1,721,022 research outputs found
A Combined Interpolation and Weighted K-Nearest Neighbours Approach for the Imputation of Longitudinal ICU Laboratory Data
No full textThe presence of missing data is a common problem that affects almost all clinical datasets. Since most available data mining and machine learning algorithms require complete datasets, accurately imputing (i.e. "filling in") the missing data is an essential step. This paper presents a methodology for the missing data imputation of longitudinal clinical data based on the integration of linear interpolation and a weighted K-Nearest Neighbours (KNN) algorithm. The Maximal Information Coefficient (MIC) values among features are employed as weights for the distance computation in the KNN algorithm in order to integrate intra- and inter-patient information. An interpolation-based imputation approach was also employed and tested both independently and in combination with the KNN algorithm. The final imputation is carried out by applying the best performing method for each feature. The methodology was validated on a dataset of clinical laboratory test results of 13 commonly measured analytes of patients in an intensive care unit (ICU) setting. The performance results are compared with those of 3D-MICE, a state-of-the-art imputation method for cross-sectional and longitudinal patient data. This work was presented in the context of the 2019 ICHI Data Analytics Challenge on Missing data Imputation (DACMI)
Relative auditory distance discrimination with virtual nearby sound sources
Full text linkIn this paper a psychophysical experiment targeted at exploring relative distance discrimination thresholds with binaurally rendered virtual sound sources in the near field is described. Pairs of virtual sources are spatialized around 6 different spatial locations (2 directions X3 reference distances) through a set of generic far-field Head-Related Transfer Functions (HRTFs) coupled with a nearfield correction model proposed in the literature, known as DVF (Distance Variation Function). Individual discrimination thresholds for each spatial location and for each of the two orders of presentation of stimuli (approaching or receding) are calculated on 20 subjects through an adaptive procedure. Results show that thresholds are higher than those reported in the literature for real sound sources, and that approaching and receding stimuli behave differently. In particular, when the virtual source is close < 25 cm) thresholds for the approaching condition are significantly lower compared to thresholds for the receding condition, while the opposite behaviour appears for greater distances (~ 1 m). We hypothesize such an asymmetric bias to be due to variations in the absolute stimulus level
Relative Auditory Distance Discrimination With Virtual Nearby Sound Sources
Full text linkIn this paper a psychophysical experiment targeted at exploring relative distance discrimination thresholds with binaurally rendered virtual sound sources in the near field is described. Pairs of virtual sources are spatialized around 6 different spatial locations (2 directions × 3 reference distances) through a set of generic far-field Head-Related Transfer Functions (HRTFs) coupled with a near-field correction model proposed in the literature, known as DVF (Distance Variation Function). Individual discrimination thresholds for each spatial location and for each of the two orders of presentation of stimuli (approaching or receding) are calculated on 20 subjects through an adaptive procedure. Results show that thresholds are higher than those reported in the literature for real sound sources, and that approaching and receding stimuli behave differently. In particular, when the virtual source is close (< 25 cm) thresholds for the approaching condition are significantly lower compared to thresholds for the receding condition, while the opposite behaviour appears for greater distances (~ 1 m). We hypothesize such an asymmetric bias to be due to variations in the absolute stimulus level
Modelling Inflammatory Bowel Diseases trajectories combining dynamic, multifactorial, Artificial Intelligence-based approaches
Full text linkTowards Value-Based Healthcare and the Role of Regional Agencies: the Approach of the Veneto Region
Full text linkInterpolation and K-Nearest Neighbours Combined Imputation for Longitudinal ICU Laboratory Data
No full text
Investigating the Dynamics of Cardio-Metabolic Comorbidities and Their Interactions in Ageing Adults Through Dynamic Bayesian Networks
No full textWith increased longevity, the likelihood of developing multiple chronic diseases also increases. Among these, cardio-metabolic comorbidities represent a burden both in terms of individual quality of life and public health. Understanding the impact of risk factors and unravelling possible cross-effects between comorbidities themselves can facilitate care management and prevention strategies. In this work, we present a model of ageing progression and the onset of three cardio-metabolic diseases, namely type 2 diabetes, hypertension, and heart problems, together with survival, based on socio-demographic, clinical, and biomarkers data of more than 11,000 subjects available in the English Longitudinal Study of Ageing. Leveraging dynamic Bayesian networks, our model effectively captures the probabilistic relationships between risk factors and morbidities over time, with many biological interactions known from the literature correctly encoded, such as the effect of body mass index and physical activity on the onset of cardio-metabolic diseases. Noticeably, some cross-relationships between outcomes’ occurrence also emerge, with an increased risk of heart problems in the presence of hypertension. In addition to the graphical description of the ageing process, we propose a simulation strategy that allows us to predict in silico the progression of the clinical state of a new patient population (iAUC between 0.62–0.83 for all outcomes), as well as a stratification analysis that allows investigating the effect of selected variables on the risk of developing morbidity. This approach provides valuable support for the acquisition of knowledge aimed at designing prevention strategies and targeted interventions to improve the health status of the ageing population
A Dynamic Bayesian Network model for simulating the progression to diabetes onset in the ageing population
No full texthis work presents a tool based on a Dynamic Bayesian Network (DBN) model to simulate the progression to type 2 diabetes (T2D) onset in the ageing population. Including longitudinally collected features characterizing different aspects of the ageing process, we dynamically model the relationships among the variables and the outcome over time, obtaining a network that shows a direct joined effect of glycated hemoglobin and body mass index (BMI) on the T2D onset. Remarkably, DBNs present a broad interpretability regardless of their complexity. We also employ the model to assess the impact of modifiable risk factors on developing the disease, showing how an increased BMI leads to an augmented T2D risk
- …
