Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services

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

Clinical risk modelling with machine learning: adverse outcomes of pregnancy

As a complex biological process, there are various health issues that are related to pregnancy. Prenatal care, a type of preventative healthcare at different points in gestation is comprised of management, treatment, and mitigation of such issues. This also includes risk prediction for adverse pregnancy outcomes, where probabilistic modelling is used to calculate individual’s risk at the early stages of pregnancy. This type of modelling can have a definite clinical scope such as in prenatal screening, and an educational aim where awareness of a healthy lifestyle is promoted, such as in health education. Currently, the most used models are based on traditional statistical approaches, as they provide sufficient predictive power and are easily interpreted by clinicians. Machine learning, a subfield of data science, contains methods for building probabilistic models with multidimensional data. Compared to existing prediction models related to prenatal care, machine learning models can provide better results by fitting more intricate nonlinear decision boundary areas, improve data-driven model fitting by generating synthetic data, and by providing more automation for routine model adjustment processes. This thesis presents the evaluation of machine learning methods to prenatal screening and health education prediction problems, along with novel methods for generating synthetic rare disorder data to be used for modelling, and an adaptive system for continuously adjusting a prediction model to the changing patient population. This way the thesis addresses all the four main entities related to predicting adverse outcomes of pregnancy: the mother or patient, the clinician, the screening laboratory and the developer or manufacturer of screening materials and systems.Kliinisen riskin mallinnus koneoppimismenetelmin: raskaudelle haitalliset lopputulemat Raskaus on kompleksinen biologinen prosessi, jonka etenemiseen liittyy useita terveysongelmia. Äitiyshoito voidaan kuvata ennalta ehkäiseväksi terveydenhuolloksi, jossa pyritään käsittelemään, hoitamaan ja lievittämään kyseisiä ongelmia. Tähän hoitoon sisältyy myös raskauden haitallisten lopputulemien riskilaskenta, missä probabilistista mallinnusta hyödynnetään määrittämään yksilön riski raskauden varhaisissa vaiheissa. Tällä mallinnuksella voi olla selkeä kliininen tarkoitus kuten prenataaliseulonta, tai terveyssivistyksellinen tarkoitus missä odottavalle äidille esitellään raskauden kannalta terveellisiä elämäntapoja. Tällä hetkellä eniten käytössä olevat ennustemallit perustuvat perinteiseen tilastolliseen mallinnukseen, sille ne tarjoavat riittävän ennustetehokkuuden ja ovat helposti tulkittavissa. Koneoppiminen on datatieteen osa-alue, joka pitää sisällään menetelmiä millä voidaan mallintaa moniulotteista dataa ennustekäyttöön. Verrattuna olemassa oleviin äitiyshoidon ennustemalleihin, koneoppiminen mahdollistaa parempien ennustetulosten tuottamisen sovittamalla hienojakoisempia epälineaarisia päätösalueita, tehostamalla datakeskeisten mallien sovitusta luomalla synteettisiä havaintoja ja tarjoamalla enemmän automaatiota rutiininomaiseen mallien hienosäätöön. Tämä väitös esittelee koneoppimismenetelmien evaluaation prenataaliseulonta-ja terveyssivistysongelmiin, ja uusia menetelmiä harvinaisten sairauksien datan luomiseen mallinnustarkoituksiin ja jatkuvan ennustemallin hienosäätämisen järjestelmän muuttuvia potilaspopulaatiota varten. Näin väitös käy läpi kaikki neljä asianomaista jotka liittyvät haitallisten lopputulemien ennustamiseen: odottava äiti eli potilas, kliinikko, seulontalaboratorio ja seulonnassa käytettävien materiaalien ja järjestelmien kehittäjä tai valmistaja

A comprehensive integrative approach to investigate factors associated with preterm birth, related perinatal outcomes and its prediction using metabolomic markers

