Bayesian Learning in the Counterfactual World

Recent years have witnessed a surging interest towards the use of machine learning tools for causal inference. In contrast to the usual large data settings where the primary goal is prediction, many disciplines, such as health, economic and social sciences, are instead interested in causal questions. Learning individualized responses to an intervention is a crucial task in many applied fields (e.g., precision medicine, targeted advertising, precision agriculture, etc.) where the ultimate goal is to design optimal and highly-personalized policies based on individual features. In this work, I thus tackle the problem of estimating causal effects of an intervention that are heterogeneous across a population of interest and depend on an individual set of characteristics (e.g., a patient's clinical record, user's browsing history, etc..) in high-dimensional observational data settings. This is done by utilizing Bayesian Nonparametric or Probabilistic Machine Learning tools that are specifically adjusted for the causal setting and have desirable uncertainty quantification properties, with a focus on the issues of interpretability/explainability and inclusion of domain experts' prior knowledge. I begin by introducing terminology and concepts from causality and causal reasoning in the first chapter. Then I include a literature review of some of the state-of-the-art regression-based methods for heterogeneous treatment effects estimation, with an attempt to build a unifying taxonomy and lay down the finite-sample empirical properties of these models. The chapters forming the core of the dissertation instead present some novel methods addressing existing issues in individualized causal effects estimation: Chapter 3 develops both a Bayesian tree ensemble method and a deep learning architecture to tackle interpretability, uncertainty coverage and targeted regularization; Chapter 4 instead introduces a novel multi-task Deep Kernel Learning method particularly suited for multi-outcome | multi-action scenarios. The last chapter concludes with a discussion

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

The Lumberjack, April 27, 1983

The student newspaper of Humboldt State University.https://digitalcommons.humboldt.edu/studentnewspaper/2016/thumbnail.jp

The Wooster Voice (Wooster, OH), 1982-05-14

The College updates its decade old investment frame. Students request to be paid on every two weeks, rather than once per month. Students respond to a previous letter to the editor in which a woman named Elizabeth Koreman disparaged modern feminists. Voice staff rate the various pizza restaurants in Wooster.https://openworks.wooster.edu/voice1981-1990/1286/thumbnail.jp

The Montclarion, March 15, 1990

Student Newspaper of Montclair State Collegehttps://digitalcommons.montclair.edu/montclarion/1586/thumbnail.jp

Mirror - Vol. 23, No. 03 - October 02, 1997

The Mirror (sometimes called the Fairfield Mirror) is the official student newspaper of Fairfield University, and is published weekly during the academic year (September - May). It runs from 1977 - the present; current issues are available online.https://digitalcommons.fairfield.edu/archives-mirror/1463/thumbnail.jp

The real-time molecular characterisation of human brain tumours during surgery using Rapid Evaporative Ionization Mass Spectrometry [REIMS] and Raman spectroscopy: a platform for precision medicine in neurosurgery

Aim: To investigate new methods for the chemical detection of tumour tissue during neurosurgery. Rationale: Surgeons operating on brain tumours currently lack the ability to directly and immediately assess the presence of tumour tissue to help guide resection. Through developing a first in human application of new technology we hope to demonstrate the proof of concept that chemical detection of tumour tissue is possible. It will be further demonstrated that information can be obtained to potentially aid treatment decisions. This new technology could, therefore, become a platform for more effective surgery and introducing precision medicine to Neurosurgery. Methods: Molecular analysis was performed using Raman spectroscopy and Rapid Evaporative Ionization Mass Spectrometry (REIMS). These systems were first developed for use in brain surgery. A single centre prospective observational study of both modalities was designed involving a total of 75 patients undergoing craniotomy and resection of a range of brain tumours. A neuronavigation system was used to register spectral readings in 3D space. Precise intraoperative readings from different tumour zones were taken and compared to matched core biopsy samples verified by routine histopathology. Results: Multivariate statistics including PCA/LDA analysis was used to analyse the spectra obtained and compare these to the histological data. The systems identified normal versus tumour tissue, tumour grade, tumour type, tumour density and tissue status of key markers of gliomagenesis. Conclusions: The work in this thesis provides proof of concept that useful real time intraoperative spectroscopy is possible. It can integrate well with the current operating room setup to provide key information which could potentially enhance surgical safety and effectiveness in increasing extent of resection. The ability to group tissue samples with respect to genomic data opens up the possibility of using this information during surgery to speed up treatment, escalate/deescalate surgery in specific phenotypic groups to introduce precision medicine to Neurosurgery.Open Acces

