Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women’s reproductive health. Pregnancy thus became a highly demanding phase in a woman’s life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.publishedVersio

PREDICTING NECROTIZING ENTEROCOLITIS IN HOSPITALIZED NEONATES

Necrotizing enterocolitis (NEC), a devastating disease of premature bowel, is challenging to predict. The disease is rare, with incompletely understood pathogenesis, rapid onset and progression, and insufficient diagnostic criteria. Using a systematic review of the literature, a cultivated dataset of published neonatal radiographs, and a publicly available neonatal critical care database, this dissertation examines novel approaches to improve predictions of NEC. First, in a review piece, we summarize surgical care for patients with NEC (Chapter 2). We provide a foundational framework to understanding NEC by describing the diverse presentations of the disease and discussing current best practices to reduce NEC-associated morbidity and mortality. Second, we conduct a systematic review of published prognostic models for predicting NEC onset and progression in hospitalized infants (Chapter 3). We find that published models have fair to poor discrimination of NEC outcomes and high risk of bias, limiting model clinical utility. Third, we develop an image classifier to support surgical resident recognition of pneumatosis intestinalis, a radiographic sign of NEC (Chapter 4). We find that a deep convolutional neural network trained on neonatal abdominal radiographs can successfully detect pneumatosis and performs comparably well to senior surgical residents. Fourth, we use the MIMIC III Clinical Database to develop an early warning score for NEC based on routinely available clinical data during an infant's stay in a neonatal intensive care unit (NICU) (Chapter 5). We find that models accurately predict NEC before disease onset, with first NEC risk detection occurring days previously. Fifth, in a perspective piece, we reflect on the promises and challenges of utilizing machine learning methods in NEC prediction and research (Chapter 6). We advocate for policy and practice changes to improve NEC prediction efforts. Overall, this dissertation highlights strengths and limitations of existing NEC prediction models and offers novel solutions to improve predictions of NEC in hospitalized neonates. We hope this dissertation helps researchers in pediatric surgery and neonatology identify steps to improve early detection of NEC, promote timely clinical management, and minimize the high morbidity and mortality of this disease

Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review

Background: Preterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs).Objective: This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach.Methods: This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported.Results: A total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1%) were screened by full text, resulting in 13 (1.8%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models’ characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve.Conclusions: Various ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set.</p

Development of machine learning schemes for use in non-invasive and continuous patient health monitoring

Stephanie Baker developed machine learning schemes for the non-invasive and continuous measurement of blood pressure and respiratory rate from heart activity waveforms. She also constructed machine learning models for mortality risk assessment from vital sign variations. This research contributes several tools that offer significant advancements in patient monitoring and wearable healthcare

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study

Introduction Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: Favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. Methods and analytics Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns' and mothers' clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. Ethics and dissemination This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study

Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data

IntroductionPreterm birth is a leading cause of infant mortality and morbidity. Despite the improvement in the overall mortality in premature infants, the intact survival of these infants remains a significant challenge. Screening the physical growth of infants is fundamental to potentially reducing the escalation of this disorder. Recently, machine learning models have been used to predict the growth restrictions of infants; however, they frequently rely on conventional risk factors and cross-sectional data and do not leverage the longitudinal database associated with medical data from laboratory tests.MethodsThis study aimed to present an automated interpretable ML-based approach for the prediction and classification of short-term growth outcomes in preterm infants. We prepared four datasets based on weight and length including weight baseline, length baseline, weight follow-up, and length follow-up. The CHA Bundang Medical Center Neonatal Intensive Care Unit dataset was classified using two well-known supervised machine learning algorithms, namely support vector machine (SVM) and logistic regression (LR). A five-fold cross-validation, and several performance measures, including accuracy, precision, recall and F1-score were used to compare classifier performances. We further illustrated the models’ trustworthiness using calibration and cumulative curves. The visualized global interpretations using Shapley additive explanation (SHAP) is provided for analyzing variables’ contribution to final prediction.ResultsBased on the experimental results with area under the curve, the discrimination ability of the SVM algorithm was found to better than that of the LR model on three of the four datasets with 81%, 76% and 72% in weight follow-up, length baseline and length follow-up dataset respectively. The LR classifier achieved a better ROC score only on the weight baseline dataset with 83%. The global interpretability results revealed that pregnancy-induced hypertension, gestational age, twin birth, birth weight, antenatal corticosteroid use, premature rupture of membranes, sex, and birth length were consistently ranked as important variables in both the baseline and follow-up datasets.DiscussionThe application of machine learning models to the early detection and automated classification of short-term growth outcomes in preterm infants achieved high accuracy and may provide an efficient framework for clinical decision systems enabling more effective monitoring and facilitating timely intervention

Improving Diagnostics with Deep Forest Applied to Electronic Health Records

An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations

SOCIOECONOMIC AND NEUROANATOMIC CONTRIBUTIONS TO LANGUAGE PERFORMANCE IN CHILDREN BORN VERY PRETERM AT PRESCHOOL AND SCHOOL AGE

Background: Children born very preterm are more likely to have difficulties with language acquisition and use that persist throughout childhood. Preterm birth occurs at a critical time in brain development and interrupts neurodevelopment, which has downstream implications for altered neural structure and function. Prematurity and socioeconomic status greatly impact language performance in children, but the neural substrates are poorly understood. Here the neural constituents of language performance are examined in select cortical and subcortical regions. Methods: Fifty-one children born preterm (24-31 weeks) and 20 born full-term were seen at preschool (mean age = 47 months) and school age (mean age = 74 months). Diverse aspects of language performance were evaluated at preschool and school age and were also aggregated into a single score using principle components analysis. At preschool age, measures of cortical thickness, surface area, subcortical volumes, and fractional anisotropy of white matter tracts were calculated for select frontotemporal regions implicated in language. Caregivers reported on many sociodemographic variables which were reduced using principle components analysis. Repeated measures general linear models were used to examine group differences in language performance and to determine the contributions of group, socioeconomic status, and neuroanatomical substrates to language performance. Results: Children born very preterm performed more poorly than children born full-term on tests of receptive language, verbal fluency and verbal working memory at preschool and school age. Five measures of language performance were reduced to one principle component at both preschool and school age. Socioeconomic status significantly accounted for language performance across groups and time points. Initial neuroanatomical analyses found that subcortical volumes significantly accounted for language performance. Analyses of language performance including neuroanatomy and socioeconomic status revealed that socioeconomic status had a significant main effect, as did some specific measures of cortical thickness, subcortical volumes and white matter tracts. Conclusions: Our findings provide support for poorer language performance in children born very preterm at preschool and school age. The relationship between structural neuroanatomic variations associated with preterm birth and language deficits is supported by our findings that language performance was significantly associated with subcortical volumes. This result highlights the possible importance of corticostriatal learning circuits in poorer language performance in children born very preterm. Importantly, our findings that socioeconomic status substantially accounted for language performance also emphasizes the multifactorial determinants of language problems in preterm birth, which is still poorly understood despite decades of research. Finally, these results have important implications for early intervention on an individual level, as well as policy reform to improve the broader social conditions and medical resources needed by so many Americans