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This thesis examines whether measurement of morbidity prevalence through survey methods provides a suitable alternative to mortality measurement for safe motherhood programme needs assessment. It considers the validity of a survey instrument by comparing results from a questionnaire on near-miss obstetric complications to hospital clinical data. Three groups of women -with severe obstetric complications, mild obstetric complications and with a normal delivery - were identified retrospectively in three hospitals in South Benin and interviewed at home using a questionnaire. The complications of interest were eclampsia, haemorrhage, dystocia and infections of the genital tract. The concept of near-miss death event was used to identify women with severe episodes of morbidity. The aim of the analysis was to find questions with very high specificity for measuring the prevalence of obstetric conditions even at the expense of sensitivity. The questionnaire was able to detect, with sufficient accuracy, eclamptic fits, abnormal bleeding in the third trimester for a recall period of at least 3-4 years, and all episodes of haemorrhage independent of timing within a shorter period of 2 years. The specificity of questions and combinations of questions for dystocia and infections of the genital tract was weak, and generated disappointing results except when information on treatment was included. Overall, better results were achieved for antepartum and acute events than complications defined as such because they are at the extreme end of a continuum. Severity only made a positive difference in the case of eclampsia with an increase in sensitivity. 1 These results are interpreted in the light of methodological constraints and findings from similar studies. Although the study could support the use of individual interview surveys for eclampsia and haemorrhage, this methodology cannot be readily recommended in view of the insufficient specificity reported elsewhere. The way forward in terms of morbidity information as well as the future of the near-miss concept is presented in the final chapter.

No / Real-world clinical setting is the major arena of traditional Chinese medicine (TCM) as it has experienced long-term practical clinical activities, and developed established theoretical knowledge and clinical solutions suitable for personalized treatment. Clinical phenotypes have been the most important features captured by TCM for diagnoses and treatment, which are diverse and dynamically changeable in real-world clinical settings. Together with clinical prescription with multiple herbal ingredients for treatment, TCM clinical activities embody immense valuable data with high dimensionalities for knowledge distilling and hypothesis generation. In China, with the curation of large-scale real-world clinical data from regular clinical activities, transforming the data to clinical insightful knowledge has increasingly been a hot topic in TCM field. This chapter introduces the application of data warehouse techniques and data mining approaches for utilizing real-world TCM clinical data, which is mainly from electronic medical records. The main framework of clinical data mining applications in TCM field is also introduced with emphasizing on related work in this field. The key points and issues to improve the research quality are discussed and future directions are proposed.

In this thesis, we describe an approach that won a psychiatric symptom severity prediction challenge. The challenge was to correctly predict the severity of psychiatric symptoms on a 4-point scale. Our winning submission uses a novel stacked machine learning architecture in which (i) a base data ingestion/cleaning step was followed by the (ii) derivation of a base set of features defined using text analytics, after which (iii) association rule learning was used in a novel way to generate new features, followed by a (iv) feature selection step to eliminate irrelevant features, followed by a (v) classifier training algorithm in which a total of 22 classifiers including new classifier variants of AdaBoost and RandomForest were trained on seven different data views, and (vi) finally an ensemble learning step, in which ensembles of best learners were used to improve on the accuracy of individual learners. All of this was tested via standard 10-fold cross-validation on training data provided by the N-GRID challenge organizers, of which the three best ensembles were selected for submission to N-GRID's blind testing. The best of our submitted solutions garnered an overall final score of 0.863 according to the organizer's measure. All 3 of our submissions placed within the top 10 out of the 65 total submissions. The challenge constituted Track 2 of the 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDOC Individualized Domains (N-GRID) Shared Task in Clinical Natural Language Processing.

In many practical scenarios, prediction for high-dimensional observations can be accurately performed using only a fraction of the existing features. However, the set of relevant predictive features, known as the sparsity pattern, varies across data. For instance, features that are informative for a subset of observations might be useless for the rest. In fact, in such cases, the dataset can be seen as an aggregation of samples belonging to several low-dimensional sub-models, potentially due to different generative processes. My thesis introduces several techniques for identifying sparse predictive structures and the areas of the feature space where these structures are effective. This information allows the training of models which perform better than those obtained through traditional feature selection. We formalize Informative Projection Recovery, the problem of extracting a set of low-dimensional projections of data which jointly form an accurate solution to a given learning task. Our solution to this problem is a regression-based algorithm that identifies informative projections by optimizing over a matrix of point-wise loss estimators. It generalizes to a number of machine learning problems, offering solutions to classification, clustering and regression tasks. Experiments show that our method can discover and leverage low-dimensional structure, yielding accurate and compact models. Our method is particularly useful in applications involving multivariate numeric data in which expert assessment of the results is of the essence. Additionally, we developed an active learning framework which works with the obtained compact models in finding unlabeled data deemed to be worth expert evaluation. For this purpose, we enhance standard active selection criteria using the information encapsulated by the trained model. The advantage of our approach is that the labeling effort is expended mainly on samples which benefit models from the hypothesis class we are considering. Additionally, the domain experts benefit from the availability of informative axis aligned projections at the time of labeling. Experiments show that this results in an improved learning rate over standard selection criteria, both for synthetic data and real-world data from the clinical domain, while the comprehensible view of the data supports the labeling process and helps preempt labeling errors.

