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

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454

Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis

Abstract—Late onset neonatal sepsis is one of the major clinical concerns when premature babies receive intensive care. Current practice relies on slow laboratory testing of blood cultures for diagnosis. A valuable research question is whether sepsis can be reliably detected before the blood sample is taken. This paper investigates the extent to which physiological events observed in the patient’s monitoring traces could be used for the early detec-tion of neonatal sepsis. We model the distribution of these events with an autoregressive hidden Markov model (AR-HMM).Both learning and inference carefully use domain knowledge to extract the baby’s true physiology from the monitoring data. Our model can produce real-time predictions about the onset of the infection and also handles missing data. We evaluate the effectiveness of the AR-HMM for sepsis detection on a dataset collected from the Neonatal Intensive Care Unit (NICU) at the Royal Infirmary of Edinburgh. Index Terms—neonatal sepsis, autoregressive hidden Markov model, real-time inference, intensive care. I

Adaptive, locally-linear models of complex dynamics

The dynamics of complex systems generally include high-dimensional, non-stationary and non-linear behavior, all of which pose fundamental challenges to quantitative understanding. To address these difficulties we detail a new approach based on local linear models within windows determined adaptively from the data. While the dynamics within each window are simple, consisting of exponential decay, growth and oscillations, the collection of local parameters across all windows provides a principled characterization of the full time series. To explore the resulting model space, we develop a novel likelihood-based hierarchical clustering and we examine the eigenvalues of the linear dynamics. We demonstrate our analysis with the Lorenz system undergoing stable spiral dynamics and in the standard chaotic regime. Applied to the posture dynamics of the nematode $C. elegans$ our approach identifies fine-grained behavioral states and model dynamics which fluctuate close to an instability boundary, and we detail a bifurcation in a transition from forward to backward crawling. Finally, we analyze whole-brain imaging in $C. elegans$ and show that the stability of global brain states changes with oxygen concentration.Comment: 25 pages, 16 figure

Dataan perustuva tapa ennustaa vastasyntyneiden lääketieteellisiä diagnooseja

Preterm infants with a very low birth weight are at a great risk of dying or of developing certain life-threatening complications due to their underdevelopment. These critically ill infants are treated at neonatal intensive care units, in which their physiological condition is monitored continuously. In this thesis, machine learning is applied on the monitored parameter recordings and other patient-specific information from Children's Hospital, Helsinki University Hospital. The purpose is to use binary classifiers to predict neonatal mortality and occurrence of three morbidities: bronchopulmonary dysplasia, necrotising enterocolitis, and retinopathy of prematurity. Majority of the current studies have focused on comparing only a few classifiers. Therefore, a wider comparison of classifier algorithms is performed in this work. In addition to a common measure, the prediction performance is evaluated with two less used measures: F1 score and area under the precision-recall curve. Additionally, the impact of data preprocessing and feature selection on the prediction result is studied. The results show large differences in the performance of classifiers. Random forests, k-nearest neighbours, and logistic regression result in the highest F1 scores. The highest values of area under the precision-recall curve are achieved by random forests along with Gaussian processes. If area under the ROC curve is measured, random forests, Gaussian processes, and support vector machines perform the best. The monitored physiological parameters are time series and their sampling technique can be altered. This shows only a negligible impact on the results. However, lengthening the monitoring time of physiological parameters to 36-48 hours has a little but positive effect on the results. On the other hand, feature selection has a significant role: birth weight and gestational age are crucial for a high performance. Further, combining them with other features improves the performance. For all that, the optimal data preprocessing procedure is classifier- and complication-specific.Syntymäpainoltaan hyvin pienet keskoset ovat suuressa riskissä kuolla tai saada hengenvaarallisia komplikaatioita alikehittyneisyyden takia. Näitä vakavasti sairaita vauvoja hoidetaan vastasyntyneiden teho-osastoilla, joissa heidän fysiologista kuntoaan valvotaan jatkuvasti. Tämä tutkielma soveltaa koneoppimista valvottujen parametrien tallenteisiin ja muihin potilaskohtaisiin tietoihin, jotka on saatu HUS:n Lastenklinikalta. Tarkoituksena on käyttää binääristä luokittelua ennustamaan vastasyntyneiden kuolleisuutta ja kolmen sairauden puhkeamista. Nämä sairaudet ovat bronkopulmonaalinen dysplasia, nekrotisoiva enterokoliitti sekä keskosten retionopatia. Suurin osa nykyisestä tutkimuksesta on keskittynyt vertailemaan vain muutamia luokittelijoita. Tässä työssä vertaillaan siksi suurempaa määrää eri luokittelualgoritmeja. Yhden yleisesti käytetyn mitan lisäksi ennusteita arvioidaan myös kahdella vähemmän käytetyllä arviointimitalla: F1-arvolla ja tarkkuus-herkkyys-käyrän alapuolisella alueella. Myös datan esikäsittelyn ja piirteiden valinnan vaikutusta ennustustulokseen tutkitaan. Tulokset osoittavat suuria eroja eri luokittelijoiden välillä. Satunnaismetsillä, k-lähimmän naapurin luokittimella sekä logistisella regressiolla saadaan korkeimmat F1-arvot. Suurimmat tarkkuus-herkkyys-käyrän alapuoliset alueet saavutetaan satunnaismetsillä sekä Gaussisten prosessien luokittimilla. Jos taas ROC-käyrän alapuolinen alue mitataan, satunnaismetsät, Gaussisten prosessien luokitin ja tukivektorikoneet toimivat parhaiten. Seuratut fysiologiset parametrit ovat aikasarjoja, joten niiden näytteenottotapaa voidaan muuttaa. Tällä on vain pieni vaikutus tuloksiin. Fysiologisten parametrien seuranta-ajan pidentämisellä 36-48 tuntiin on kuitenkin pieni, mutta myönteinen vaikutus tuloksiin. Piirteiden valinnalla on puolestaan merkittävästi väliä: syntymäpaino ja gestaatioikä ovat ratkaisevia hyvien tulosten saamiseksi. Niiden yhdistäminen muiden piirteiden kanssa parantaa tuloksia. Ihanteellinen datan esikäsittely on kaikesta huolimatta luokittelija- ja komplikaatiokohtaista

Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring

We present a Discriminative Switching Linear Dynamical System (DSLDS) applied to patient monitoring in Intensive Care Units (ICUs). Our approach is based on identifying the state-of-health of a patient given their observed vital signs using a discriminative classifier, and then inferring their underlying physiological values conditioned on this status. The work builds on the Factorial Switching Linear Dynamical System (FSLDS) (Quinn et al., 2009) which has been previously used in a similar setting. The FSLDS is a generative model, whereas the DSLDS is a discriminative model. We demonstrate on two real-world datasets that the DSLDS is able to outperform the FSLDS in most cases of interest, and that an $\alpha$-mixture of the two models achieves higher performance than either of the two models separately