Markov model

Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training methods with Ising machine physics, even though these methods are essential for achieving high accuracy. In this study, we demonstrate an efficient approach to train Ising machines in a supervised way through the Equilibrium Propagation algorithm, achieving comparable results to software-based implementations...

Vitamin B12 is a group of biologically active cobalamin compounds. In this study, we investigated the inhibitory effects of methylcobalamin (MeCbl) and hydroxocobalamin acetate (OHCbl Acetate) on protein tyrosine phosphatase 1B (PTP1B). MeCbl and OHCbl Acetate exhibited an IC50 of approximately 58.390 ± 2.811 μM and 8.998 ± 0.587 μM, respectively. The Ki values of MeCbl and OHCbl Acetate were 25.01 μM and 4.04 μM respectively...

This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments...

BACKGROUND: The quality-adjusted life year (QALY) is a metric that is increasingly used today in the field of health economics to evaluate the value of different medical treatments and procedures. Surgical waiting lists (SWLs) represent a pressing problem in public healthcare. The QALY measure has rarely been used in the context of surgery. It would be interesting to know how many QALYs are lost by patients on SWLs. AIM: To investigate the relationship between QALYs and SWLs in a systematic review of the scientific literature...

BACKGROUND: Obesity and overweight are a significant public health concern. Subcutaneous semaglutide 2.4 mg injection is a glucagon-like peptide-1 (GLP-1) analogue approved by the European Medicines Agency as an adjunct to a reduced calorie diet and increased physical activity (diet and exercise, D&E) for the treatment obesity and overweight in the presence of at least one weight related comorbidity. This study aimed to assess the cost-effectiveness of semaglutide 2.4 mg in combination with D&E compared to D&E alone for the Portuguese setting...

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening)...

OBJECTIVE: Since 2013, Flanders has introduced a screening programme for colorectal cancer for all citizens aged between 50 and 74 years. The objective of this study was to assess the cost-utility of an expansion of the colorectal cancer screening policy in Flanders (Belgium) and to place these findings in the international context. METHODS: Cost-utility analysis using high-detail data about screening participation, screening results, and epidemiological data, a Markov cohort model has been constructed to study long-term costs and effects...

INTRODUCTION: This research evaluates the budget impact of treating acute ischaemic stroke (AIS) using a combination of mechanical thrombectomy (MT) with stent retrievers (SR) and intravenous tissue-plasminogen activator (IV-tPA) in Australia. METHODS: This study examined the economic impact over five years for a patient cohort based on the number of patients treated with MT+ IV-tPA in Australia 2021, versus treatment with IV-tPA alone. A budget impact (BI) model was developed to project direct medical costs (economic impact) of IV-tPA+ MT with SR vs...

Measuring vital signs (VS) contained in the echoes is crucial to the analyses of breathing and heartbeat signals using medical radar. Although many advanced signal processing algorithms have been developed for radar-based VS measurement and make some improved progress, existing schemes cannot achieve a good estimation of echo phases modulated by the respiratory and cardiac activities with high accuracy or low computation, and thus resulting in serious performance degradation on the subsequent separation of breathing and heartbeat patterns as well as the assessment of breathing rate (BR), heart rate (HR), and heart rate variability (HRV)...

In this study, we designed a machine learning-based parallel global searching method using the Bayesian inversion framework for efficient identification of dense non-aqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Swarm intelligence organized hybrid-kernel extreme learning machine (SIO-HKELM) was proposed to approximate the forward and inverse input-output correlation with a high accuracy using the DNAPL transport numerical simulation model. An adaptive inverse-HKELM was established for preliminary estimation of the source characteristics and contaminant transport parameters to correct prior information and generate high-quality initial starting points of parallel searching...

INTRODUCTION: Several prevalence-based cost-of-illness (COI) studies have been conducted to estimate the economic burden of alcohol consumption borne by a particular society in a given year. Yet there are few studies examining the economic costs incurred by an individual drinker over his/her lifetime. Thus, this study aims to estimate the costs incurred by an individual drinker's alcohol consumption over his or her lifetime in Thailand. METHODS AND ANALYSIS: An incidence-based COI approach will be employed...

OBJECTIVES: We aimed to assess the healthcare costs and impact on the economy at large arising from emergency medical services (EMS) treated non-traumatic shock. DESIGN: We conducted a population-based cohort study, where EMS-treated patients were individually linked to hospital-wide and state-wide administrative datasets. Direct healthcare costs (Australian dollars, AUD) were estimated for each element of care using a casemix funding method. The impact on productivity was assessed using a Markov state-transition model with a 3-year horizon...

BACKGROUND: The Japanese National Immunization Program currently includes the pediatric 13valent pneumococcal conjugate vaccine (PCV13) to prevent pneumococcal infections. We aimed to evaluate the cost-effectiveness of 20-valent PCV (PCV20) as a pediatric vaccine versus PCV13. METHODS: A decision-analytic Markov model was used to estimate expected costs, qualityadjusted life-years (QALYs), and prevented cases and deaths caused by invasive pneumococcal disease, pneumonia, and acute otitis media over a ten-year time horizon from the societal and healthcare payer perspectives...

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types...

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation...

Advances in technology have enabled the use of sensors with varied modalities to monitor different parts of systems, each providing diverse levels of information about the underlying system. However, resource limitations and computational power restrict the number of sensors/data that can be processed in real-time in most complex systems. These challenges necessitate the need for selecting/scheduling a subset of sensors to obtain measurements that guarantee the best monitoring objectives. This paper focuses on sensor scheduling for systems modeled by hidden Markov models...

The Event Based Model (EBM) is a probabilistic generative model to explore biomarker changes occurring as a disease progresses. Disease progression is hypothesized to occur through a sequence of biomarker dysregulation "events". The EBM estimates the biomarker dysregulation event sequence. It computes the data likelihood for a given dysregulation sequence, and subsequently evaluates the posterior distribution on the dysregulation sequence. Since the posterior distribution is intractable, Markov Chain Monte-Carlo is employed to generate samples under the posterior distribution...

INTRODUCTION: The first targeted therapy in oncology, imatinib, revolutionized chronic myeloid leukemia (CML) treatment and spurred research in targeted therapies for various cancers. CML results from a chromosomal translocation, forming the BCR-ABL1 fusion gene. Asciminib has been recently approved for third-line refractory or intolerant patients. Treatment-free remission (TFR) is attainable with sustained deep molecular response (DMR) and this approach could be incorporated into pharmacoeconomic models...

This study investigates the impact of spatio- temporal correlation using four spatio-temporal models: Spatio-Temporal Poisson Linear Trend Model (SPLTM), Poisson Temporal Model (TMS), Spatio-Temporal Poisson Anova Model (SPAM), and Spatio-Temporal Poisson Separable Model (STSM) concerning food security and nutrition in Africa. Evaluating model goodness of fit using the Watanabe Akaike Information Criterion (WAIC) and assessing bias through root mean square error and mean absolute error values revealed a consistent monotonic pattern...

BACKGROUND AND AIMS: Familial hypercholesterolemia is an underdiagnosed genetic metabolic condition limiting the clearance of low-density lipoprotein cholesterol and increasing lifetime risk of cardiovascular disease. Population genetic screening in unselected individuals could quickly identify cases of familial hypercholesterolemia and enable early prevention, but the economic impact of widespread screening on patients has not been studied. METHODS: We assessed the cost-effectiveness of population genetic screening for familial hypercholesterolemia in 20 and 35-year-old adults in the United States from the perspective of patients...