Deepr: A Convolutional Net for Medical Records

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space

Machine learning in healthcare : an investigation into model stability

Current machine learning algorithms, when directly applied to medical data, often fail to provide a good understanding of prognosis. This study provides three pathways to make predictive models stable and usable for healthcare. When tested on heart failure and diabetes patients from a local hospital, this study demonstrated 20% improvement over existing methods.<br /

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Discovery of Type 2 Diabetes Trajectories from Electronic Health Records

University of Minnesota Ph.D. dissertation. September 2020. Major: Health Informatics. Advisor: Gyorgy Simon. 1 computer file (PDF); xiii, 110 pages.Type 2 diabetes (T2D) is one of the fastest growing public health concerns in the United States. There were 30.3 million patients (9.4% of the US populations) suffering from diabetes in 2015. Diabetes, which is the seventh leading cause of death in the United States, is known to be a non-reversible (incurable) chronic disease, leading to severe complications, including chronic kidney disease, amputation, blindness, and various cardiac and vascular diseases. Early identification of patients at high risk is regarded as the most effective clinical tool to prevent or delay the development of diabetes, allowing patients to change their life style or to receive medication earlier. In turn, these interventions can help decrease the risk of diabetes by 30-60%. Many studies have been conducted aiming at the early identification of patients at high risk in the clinical settings. These studies typically only consider the patient's current state at the time of the assessment and do not fully utilize all available information such as patient's medical history. Past history is important. It has been shown that laboratory results and vital signs can differ between diabetic and non-diabetic patients as many as 15-20 years before the onset of diabetes. We have also shown in our study that the order in which patients develop diabetes-related comorbidities is predictive of their diabetes risk even after adjusting for the severity of the comorbidities. In this thesis, we develop multiple novel methods to discover T2D trajectories from Electronic Health Records (EHR). We define trajectory as an order of in which diseases developed. We aim to discover typical and atypical trajectories where typical trajectories represent predominant patterns of progressions and atypical trajectories refer to the rest of the trajectories. Revealing trajectories can allow us to divide patients into subpopulations that can uncover the underlying etiology of diabetes. More importantly, by assessing the risk correctly and by a better understanding of the heterogeneity of diabetes, we can provide better care. Since data collected from EHR poses several challenges to directly identify trajectories from EHR data, we devise four specific studies to address the challenges: First, we propose a new knowledge-driven representation for clinical data mining, second, we demonstrate a method for estimating the onset time of slow-onset diseases from intermittently observable laboratory results in the specific context of T2D, third, we present a method to infer trajectories, the sequence of comorbidities potentially leading up to a particular disease of interest, and finally, we propose a novel method to discover multiple trajectories from EHR data. The patterns we discovered from above four studies address a clinical issue, are clinically verifiable and are amenable to deployment in practice to improve the quality of individual patient care towards promoting public health in the United States