926 research outputs found
Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data
Preterm births occur at an alarming rate of 10-15%. Preemies have a higher
risk of infant mortality, developmental retardation and long-term disabilities.
Predicting preterm birth is difficult, even for the most experienced
clinicians. The most well-designed clinical study thus far reaches a modest
sensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a different
approach by exploiting databases of normal hospital operations. We aims are
twofold: (i) to derive an easy-to-use, interpretable prediction rule with
quantified uncertainties, and (ii) to construct accurate classifiers for
preterm birth prediction. Our approach is to automatically generate and select
from hundreds (if not thousands) of possible predictors using stability-aware
techniques. Derived from a large database of 15,814 women, our simplified
prediction rule with only 10 items has sensitivity of 62.3% at specificity of
81.5%.Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC
2016), Los Angeles, C
Gaussian process classification for prediction of in-hospital mortality among preterm infants
We present a method for predicting preterm infant in-hospital mortality using Bayesian Gaussian process classification. We combined features extracted from sensor measurements, made during the first 72 h of care for 598 Very Low Birth Weight infants of birth weight <1500 g, with standard clinical features calculated on arrival at the Neonatal Intensive Care Unit. Time periods of 12, 18, 24, 36, 48, and 72 h were evaluated. We achieved a classification result with area under the receiver operating characteristic curve of 0.948, which is in excess of the results achieved by using the clinical standard SNAP-II and SNAPPE-II scores. (C) 2018 Elsevier B.V. All rights reserved.Peer reviewe
Machine Learning Methods for Neonatal Mortality and Morbidity Classification
Preterm birth is the leading cause of mortality in children under the age of five. In particular, low birth weight and low gestational age are associated with an increased risk of mortality. Preterm birth also increases the risks of several complications, which can increase the risk of death, or cause long-term morbidities with both individual and societal impacts. In this work, we use machine learning for prediction of neonatal mortality as well as neonatal morbidities of bronchopulmonary dysplasia, necrotizing enterocolitis, and retinopathy of prematurity, among very low birth weight infants. Our predictors include time series data and clinical variables collected at the neonatal intensive care unit of Children's Hospital, Helsinki University Hospital. We examine 9 different classifiers and present our main results in AUROC, similar to our previous studies, and in F1-score, which we propose for classifier selection in this study. We also investigate how the predictive performance of the classifiers evolves as the length of time series is increased, and examine the relative importance of different features using the random forest classifier, which we found to generally perform the best in all tasks. Our systematic study also involves different data preprocessing methods which can be used to improve classifier sensitivities. Our best classifier AUROC is 0.922 in the prediction of mortality, 0.899 in the prediction of bronchopulmonary dysplasia, 0.806 in the prediction of necrotizing enterocolitis, and 0.846 in the prediction of retinopathy of prematurity. Our best classifier F1-score is 0.493 in the prediction of mortality, 0.704 in the prediction of bronchopulmonary dysplasia, 0.215 in the prediction of necrotizing enterocolitis, and 0.368 in the prediction of retinopathy of prematurity.Peer reviewe
ASSESSMENT OF RISK IN PRETERM INFANTS USING POINT PROCESS AND MACHINE LEARNING APPROACHES
Preemies, infants who are born too soon, have a higher incidence of Life-Threatening Events (LTE’s) such as apnea (cessation of breathing), bradycardia (slowing of heart rate) and hypoxemia (oxygen desaturation) also termed as ABD (Apnea, Bradycardia, and Desaturation) events. Clinicians at Neonatal Intensive Care Units (NICU) are facing the demanding task of assessing the risk of infants based on their physiological signals. The aim of this thesis is to develop a risk stratification algorithm using a machine-learning framework with the features related to pathological fluctuations derived from point process model that will be embedded into the current physiological recording system to assess the risk of life-threatening events well in advance of occurrence in individual infants in the NICU. We initially propose a point process algorithm of heart rate dynamics for risk stratification of preterm infants. Based on this analysis, point process indices were tested to determine whether they were useful as precursors for life-threatening events. Finally, a machine-learning framework using point process indices as precursors were designed and tested to classify the risk of preterm infants. This work helps to predict the number of bradycardia events, N, in the subsequent hours measuring point process indices for the current hour. The model proposed uses Quadratic Support Vector Machine (QSVM), a machine learning classifier, which can solve class optimization problems and execute data at an exponential speed with higher accuracy for risk assessment that might facilitate effective management and treatment for preterm infants in NICU. The findings are relevant to risk assessment by analyzing the fluctuations in physiological signals that can act as precursors for the future life-threatening events
Systematic review and network meta-analysis with individual participant data on cord management at preterm birth (iCOMP): study protocol
Introduction
Timing of cord clamping and other cord management strategies may improve outcomes at preterm birth. However, it is unclear whether benefits apply to all preterm subgroups. Previous and current trials compare various policies, including time-based or physiology-based deferred cord clamping, and cord milking. Individual participant data (IPD) enable exploration of different strategies within subgroups. Network meta-analysis (NMA) enables comparison and ranking of all available interventions using a combination of direct and indirect comparisons.
Objectives
(1) To evaluate the effectiveness of cord management strategies for preterm infants on neonatal mortality and morbidity overall and for different participant characteristics using IPD meta-analysis. (2) To evaluate and rank the effect of different cord management strategies for preterm births on mortality and other key outcomes using NMA.
