Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data

Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases

Shared Value in Emerging Markets: How Multinational Corporations Are Redefining Business Strategies to Reach Poor or Vulnerable Populations

This report illuminates the enormous opportunities in emerging markets for companies to drive competitive advantage and sustainable impact at scale. It identifies how over 30 companies across multiple sectors and geographies design and measure business strategies that also improve the lives of underserved individuals

Serum calcium and risk of migraine : a Mendelian randomization study

Migraine affects similar to 14% of the world's population, though not all predisposing causal risk factors are known. We used electronic health records, genetic co-heritability analysis, and a two-sampleMendelian Randomization (MR) design to determine if elevated serum calcium levels were associated with risk of migraine headache. Co-morbidity was evaluated using electronic health records obtained from the PennOmics database comprising>1 million patient entries. Genetic co-heritability and causality via MR was assessed using data from the International Headache Consortium (23,285 cases, 95,425 controls) and circulating serum calcium levels (39,400 subjects). We observed co-occurrence of migraine and hypercalcaemia ICD-9 diagnoses (OR = 1.58, P = 4 x 10-(13)), even after inclusion of additional risk factors for migraine (OR = 1.23, P = 2 x 10 -(3)). Second, we observed co-heritability (r(g) =0.191, P = 0.03) between serum calcium and migraine headache, indicating that these traits have a genetic basis in common. Finally, we found that elevation of serum calcium levels by 1 mg/dl resulting from our genetic score was associated with an increase in risk of migraine (OR = 1.80, 95% CI: 1.31-2.46, P = 2.5 x 10 -(4)), evidence supporting a causal hypothesis. We also present multiple MR sensitivity analyses in support of this central finding. Our results provide evidence that hypercalcaemia is comorbid with migraine headache diagnoses, and that genetically elevated serum calcium over lifetime appears to increase risk for migraine. Further studies will be required to understand the biologicalmechanism, pathways, and clinical implication for riskmanagement.Peer reviewe

When darkness falls at mid-day: Young patients’ perceptions and meanings of chronic illness and their implications for medical care

Background: This study illustrates the perceptions and meanings that patients who have had the onset of certain chronic diseases at young adulthood ascribe to their condition of chronic illness. The paper further examines the implications of such perceptions and construction for medical care.Design: Qualitative and ethnography.Setting: Outpatient chronically ill patients were recruited from the Korle Bu Teaching Hospital, Accra Ghana. Patients were followed up and studied in-depth in their homes.Participants: Purposive sample of 24 consenting patients diagnosed of cancer, renal disease and stroke.Methods: Anthropological data collection techniques mainly in-depth interviews, narratives, conversation and observations were used. Issues explored included patients’ perceptions, causal explanations, meanings ascribed to diagnosis, and search for treatment and cure.Results: Young adults had very low knowledge of chronic illnesses and did not consider themselves at risk to chronic diseases. The search for diagnosis, upon the presentation of symptoms, was long and winding. Families of young patients were more likely to make future search for “spiritual diagnoses” than elderly patients and their families. Keeping silent and maintaining secrecy about diagnosis are important ways by which young adult patients cope with their condition. Irrespective of the prognosis, young patients nurture a strong hope of cure.Conclusion: Young adults diagnosed of certain chronic illnesses ascribe supernatural interpretations to their disease condition. These determine their attitude to their condition as well as health seeking behaviours adopted by them and their families. Responses interfered with their biomedical care and thus have implications for health promotion and healthcare planning and policy.Keywords: young adulthood, chronic illness, causal explanation

Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review.

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis