Joyce C. Ho

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The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to phenotypes, or medical concepts, that(More)
The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to medical concepts that clinical researchers(More)
Sepsis and septic shock are potentially fatal complications that frequently occur in intensive care unit patients. The ability to predict which patients are at risk for sepsis and septic shock is therefore crucial to limiting the effects of these complications. Potential indications for sepsis risk are scattered in a wide range of clinical measurements,(More)
Sepsis and septic shock are common and potentially fatal conditions that often occur in intensive care unit (ICU) patients. Early prediction of patients at risk for septic shock is therefore crucial to minimizing the effects of these complications. Potential indications for septic shock risk span a wide range of measurements, including physiological data(More)
Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. The progression and severity of MS varies by individual, but it is generally a disabling disease. Although medications have been developed to slow the disease progression and help manage symptoms, MS research has yet to result in a cure. Early diagnosis and(More)
ICU patients are vulnerable to in-ICU morbidities and mortality, making accurate systems for identifying at-risk patients a necessity for improving clinical care. Here, we present an improved model for predicting in-hospital mortality using data collected from the first 48 hours of a pa-tient's ICU stay. We generated predictive features for each patient(More)
In many healthcare settings, intuitive decision rules for risk stratification can help effective hospital resource allocation. This paper introduces a novel variant of decision tree algorithms that produces a chain of decisions, not a general tree. Our algorithm, α-Carving Decision Chain (ACDC), sequentially carves out " pure " subsets of the majority class(More)
Cardiac arrest is a deadly condition caused by a sudden failure of the heart with an in-hospital mortality rate of ∼ 80%. Therefore, the ability to accurately estimate patients at high risk of cardiac arrest is crucial for improving the survival rate. Existing research generally fails to utilize a patient's temporal dynamics. In this paper, we present two(More)