Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis

Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have determined combinations of different fluorescent markers which generate relatively known expression patterns for specific subtypes of leukemia and lymphoma -- cancers of the hematopoietic system. By only viewing a series of 2-dimensional projections, the high-dimensional nature of the data is rarely exploited. In this paper we present a means of determining a low-dimensional projection which maintains the high-dimensional relationships (i.e. information) between differing oncological data sets. By using machine learning techniques, we allow clinicians to visualize data in a low dimension defined by a linear combination of all of the available markers, rather than just 2 at a time. This provides an aid in diagnosing similar forms of cancer, as well as a means for variable selection in exploratory flow cytometric research. We refer to our method as Information Preserving Component Analysis (IPCA).Comment: 26 page

Visualising the cross-level relationships between pathological and physiological processes and gene expression: analyses of haematological diseases.

The understanding of pathological processes is based on the comparison between physiological and pathological conditions, and transcriptomic analysis has been extensively applied to various diseases for this purpose. However, the way in which the transcriptomic data of pathological cells relate to the transcriptomes of normal cellular counterparts has not been fully explored, and may provide new and unbiased insights into the mechanisms of these diseases. To achieve this, it is necessary to develop a method to simultaneously analyse components across different levels, namely genes, normal cells, and diseases. Here we propose a multidimensional method that visualises the cross-level relationships between these components at three different levels based on transcriptomic data of physiological and pathological processes, by adapting Canonical Correspondence Analysis, which was developed in ecology and sociology, to microarray data (CCA on Microarray data, CCAM). Using CCAM, we have analysed transcriptomes of haematological disorders and those of normal haematopoietic cell differentiation. First, by analysing leukaemia data, CCAM successfully visualised known relationships between leukaemia subtypes and cellular differentiation, and their characteristic genes, which confirmed the relevance of CCAM. Next, by analysing transcriptomes of myelodysplastic syndromes (MDS), we have shown that CCAM was effective in both generating and testing hypotheses. CCAM showed that among MDS patients, high-risk patients had transcriptomes that were more similar to those of both haematopoietic stem cells (HSC) and megakaryocyte-erythroid progenitors (MEP) than low-risk patients, and provided a prognostic model. Collectively, CCAM reveals hidden relationships between pathological and physiological processes and gene expression, providing meaningful clinical insights into haematological diseases, and these could not be revealed by other univariate and multivariate methods. Furthermore, CCAM was effective in identifying candidate genes that are correlated with cellular phenotypes of interest. We expect that CCAM will benefit a wide range of medical fields

Would you be surprised if this patient died?: Preliminary exploration of first and second year residents' approach to care decisions in critically ill patients

BACKGROUND: How physicians approach decision-making when caring for critically ill patients is poorly understood. This study aims to explore how residents think about prognosis and approach care decisions when caring for seriously ill, hospitalized patients. METHODS: Qualitative study where we conducted structured discussions with first and second year internal medicine residents (n = 8) caring for critically ill patients during Medical Intensive Care Unit Ethics and Discharge Planning Rounds. Residents were asked to respond to questions beginning with "Would you be surprised if this patient died?" RESULTS: An equal number of residents responded that they would (n = 4) or would not (n = 4) be surprised if their patient died. Reasons for being surprised included the rapid onset of an acute illness, reversible disease, improving clinical course and the patient's prior survival under similar circumstances. Residents reported no surprise with worsening clinical course. Based on the realization that their patient might die, residents cited potential changes in management that included clarifying treatment goals, improving communication with families, spending more time with patients and ordering fewer laboratory tests. Perceived or implied barriers to changes in management included limited time, competing clinical priorities, "not knowing" a patient, limited knowledge and experience, presence of diagnostic or prognostic uncertainty and unclear treatment goals. CONCLUSIONS: These junior-level residents appear to rely on clinical course, among other factors, when assessing prognosis and the possibility for death in severely ill patients. Further investigation is needed to understand how these factors impact decision-making and whether perceived barriers to changes in patient management influence approaches to care

Constraint based approaches to interpretable and semi-supervised machine learning

Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social media and governance. A related major challenge in deploying ML systems pertains to reliable learning when expert annotation is severely limited. This dissertation prescribes a common framework to address these challenges, based on the use of constraints that can make an ML model more interpretable, lead to novel methods for explaining ML models, or help to learn reliably with limited supervision. In particular, we focus on the class of latent variable models and develop a general learning framework by constraining realizations of latent variables and/or model parameters. We propose specific constraints that can be used to develop identifiable latent variable models, that in turn learn interpretable outcomes. The proposed framework is first used in Non–negative Matrix Factorization and Probabilistic Graphical Models. For both models, algorithms are proposed to incorporate such constraints with seamless and tractable augmentation of the associated learning and inference procedures. The utility of the proposed methods is demonstrated for our working application domain – identifiable phenotyping using Electronic Health Records (EHRs). Evaluation by domain experts reveals that the proposed models are indeed more clinically relevant (and hence more interpretable) than existing counterparts. The work also demonstrates that while there may be inherent trade–offs between constraining models to encourage interpretability, the quantitative performance of downstream tasks remains competitive. We then focus on constraint based mechanisms to explain decisions or outcomes of supervised black-box models. We propose an explanation model based on generating examples where the nature of the examples is constrained i.e. they have to be sampled from the underlying data domain. To do so, we train a generative model to characterize the data manifold in a high dimensional ambient space. Constrained sampling then allows us to generate naturalistic examples that lie along the data manifold. We propose ways to summarize model behavior using such constrained examples. In the last part of the contributions, we argue that heterogeneity of data sources is useful in situations where very little to no supervision is available. This thesis leverages such heterogeneity (via constraints) for two critical but widely different machine learning algorithms. In each case, a novel algorithm in the sub-class of co–regularization is developed to combine information from heterogeneous sources. Co–regularization is a framework of constraining latent variables and/or latent distributions in order to leverage heterogeneity. The proposed algorithms are utilized for clustering, where the intent is to generate a partition or grouping of observed samples, and for Learning to Rank algorithms – used to rank a set of observed samples in order of preference with respect to a specific search query. The proposed methods are evaluated on clustering web documents, social network users, and information retrieval applications for ranking search queries.Electrical and Computer Engineerin

Liquid biopsy in ovarian cancer: recent advances in circulating extracellular vesicle detection for early diagnosis and monitoring progression.

The current biomarkers available in the clinic are not enough for early diagnosis or for monitoring disease progression of ovarian cancer. Liquid biopsy is a minimally invasive test and has the advantage of early diagnosis and real-time monitoring of treatment response. Although significant progress has been made in the usage of circulating tumor cells and cell-free DNA for ovarian cancer diagnosis, their potential for early detection or monitoring progression remains elusive. Extracellular vesicles (EVs) are a heterogeneous group of lipid membranous particles released from almost all cell types. EVs contain proteins, mRNA, DNA fragments, non-coding RNAs, and lipids and play a critical role in intercellular communication. Emerging evidence suggests that EVs have crucial roles in cancer development and metastasis, thus holding promise for liquid biopsy-based biomarker discovery for ovarian cancer diagnosis. In this review, we discuss the advantages of EV-based liquid biopsy, summarize the protein biomarkers identified from EVs in ovarian cancer, and highlight the utility of new technologies recently developed for EV detection with an emphasis on their use for diagnosing ovarian cancer, monitoring cancer progression, and developing personalized medicine

Illness representations, treatment beliefs and the relationship to self-care in heart failure

Purpose The purpose of this study was to explore the beliefs people with heart failure hold about their illness and its treatment and to determine any relationships between these beliefs and self-care using the Common Sense Model (CSM) of illness cognitions and behaviour as the theoretical framework (Leventhal et al, 1980). Methods Using a mixed methodology (Creswell and Plano Clark, 2007), findings from patient interviews were used to adapt the Revised Illness Perception Questionnaire (IPQ-R) (Moss-Morris et al, 2002) and the Beliefs about Medicines Questionnaire (BMQ) (Horne et al, 1999) in order to make them illness-specific. A questionnaire assessing self-care was developed based on the European Heart Failure Self-care Behaviour Scale (EHFScBS) (Jaarsma et al, 2003), the interview findings and a nominal group technique with specialist heart failure nurses. These questionnaires were used to determine beliefs and the relationship to behaviour in a cross-sectional survey of 169 patients with heart failure. Results A number of statistically significant correlations were found between beliefs and self-care. Most notably, perceived medication knowledge (r = 0.51, p ≤ 0.01), beliefs about the necessity of medication (r = 0.45, p ≤ 0.01) and illness coherence (r = 0.39, p ≤ 0.01). Multiple regression analysis revealed that 46% of the variance in self-care could be explained by illness representations and treatment beliefs (Adj. R2 = 0.46, F = 9.93, p = 0.00). Three factors were significant predictors of self-care - medication knowledge (β = 0.319, p = 0.003), a belief in the illness having serious consequences (β = 0.258, p = 0.008) and the impact of medication use on lifestyle (β = -0.231, p = 0.03). Discussion The exploration of illness representations revealed a realistic picture of heart failure with a cluster of beliefs around a chronic illness with serious consequences and a high number of symptoms. There was a strong belief in the necessity of medication but for some, medication use had a negative impact on daily life. Patients were confident in their knowledge of medication but this was reduced when family members took control of medication management. A number of beliefs were predictive of self-care, suggesting that interventions designed to maximise these beliefs and correct any misconceptions may enhance self-care and potentially improve clinical outcomes in this population