Methods and analysis
Systematic searches of Medline, Embase, clinical trial registries, and other sources for all ongoing and completed randomised controlled trials comparing cord management strategies at preterm birth (before 37 weeks’ gestation) have been completed up to 13 February 2019, but will be updated regularly to include additional trials. IPD will be sought for all trials; aggregate summary data will be included where IPD are unavailable. First, deferred clamping and cord milking will be compared with immediate clamping in pairwise IPD meta-analyses. The primary outcome will be death prior to hospital discharge. Effect differences will be explored for prespecified participant subgroups. Second, all identified cord management strategies will be compared and ranked in an IPD NMA for the primary outcome and the key secondary outcomes. Treatment effect differences by participant characteristics will be identified. Inconsistency and heterogeneity will be explored.
Ethics and dissemination
Ethics approval for this project has been granted by the University of Sydney Human Research Ethics Committee (2018/886). Results will be relevant to clinicians, guideline developers and policy-makers, and will be disseminated via publications, presentations and media releases
Deciphering infant mortality. Part 1: empirical evidence
This paper is not (or at least not only) about human infant mortality. In
line with reliability theory, "infant" will refer here to the time interval
following birth during which the mortality (or failure) rate decreases. This
definition provides a systems science perspective in which birth constitutes a
sudden transition which falls within the field of application of the "Transient
Shock" (TS) conjecture put forward in Richmond et al. (2016c). This conjecture
provides predictions about the timing and shape of the death rate peak. (i) It
says that there will be a death rate spike whenever external conditions change
abruptly and drastically. (ii) It predicts that after a steep rising there will
be a much longer hyperbolic relaxation process. These predictions can be tested
by considering living organisms for which birth is a multi-step process. Thus,
for fish there are three states: egg, yolk-sac phase, young adult. The TS
conjecture predicts a mortality spike at the end of the yolk-sac phase, and
this timing is indeed confirmed by observation. Secondly, the hyperbolic nature
of the relaxation process can be tested using high accuracy Swiss statistics
which give postnatal death rates from one hour after birth up to the age of 10
years. It turns out that since the 19th century despite a great overall
reduction in infant mortality, the shape of the age-specific death rate has
remained basically unchanged. This hyperbolic pattern is not specific to
humans. It can also be found in small primates as recorded in the archives of
zoological gardens. Our ultimate objective is to set up a chain of cases which
starts from simple systems and then moves up step by step to more complex
organisms. The cases discussed here can be seen as initial landmarks.Comment: 46 pages, 14 figures, 4 table
Predicting neonatal sepsis using features of heart rate variability, respiratory characteristics and ECG-derived estimates of infant motion
This study in preterm infants was designed to characterize the prognostic potential of several features of heart rate variability (HRV), respiration, and (infant) motion for the predictive monitoring of late-onset sepsis (LOS). In a neonatal intensive care setting, the cardiorespiratory waveforms of infants with blood-culture positive LOS were analyzed to characterize the prognostic potential of 22 features for discriminating control from sepsis-state, using the Naïve Bayes algorithm. Historical data of the subjects acquired from a period sufficiently before the clinical suspicion of LOS was used as control state, whereas data from the 24 h preceding the clinical suspicion of LOS were used as sepsis state (test data). The overall prognostic potential of all features was quantified at three-hourly intervals for the period corresponding to test data by calculating the area under the receiver operating characteristics curve. For the 49 infants studied, features of HRV, respiration, and movement showed characteristic changes in the hours leading up to the clinical suspicion of sepsis, namely, an increased propensity toward pathological heart rate decelerations, increased respiratory instability, and a decrease in spontaneous infant activity, i.e., lethargy. While features characterizing HRV and respiration can be used to probe the state of the autonomic nervous system, those characterizing movement probe the state of the motor system-dysregulation of both reflects an increased likelihood of sepsis. By using readily interpretable features derived from cardiorespiratory monitoring, opportunities for pre-emptively identifying and treating LOS can be developed.</p
Newborn skin reflection: Proof of concept for a new approach for predicting gestational age at birth. A cross-sectional study
Current methods to assess the gestational age during prenatal care or at
birth are a global challenge. Disadvantages, such as low accessibility, high
costs, and imprecision of clinical tests and ultrasonography measurements, may
compromise health decisions at birth, based on the gestational age. Newborns
organs and tissues can indirectly indicate their physical maturity, and we
hypothesized that evolutionary changes in their skin, detected using an
optoelectronic device meter, may aid in estimating the gestational age. This
study analyzed the feasibility of using newborn skin reflectance to estimate
the gestational age at birth noninvasively. A cross-sectional study evaluated
the skin reflectance of selected infants, preferably premature, at birth. The
first-trimester ultrasound was the reference for gestational age. A prototype
of a new noninvasive optoelectronic device measured the backscattering of light
from the skin, using a light emitting diode at wavelengths of 470 nm, 575 nm,
and 630 nm. Univariate and multivariate regression analysis models were
employed to predict gestational age, combining skin reflectance with clinical
variables for gestational age estimation. The gestational age at birth of 115
newborns from 24.1 to 41.8 weeks of gestation correlated with the light at 630
nm wavelength reflectance 3.3 mm/6.5 mm ratio distant of the sensor, at the
forearm and sole . The best-combined variables to predict the gold standard
gestational age at birth was the skin reflectance at wavelengths of 630 nm and
470 nm in combination with birth weight, phototherapy, and adjusted to include
incubator stay, and sex. The main limitation of the study is that it was very
specific to the premature population we studied and needs to be studied in a
broader spectrum of newborns
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
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