Learning Better Clinical Risk Models.

Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models. As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings. Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1

Activity-based protein profiling in drug-discovery

In the last decades, activity-based protein profiling (ABPP) has emerged as a powerful chemical tool that may aid the ever-challenging drug discovery process. In this thesis ABPP is explored as a versatile tool in drug discovery and cell biology.ABPP enabled rapid assessment of clinical samples from patients suffering from cardiac ischemia, thereby giving insight into the serine hydrolase activity profile of these patients. The identification of molecular role players may lead to the discovery of novel therapeutic targets or biomarkers. In addition, ABPP can provide insight in a drug’s interaction landscape, by enabling target engagement studies and inhibitor selectivity profiling. This was demonstrated by the identification of multiple off targets of the experimental drug BIA 10-2474 that caused severe neurological symptoms in a phase I clinical trial. In zebrafish larvae, the ABPP methodology enabled in vivo selectivity profiling and in addition served as a powerful tool to map the kinase and serine hydrolase landscape throughout embryonic development. Lastly, combining ABPP with other biochemical techniques including CRISPR/Cas9 technology and lipidomics, can provide new insights in cellular biology, which was showcased by the identification of ABHD6 as a diacylglycerol-lipase in a cellular model of neuronal differentiation.Molecular Physiolog