Spartan Daily, May 22, 1973

Volume 60, Issue 126https://scholarworks.sjsu.edu/spartandaily/5760/thumbnail.jp

Automated deep phenotyping of the cardiovascular system using magnetic resonance imaging

Across a lifetime, the cardiovascular system must adapt to a great range of demands from the body. The individual changes in the cardiovascular system that occur in response to loading conditions are influenced by genetic susceptibility, and the pattern and extent of these changes have prognostic value. Brachial blood pressure (BP) and left ventricular ejection fraction (LVEF) are important biomarkers that capture this response, and their measurements are made at high resolution. Relatively, clinical analysis is crude, and may result in lost information and the introduction of noise. Digital information storage enables efficient extraction of information from a dataset, and this strategy may provide more precise and deeper measures to breakdown current phenotypes into their component parts. The aim of this thesis was to develop automated analysis of cardiovascular magnetic resonance (CMR) imaging for more detailed phenotyping, and apply these techniques for new biological insights into the cardiovascular response to different loading conditions. I therefore tested the feasibility and clinical utility of computational approaches for image and waveform analysis, recruiting and acquiring additional patient cohorts where necessary, and then applied these approaches prospectively to participants before and after six-months of exercise training for a first-time marathon. First, a multi-centre, multi-vendor, multi-field strength, multi-disease CMR resource of 110 patients undergoing repeat imaging in a short time-frame was assembled. The resource was used to assess whether automated analysis of LV structure and function is feasible on real-world data, and if it can improve upon human precision. This showed that clinicians can be confident in detecting a 9% change in EF or a 20g change in LV mass. This will be difficult to improve by clinicians because the greatest source of human error was attributable to the observer rather than modifiable factors. Having understood these errors, a convolutional neural network was trained on separate multi-centre data for automated analysis and was successfully generalizable to the real-world CMR data. Precision was similar to human analysis, and performance was 186 times faster. This real-world benchmarking resource has been made freely available (thevolumesresource.com). Precise automated segmentations were then used as a platform to delve further into the LV phenotype. Global LVEFs measured from CMR imaging in 116 patients with severe aortic stenosis were broken down into ~10 million regional measurements of structure and function, represented by computational three-dimensional LV models for each individual. A cardiac atlas approach was used to compile, label, segment and represent these data. Models were compared with healthy matched controls, and co-registered with follow-up one year after aortic valve replacement (AVR). This showed that there is a tendency to asymmetric septal hypertrophy in all patients with severe aortic stenosis (AS), rather than a characteristic specific to predisposed patients. This response to AS was more unfavourable in males than females (associated with higher NT-proBNP, and lower blood pressure), but was more modifiable with AVR. This was not detected using conventional analysis. Because cardiac function is coupled with the vasculature, a novel integrated assessment of the cardiovascular system was developed. Wave intensity theory was used to combine central blood pressure and CMR aortic blood flow-velocity waveforms to represent the interaction of the heart with the vessels in terms of traveling energy waves. This was performed and then validated in 206 individuals (the largest cohort to date), demonstrating inefficient ventriculo-arterial coupling in female sex and healthy ageing. CMR imaging was performed in 236 individuals before training for a first-time marathon and 138 individuals were followed-up after marathon completion. After training, systolic/diastolic blood pressure reduced by 4/3mmHg, descending aortic stiffness decreased by 16%, and ventriculo-arterial coupling improved by 14%. LV mass increased slightly, with a tendency to more symmetrical hypertrophy. The reduction in aortic stiffness was equivalent to a 4-year reduction in estimated biological aortic age, and the benefit was greater in older, male, and slower individuals. In conclusion, this thesis demonstrates that automating analysis of clinical cardiovascular phenotypes is precise with significant time-saving. Complex data that is usually discarded can be used efficiently to identify new biology. Deeper phenotypes developed in this work inform risk reduction behaviour in healthy individuals, and demonstrably deliver a more sensitive marker of LV remodelling, potentially enhancing risk prediction in severe aortic stenosis