Unknown-Multiple Speaker clustering using HMM

An HMM-based speaker clustering framework is presented, where the number of speakers and segmentation boundaries are unknown \emph{a priori}. Ideally, the system aims to create one pure cluster for each speaker. The HMM is ergodic in nature with a minimum duration topology. The final number of clusters is determined automatically by merging closest clusters and retraining this new cluster, until a decrease in likelihood is observed. In the same framework, we also examine the effect of using only the features from highly voiced frames as a means of improving the robustness and computational complexity of the algorithm. The proposed system is assessed on the 1996 HUB-4 evaluation test set in terms of both cluster and speaker purity. It is shown that the number of clusters found often correspond to the actual number of speakers

Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae

Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.Comment: 10 pages, SBP-BRiMS 201

The non-Verbal Structure of Patient Case Discussions in Multidisciplinary Medical Team Meetings

Meeting analysis has a long theoretical tradition in social psychology, with established practical rami?cations in computer science, especially in computer supported cooperative work. More recently, a good deal of research has focused on the issues of indexing and browsing multimedia records of meetings. Most research in this area, however, is still based on data collected in laboratories, under somewhat arti?cial conditions. This paper presents an analysis of the discourse structure and spontaneous interactions at real-life multidisciplinary medical team meetings held as part of the work routine in a major hospital. It is hypothesised that the conversational structure of these meetings, as indicated by sequencing and duration of vocalisations, enables segmentation into individual patient case discussions. The task of segmenting audio-visual records of multidisciplinary medical team meetings is described as a topic segmentation task, and a method for automatic segmentation is proposed. An empirical evaluation based on hand labelled data is presented which determines the optimal length of vocalisation sequences for segmentation, and establishes the competitiveness of the method with approaches based on more complex knowledge sources. The effectiveness of Bayesian classi?cation as a segmentation method, and its applicability to meeting segmentation in other domains are discusse

Modelling the effects of glucagon during glucose tolerance testing.

From Europe PMC via Jisc Publications RouterHistory: ppub 2019-12-01, epub 2019-12-12Publication status: PublishedBACKGROUND:Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. RESULTS:Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). CONCLUSIONS:The models are used to investigate how different degrees of pax'tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

A prospective study of artificially sweetened beverage intake and cardiometabolic health among women at high risk

BackgroundArtificially sweetened beverages (ASBs) are commonly consumed and recommended for individuals at high risk for cardiometabolic diseases; however, the health effects of ASBs remain contradictory. Given that cross-sectional analyses are subject to reverse causation, prospective studies with long-term follow-up are needed to evaluate associations between ASBs and cardiometabolic health, especially among high-risk individuals.ObjectiveThe aim of this study was to examine associations of ASB intake and cardiometabolic health among high-risk women with prior gestational diabetes mellitus (GDM).MethodsWe included 607 women with GDM from the Danish National Birth Cohort (DNBC; 1996-2002) who completed a clinical exam 9-16 y after the DNBC pregnancy for the Diabetes & Women's Health (DWH) Study (2012-2014). We assessed ASB intake using FFQs completed during the DNBC pregnancy and at the DWH Study clinical exam. We examined cardiometabolic outcomes at the DWH clinical exam. We estimated percentage differences in continuous cardiometabolic markers and RRs for clinical endpoints in association with ASB intake both during pregnancy and at follow-up adjusted for prepregnancy BMI, diet, and lifestyle factors. Sensitivity analyses to account for reverse causation were performed.ResultsIn pregnancy and at follow-up, 30.4% and 36.4% of women regularly (≥2 servings/wk) consumed ASB, respectively. Consumption of ASBs, both during pregnancy and at follow-up, was associated with higher glycated hemoglobin (HbA1c), insulin, HOMA-IR, triglycerides, liver fat, and adiposity and with lower HDL at follow-up. After adjustment for covariates, particularly prepregnancy BMI, the majority of associations between ASB intake in pregnancy and outcomes at follow-up became null with the exception of HbA1c. ASB intake at follow-up (≥1 serving/d compared with <1 serving/mo) was associated with higher HbA1c (6.5%; 95% CI: 1.9, 11.3; P-trend = 0.007); however, associations were not upheld in sensitivity analyses for reverse causation.ConclusionsAmong Danish women with a history of GDM, ASB intake was not significantly associated with cardiometabolic profiles

Improved Unknown-Multiple Speaker clustering using HMM

In this report, we build up on our previous work on speaker clustering, where the number of speakers and segmentation boundaries are unknown a priori. We employ an ergodic HMM with minimum duration topology for this purpose. Starting from a large number of clusters in the beginning, we merge a pair of clusters in every iteration. A new criterion for the merging of two clusters is proposed, which ensures an increase in likelihood of the data. The merging is done in such a way that, the total number of parameters needed to model all the clusters remain same. Thus, the system finally achieves maximum likelihood (which was not the case in our last work) with the constant number of parameters. The merging process is repeated until there are no candidates available for merging. The efficiency and advantages of using only highly voiced frames was reported in our previous work, and we use the same features for this work. The system was evaluated on Hub-4 1996 evaluation set. Improvements over the previous work are reported and it is shown that, the system converges to right number of clusters in case of limited number of speakers

Improved unknown-multiple speaker clustering using HMM,” IDIAP Reseach Report RR-02-23

An HMM-based speaker clustering framework is presented, where the number of speakers and segmentation boundaries are unknown a priori. Ideally, the system aims to create one pure cluster for each speaker. The HMM is ergodic in nature with a minimum duration topology. The final number of clusters is determined automatically by merging closest clusters and retraining this new cluster, until a decrease in likelihood is observed. In the same framework, we also examine the effect of using only the features from highly voiced frames as a means of improving the robustness and computational complexity of the algorithm. The proposed system is assessed on the 1996 HUB-4 evaluation test set in terms of both cluster and speaker purity. It is shown that the number of clusters found often correspond to the actual number of speakers. 1

Unknown-Multiple Speaker Clustering Using Hmm

An HMM-based speaker clustering framework is presented, where the number of speakers and segmentation boundaries are unknown a priori. Ideally, the system aims to create one pure cluster for each speaker. The HMM is ergodic in nature with a minimum duration topology. The final number of clusters is determined automatically by merging closest clusters and retraining this new cluster, until a decrease in likelihood is observed. In the same framework, we also examine the effect of using only the features from highly voiced frames as a means of improving the robustness and computational complexity of the algorithm. The proposed system is assessed on the 1996 HUB-4 evaluation test set in terms of both cluster and speaker purity. It is shown that the number of clusters found often correspond to the actual number of speakers