Comparative Analysis of Prognostic Model for Risk Classification of Neonatal Jaundice using Machine Learning Algorithms

This study focused on the development of a prediction model using identified classification factors in order to classify the risk of jaundice in selected neonates. Historical dataset on the distribution of the classification of risk of jaundice among neonates was collected using questionnaires following the identification of associated classification factors of risk of jaundice from medical practitioners. The dataset containing information about the classification factors identified and collected from the neonates were used to formulate predictive model for the classification of risk of jaundice using 2 machine learning algorithm – Naïve Bayes’ classifier and the multi-layer perceptron.The predictive model development using the decision trees algorithm was formulated and simulated using the WEKA software.The predictive model developed using the multi-layer perceptron and Naïve Bayes’ classifier algorithms were compared in order to determine the algorithm with the best performance.The result shows that 10 variables were identified by the medical expert to be necessary in predicting jaundice in neonates for which a dataset containing information of 23 neonates alongside their respective jaundice diagnosis (Low, Moderate and High) was also provided with 22 attributes following the identification of the required variables.The 10-fold cross validation method was used to train the predictive model developed using the machine learning algorithms and the performance of the models evaluated The multi-layer perceptron algorithm proved to be an effective algorithm for predicting the diagnosis of jaundice in Nigerian neonate

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

Validating the early phototherapy prediction tool across cohorts

Background: Hyperbilirubinemia of the newborn infant is a common disease worldwide. However, recognized early and treated appropriately, it typically remains innocuous. We recently developed an early phototherapy prediction tool (EPPT) by means of machine learning (ML) utilizing just one bilirubin measurement and few clinical variables. The aim of this study is to test applicability and performance of the EPPT on a new patient cohort from a different population. Materials and methods: This work is a retrospective study of prospectively recorded neonatal data from infants born in 2018 in an academic hospital, Regensburg, Germany, meeting the following inclusion criteria: born with 34 completed weeks of gestation or more, at least two total serum bilirubin (TSB) measurement prior to phototherapy. First, the original EPPT—an ensemble of a logistic regression and a random forest—was used in its freely accessible version and evaluated in terms of the area under the receiver operating characteristic curve (AUROC). Second, a new version of the EPPT model was re-trained on the data from the new cohort. Third, the predictive performance, variable importance, sensitivity and specificity were analyzed and compared across the original and re-trained models. Results: In total, 1,109 neonates were included with a median (IQR) gestational age of 38.4 (36.6–39.9) and a total of 3,940 bilirubin measurements prior to any phototherapy treatment, which was required in 154 neonates (13.9%). For the phototherapy treatment prediction, the original EPPT achieved a predictive performance of 84.6% AUROC on the new cohort. After re-training the model on a subset of the new dataset, 88.8% AUROC was achieved as evaluated by cross validation. The same five variables as for the original model were found to be most important for the prediction on the new cohort, namely gestational age at birth, birth weight, bilirubin to weight ratio, hours since birth, bilirubin value. Discussion: The individual risk for treatment requirement in neonatal hyperbilirubinemia is robustly predictable in different patient cohorts with a previously developed ML tool (EPPT) demanding just one TSB value and only four clinical parameters. Further prospective validation studies are needed to develop an effective and safe clinical decision support system

Applying naive bayesian networks to disease prediction: A systematic review

Introduction: Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction. Objective: This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms. Methods: PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study. Result: In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms. Discussion: This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy. Conclusion: The method, termed NBNs is proposed and can efficiently construct a prediction model for disease. � 2016 Mostafa Langarizadeh and Fateme Moghbeli

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques

A brain tumor is a severe malignant condition caused by uncontrolled and abnormal cell division. Recent advances in deep learning have aided the health business in Medical Imaging for the diagnosis of numerous disorders. The most frequent and widely used deep learning algorithm for visual learning and image recognition. This research seeks to multi-classification tumors in the brain from images attained by Magnetic Resonance Imaging (MRI) using deep learning models that have been pre-trained for transfer learning. As per the publicly available MRI brain tumor dataset, brain tumors identified as glioma, meningioma, and pituitary, are accounting for most brain tumors. To ensure the robustness of the suggested method, data acquisition, and preprocessing are performed in the first step followed by data augmentation. Finally, Transfer Learning algorithms including DenseNet, ResNetV2, and InceptionResNetv2 have been applied to find out the optimum algorithm based on various parameters including accuracy, precision, and recall, and are under the curve (AUC). The experimental outcomes show that the model’s validation accuracy is high for DenseNet (about 97%), while ResNetv2 and InceptionResNetv2 achieved 77% and 80% only

STDMn+p0: a multidimensional patient oriented data mining framework for critical care research

In the neonatal intensive care unit (NICU) environment, critical care and treatment directly correlate to the multidimensional development of an infant and are influenced by attributes such as gender and gestational age (GA). Recent literature on guidelines developed for neonatal intensive care; do not take the gender or the GA of the infant into account. The exponential activity of a growing neonate in its early stages of life needs to be captured and embedded into algorithms designed to extract patterns of predictive temperament within the NICU domain. The STDMn+p0 framework presents an extended multidimensional approach with the ability to create patient characteristic clinical rules. Further defining NICU algorithms, through the extended use of attributes to include gender and GA, and using these new algorithms in clinical decision support systems increases the accuracy and thereby minimizes the risk of adverse events