Orientador: José Guilherme CecattiTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências MédicasResumo: Introdução: O parto prematuro é uma das principais causas de morbimortalidade perinatal, neonatal e de crianças até 5 anos de idade e suas causas e fisiopatologia ainda são pouco conhecidas. Identificar quais são as mulheres de maior risco e desenvolver modelos de predição é ainda um grande desafio, potencialmente impactando nas medidas preventivas. Objetivo: Desenvolver uma abordagem abrangente com diferentes estudos e produtos relacionados aos fatores clínicos e epidemiológicos associados ao parto prematuro, seus preditores metabolômicos e respectivos desfechos perinatais. Métodos: Diferentes projetos de pesquisa e métodos foram utilizados, incluindo: duas análises secundárias de um estudo multicêntrico de corte transversal avaliando a associação do índice de massa corpórea (IMC), o ganho de peso gestacional por semana e fenótipos maternos com a ocorrência de prematuridade e desfechos maternos e perinatais; uma revisão narrativa sobre ciência ômica aplicada na área de saúde materna e perinatal, com enfoque na metabolômica; uma revisão sistemática e seu respectivo artigo de protocolo sobre a performance da metabolômica em predizer prematuridade espontânea em mulheres assintomáticas; dois artigos abordando o desenvolvimento do método e dos procedimentos técnicos para um estudo multicêntrico prospectivo para investigar parto prematuro; um estudo caso-controle aninhado a uma coorte multicêntrica internacional para identificar preditores clínicos e metabolômicos para prematuridade espontânea; dois artigos originais abordando a incidência, fatores clínicos e epidemiológicos e os desfechos maternos e perinatais associados ao parto prematuro em uma coorte multicêntrica no Brasil com gestantes nulíparas de baixo risco. Resultados: Nas análises secundárias do EMIP, observou-se que independente do IMC inicial, quanto maior o ganho de peso materno, maior a probabilidade para todos os subtipos de prematuridade, exceto para prematuridade espontânea em mulheres com IMC normal ou sobrepeso. Foram identificados três clusters de mulheres com parto prematuro, sendo um caracterizado principalmente por mulheres sem nenhuma das condições de risco, o segundo por mulheres com várias condições (cluster misto) e o terceiro por mulheres que tiveram pré-eclâmpsia, eclâmpsia, síndrome HELLP e/ou restrição de crescimento fetal. A revisão narrativa aborda os métodos e o embasamento teórico das ciências ômicas, como a genômica, transcriptômica, proteômica e metabolômica, dando enfoque especial à aplicação dessa última técnica na área de saúde materna e perinatal. A identificação e validação de marcadores pode auxiliar na predição e também no entendimento da fisiopatologia de doenças complexas como a prematuridade. A técnica de metabolômica identificou mais de 140 metabólitos nas amostras de soro de gestantes nulíparas e três destes foram significativamente associados com parto prematuro espontâneo nas amostras de Cork, Irlanda. Modelos preditores usando marcadores clínicos e metabolômicos mostraram uma área sob a curva ROC de 0,73 e 0,85 para parto prematuro abaixo de 37 e 34 semanas, respectivamente. Conclusão: O ganho de peso gestacional, um fator modificável, mostrou diferentes associações com a probabilidade de parto prematuro, a depender do IMC inicial. Possíveis investigações de risco e de prevenção devem considerar essa evidência. A utilização de critérios clínicos no rastreamento e predição do parto prematuro ainda mostra limitações. A análise por cluster, por exemplo, mostrou que um número considerável não possui nenhuma das condições pré-definidas como potencialmente associadas ao parto prematuro. A aplicação de estudos da ciência Ômica parece ser uma abordagem adequada para a identificação da etiologia e de marcadores para predição de complicações maternas e perinatais, embora ainda necessitem de sucessivas validações e evidência de reprodutibilidade. O desenvolvimento, implementação e coordenação de um estudo multicêntrico para estudar preditores e fatores associados ao parto prematuro requer recursos humanos qualificados, infraestrutura para pesquisa adequada, comprometimento institucional e envolvimento de agências de fomento e desenvolvimento de pesquisa. O modelo preditor para parto prematuro espontâneo em mulheres nulíparas mostra resultados de boa performance, entretanto requer futuras validações antes de qualquer uso clínico. É provável que os metabólitos que compõem o modelo não sejam identificados da mesma forma em outras populaçõesAbstract: Introduction: Preterm birth is the leading cause of perinatal, neonatal and under-5 year¿s morbidity and mortality. Identifying women at higher risk and developing prediction models remains a great challenge, potentially affecting preventive interventions. Objectives: To develop a comprehensive approach including diverse study designs to investigate clinical and epidemiological risk factors associated with preterm birth, its metabolomics predictors and respective perinatal outcomes. Methods: Different projects and methods were applied in this thesis, including: two secondary analysis of a multicentre cross-sectional with a nested case-control study addressing the association of maternal body mass index (BMI), gestational weight gain per week and phenotypes with the occurrence of preterm birth and maternal and perinatal outcomes; an integrative review about omics sciences applied to maternal and perinatal health, focusing on metabolomics; a systematic review and respective protocol investigating the performance of metabolomics to predict spontaneous preterm birth (sPTB) in asymptomatic women; two articles describing the methods, clinical protocol, technical procedures for the development and implementation of a multicentre prospective cohort study to investigate preterm birth and other maternal and perinatal complications; a nested case-control from a multicentre international cohort to identify clinical and metabolomics predictors for sPTB; two articles addressing incidence, clinical and epidemiological risk factors and maternal and perinatal outcomes associated with sPTB in a Brazilian multicentre cohort of low-risk nulliparous pregnant women. Results: According to the EMIP secondary analyses, the greater the rate of weight gain, the higher the predicted probability for all preterm birth subtypes regardless the initial BMI, except in normal BMI or overweight women and sPTB. Three clusters of women with preterm birth were identified; cluster one of women without any pre-defined conditions, cluster two with mixed conditions and cluster three with women who had preeclampsia, eclampsia, HELLP syndrome and/or fetal growth restriction. Maternal and perinatal outcomes did not differ between clusters. An integrative review addressed Omis Science's methods and theoretical background, as genomics, transcriptomics, proteomics and metabolomics, focusing on the application on maternal and perinatal health. Metabolomics approach has been applied to better understand the pathophysiology and to identify and validate predictors for complex diseases as preterm birth. Metabolomics technique identified more than 140 metabolites in serum samples of nulliparous pregnant women and three of them were significantly associated with sPTB in samples from Cork, Ireland. Predictive models associating metabolites and clinical markers showed an area under ROC curve of 0.73 and 0.85 for sPTB below 37 and 34 weeks, respectively. Conclusion: Gestational weight gain, a modifiable factor, showed to have different associations with the predicted probability for preterm birth, depending on the initial BMI. The use of clinical criteria in the screening of preterm birth still shows limited performance. Cluster analysis, for instance, showed that a substantial number of women does not present the predefined potential conditions associated with preterm birth. Omics science studies might be a reasonable approach to investigate the aetiology and predictive markers for maternal and perinatal complications. Metabolomic studies addressing the prediction for sPTB, preeclampsia, gestational diabetes mellitus and fetal growth restriction show promising findings, although they still require repeated validations and reproducibility. The development, implementation and management of a multicenter study to investigate factors associated with sPTB requires qualified human resources, adequate infrastructure, institutional commitment and the involvement of funding and research agencies. The predictive model for sPTB in nulliparous women showed a good performance, although further validation is required before clinical application. Possibly, reproducibility of the predictive model is limited, once metabolites comprising the model were only identified in one of the subsetDoutoradoSaúde Materna e PerinatalDoutor em Ciências da SaúdeCAPE

