Dynamical models for neonatal intensive care monitoring

The vital signs monitoring data of an infant receiving intensive care are a rich source of information about its health condition. One major concern about the state of health of such patients is the onset of neonatal sepsis, a life-threatening bloodstream infection. As early signs are subtle and current diagnosis procedures involve slow laboratory testing, sepsis detection based on the monitored physiological dynamics is a clinically significant task. This challenging problem can be thoroughly modelled as real-time inference within a machine learning framework. In this thesis, we develop probabilistic dynamical models centred around the goal of providing useful predictions about the onset of neonatal sepsis. This research is characterised by the careful incorporation of domain knowledge for the purpose of extracting the infant’s true physiology from the monitoring data. We make two main contributions. The first one is the formulation of sepsis detection as learning and inference in an Auto-Regressive Hidden Markov Model (AR-HMM). The model investigates the extent to which physiological events observed in the patient’s monitoring traces could be used for the early detection of neonatal sepsis. In addition, the proposed approach involves exact marginalisation over missing data at inference time. When applying the ARHMM on a real-world dataset, we found that it can produce effective predictions about the onset of sepsis. Second, both sepsis and clinical event detection are formulated as learning and inference in a Hierarchical Switching Linear Dynamical System (HSLDS). The HSLDS models dynamical systems where complex interactions between modes of operation can be represented as a twolevel hidden discrete hierarchical structure. For neonatal condition monitoring, the lower layer models clinical events and is controlled by upper layer variables with semantics sepsis/nonsepsis. The model parameterisation and estimation procedures are adapted to the specifics of physiological monitoring data. We demonstrate that the performance of the HSLDS for the detection of sepsis is not statistically different from the AR-HMM, despite the fact that the latter model is given “ground truth” annotations of the patient’s physiology

The Assesment of the Decayed Lime Wood Polymeric Components by TG and FT-IR Parameters Correlation

The restoration and conservation practices of wooden artifacts increasingly need more nondestructive, inexpensive and no-time consuming diagnosis methods. A number of twelve samples of lime wood in various states of degradation from furniture and panel painting were investigated by thermal analysis and infrared spectroscopy. The present work was undertaken to quantify the degree of complementarity of the TG and FT-IR methods by assessing >the ration between cellulose, hemicelluloses and lignin of the decayed lime wood by the correlation of the parameters RTG = ΔmHC /ΔmL from TGA data, and R1FTIR = Aab1370/Aab1505, R2FTIR = Abz1370/Abz1505, R3 FTIR = Iab1370/Iab1505, R4FTIR = Ibz1370/Ibz1505 , from the corresponding peaks of cellulose and hemicelluloses at ~ 1370 cm-1 and of lignin at 1505 cm-1 in FT-IR spectra, where RTG is the ratio between the mass loss of cellulose & hemicelluloses ΔmHC and the mass loss of lignin ΔmL; R1FTIR, R2FTIR, are the ratio between the absolute peaks area Aab1370/Aab1505, and the normalized peaks area Abz1370/Abz1505, respectively, while R3FTIR, R4FTIR are the ratio between the absolute peaks intensity Iab1370/Iab1505, and the normalized peaks intensity Ibz1370/Ibz1505, respectively. We found that the TG parameter decreases in the same order as FT-IR parameters R1 FTIR, R2FTIR, R3FTIR, R4FTIR, and relates well in the values with R1FTIR