverse effects, concomitant therapy (ie, non-steroidal anti-inflammatory drugs, gastroprotection and cyclo-oxygenase-2 selective inhibitors, calcium and vitamin D, bisphosphonates) and special safety advice (ie, adrenal insufficiency, pregnancy, growth impairment).Conclusion:Ten key recommendations for the management of systemic GC-therapy were formulated using a combination of systematically retrieved research evidence and expert consensus. There are areas of importance that have little evidence (ie, dosing and tapering strategies, timing, risk factors and monitoring for adverse effects, perioperative GC-replacement) and need further research; therefore also a research agenda was composed.
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