Clinical information extraction for preterm birth risk prediction

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Towards optimum smoking cessation interventions during pregnancy: a household model to explore cost‐effectiveness

BACKGROUND AND AIMS: Previous economic evaluations of smoking cessation interventions for pregnant women are limited to single components, which do not in isolation offer sufficient potential impact to address smoking cessation targets. To inform the development of more appropriate complex interventions, we (1) describe the development of the Economics of Smoking in Pregnancy: Household (ESIP.H) model for estimating the life‐time cost‐effectiveness of smoking cessation interventions aimed at pregnant women and (2) use a hypothetical case study to demonstrate how ESIP.H can be used to identify the characteristics of optimum smoking cessation interventions. METHODS: The hypothetical intervention was based on current evidence relating to component elements, including financial incentives, partner smoking, intensive behaviour change support, cigarettes consumption and duration of support to 12 months post‐partum. ESIP.H was developed to assess the life‐time health and cost impacts of multi‐component interventions compared with standard National Health Service (NHS) care in England. ESIP.H considers cigarette consumption, partner smoking and some health conditions (e.g. obesity) that were not included in previous models. The Markov model's parameters were estimated based on published literature, expert judgement and evidence‐based assumptions. The hypothetical intervention was evaluated from an NHS perspective. RESULTS: The hypothetical intervention was associated with an incremental gain in quitters (mother and partner) at 12 months postpartum of 249 [95% confidence interval (CI) = 195–304] per 1000 pregnant smokers. Over the long‐term, it had an incremental negative cost of £193 (CI = –£779 to 344) and it improved health, with a 0.50 (CI = 0.36–0.69) increase in quality‐adjusted life years (QALYs) for mothers, partners and offspring, with a 100% probability of being cost‐effective. CONCLUSIONS: The Economics of Smoking in Pregnancy: Household model for estimating cost‐effectiveness of smoking cessation interventions aimed at pregnant women found that a hypothetical smoking cessation intervention would greatly extend reach, reduce smoking and be cost‐effective