Clinico-genomic modeling refers to the statistical analysis that incorporates both clinical data such as medical test results, demographic information and genomic data such as gene expression profiles. It is an emerging research area in biomedical science and has been shown to be able to extend our understanding of complex diseases. We describe a general statistical modeling strategy for the integrated analysis of clinical and genomic data in which the clinical data are longitudinal observations. Our modeling strategy is aimed at the identification of disease-associated genes and it consists of two stages. In the first stage, we propose a hierarchical B spline model to estimate the disease severity trajectory based on the clinical variables. This disease severity trajectory is a functional summary of the disease progression. We can extract any characteristics of interest from the trajectory. In the second stage, combinations of the extracted characteristics are included in the gene-wise linear model to detect the genes that are responsible for variations in the disease progression. We illustrate our modeling approach in the context of two biomedical studies of complex diseases: tuberculosis (Tb) and colitis-associated carcinoma. The animal experimental subjects were measured longitudinally for clinical information and biological samples were extracted at the final points of the subjects to determine the gene expression profiles. Our results demonstrate that the incorporation of the longitudinal clinical data increases the value of information extracted from the expression profiles and contributes to the identification of predictive biomarkers. / acase@tulane.edu

京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第12801号 / 論医博第2073号 / 新制||医||1001(附属図書館) / 80845 / 京都大学大学院薬学研究科創薬科学専攻 / (主査)教授 川上 浩司, 教授 松原 和夫, 教授 今中 雄一 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

Indiana University-Purdue University Indianapolis (IUPUI) / The information gained from sequencing of the human genome has begun to transform human biology and genetic medicine. The discovery of functionally important genetic variation lies at the heart of these endeavors, and there has been substantial progress in understanding the common patterns of single-nucleotide polymorphism (SNP) in humans- the most frequent type of variation in humans. Although more than 99% of human DNA sequences are the same across the population, variations in DNA sequence have a major impact on how we humans respond to disease; to environmental entities such as bacteria, viruses, toxins, and chemicals; and drugs and other therapies and thus studying differences between our genomes is vital. This makes SNPs as well other genetic variation data of great value for biomedical research and for developing pharmaceutical products or medical diagnostics. The goal of the project is to link genetic variation data to biological pathways and networks data, and also to clinical data for creating a framework for translational and systems biology studies. The study of the interactions between the components of biological systems and biological pathways has become increasingly important. It is known and accepted by scientists that it as important to study different biological entities as interacting systems, as in isolation. This project has ideas rooted in this thinking aiming at the integration of a genetic variation dataset with biological pathways dataset. Annotating genetic variation data with standardized disease notation is a very difficult yet important endeavor. One of the goals of this research is to identify whether informatics approaches can be applied to automatically annotate genetic variation data with a classification of diseases.

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In the past three years, evidence based medicine emerged as a powerful force in an effort to improve quality and health outcomes, and to reduce cost of care. Computerized physician order entry (CPOE) applications brought safety and efficiency features to clinical settings, including ease of ordering medications via pre-defined sets. Order sets offer promise of standardized care beyond convenience features through evidence-based practices built upon a growing and powerful knowledge of clinical professionals to achieve potentially more consistent health outcomes with patients and to reduce frequency of medical errors, adverse drug effects, and unintended side effects during treatment. While order sets existed in paper form prior to the introduction of CPOE, their true potential was only unleashed with support of clinical informatics, at those healthcare facilities that installed CPOE systems and reap rewards of standardized care. Despite ongoing utilization of order sets at facilities that implemented CPOE, there is a lack of quantitative evidence behind their benefits. Comprehensive research into their impact requires a history of electronic medical records necessary to produce large population samples to achieve statistically significant results. The study, conducted at a large Midwest healthcare system consisting of several community and academic hospitals, was aimed at quantitatively analyzing benefits of the order sets applied to prevent venous thromboembolism (VTE) and treat pneumonia, congestive heart failure (CHF), and acute myocardial infarction (AMI) - testing hospital mortality, readmission, complications, and length of stay (LOS) as health outcomes. Results indicated reduction of acute VTE rates among non-surgical patients in the experimental group, while LOS and complications benefits were inconclusive. Pneumonia patients in the experimental group had lower mortality, readmissions, LOS, and complications rates. CHF patients benefited from order sets in terms of mortality and LOS, while there was no sufficient data to display results for readmissions and complications. Utilization of AMI order sets was insufficient to produce statistically significant results. Results will (1) empower health providers with evidence to justify implementation of order sets due to their effectiveness in driving improvements in health outcomes and efficiency of care and (2) provide researchers with new ideas to conduct health outcomes research.