Context-Sensitive Correlation of Implicitly Related Data: An Episode Creation Methodology

Son, Roderick Y.; Taira, Ricky K.; Kangarloo, Hooshang; Cárdenas, Alfonso F.

doi:10.1109/titb.2008.917901

Cited by 4 publications

(3 citation statements)

References 19 publications

(21 reference statements)

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“…Health outcomes research focuses on outcomes or health care. Episode creation described as classifying medical events and related data to higher level concepts like diseases using a combinatorial optimization approach for constructing episodes demonstrated significant improvement over traditional healthcare approaches [13]. Older adults living in retirement communities wore an aceleramotor and GPS device for seven days and those who spent more than 30 minutes outdoors or in physical activity for depressive symptoms, fear of falling and selfreported functioning [14].…”

Section: Health Outcomesmentioning

confidence: 99%

Collaborations between Public Health and Computer Science: A Path Worth Pursuing

Epstein¹

2013

AJPHR

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Collaboration between public health and computer science shows much promise. The history of the two disciplines is discussed. Examples of recent efforts at collaboration are described to give a flavor of where this path may be going. These studies ranged from some showing effectiveness to others demonstrating the infancy of the collaboration at this point in time. Technology and computer science offer public health many new technological advances by making further use of the internet, enhancements in public health informatics, using computer science to accomplish clinical work gathered by physicians to name a few examples. Still other computer science research concentrations such as animation and virtual reality deserve further exploration. Pursuing this path is certainly worth taking to further advance knowledge and the mission of public health primarily through collaborative efforts taken by individuals/teams in the two disciplines. A collaboration with computer science will help bring public health to meet the 21 st century both in terms of challenges and technology.

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Section: Health Outcomesmentioning

confidence: 99%

Collaborations between Public Health and Computer Science: A Path Worth Pursuing

Epstein¹

2013

AJPHR

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“…Concept classes (eg, tumors) that can pertain to multiple-instance entities (eg, multiple physical findings of tumor mentioned within a patient image report), where each entity is uniquely identified, are not addressed. Such tracking of multiple entity instances would require more extensive NLP analysis utilizing techniques, such as coreference resolution, [21][22][23][24] which is outside the scope of this work.…”

Section: Concept Summarizationmentioning

confidence: 99%

Towards Automatic Diabetes Case Detection and ABCS Protocol Compliance Assessment

Mishra

Son

Arnzen

2012

Clinical Medicine & Research

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Objective: According to the American Diabetes Association, the implementation of the standards of care for diabetes has been suboptimal in most clinical settings. Diabetes is a disease that had a total estimated cost of $174 billion in 2007 for an estimated diabetes-affected population of 17.5 million in the United States. With the advent of electronic medical records (EMR), tools to analyze data residing in the EMR for healthcare surveillance can help reduce the burdens experienced today. This study was primarily designed to evaluate the efficacy of employing clinical natural language processing to analyze discharge summaries for evidence indicating a presence of diabetes, as well as to assess diabetes protocol compliance and high risk factors.Methods: Three sets of algorithms were developed to analyze discharge summaries for:(1) identification of diabetes, (2) protocol compliance, and (3) identification of high risk factors. The algorithms utilize a common natural language processing framework that extracts relevant discourse evidence from the medical text. Evidence utilized in one or more of the algorithms include assertion of the disease and associated findings in medical text, as well as numerical clinical measurements and prescribed medications. Results:The diabetes classifier was successful at classifying reports for the presence and absence of diabetes. Evaluated against 444 discharge summaries, the classifier's performance included macro and micro F-scores of 0.9698 and 0.9865, respectively. Furthermore, the protocol compliance and high risk factor classifiers showed promising results, with most F-measures exceeding 0.9. Conclusions:The presented approach accurately identified diabetes in medical discharge summaries and showed promise with regards to assessment of protocol compliance and high risk factors. Utilizing free-text analytic techniques on medical text can complement clinical-public health decision support by identifying cases and high risk factors.

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“…Further details about the coreference resolution component can be seen in our earlier published article (39). phrases.…”

Section: Information Organizationmentioning

confidence: 99%

Problem-centric Organization and Visualization of Patient Imaging and Clinical Data

Bashyam¹,

Hsu²,

Watt³

et al. 2009

RadioGraphics

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A patient's electronic medical record contains a large amount of unstructured textual information. As patient records become increasingly dense owing to an aging population and increased occurrence of chronic diseases, a tool is needed to help organize and navigate patient data in a way that facilitates a clinician's ability to understand this information and that improves efficiency. A system has been developed for physicians that summarizes clinical information from a patient record. This system provides a gestalt view of the patient's record by organizing information about each disease along four dimensions (axes): time (eg, disease progression over time), space (eg, tumor in left frontal lobe), existence (eg, certainty of existence of a finding), and causality (eg, response to treatment). A display is generated from information provided by radiology reports and discharge summaries. Natural language processing is used to identify clinical abnormalities (problems, symptoms, findings) from these reports as well as associated properties and relationships. This information is presented in an integrated format that organizes extracted findings into a problem list, depicts the information on a timeline grid, and provides direct access to relevant reports and images. The goal of this system is to improve the structure of clinical information and its presentation to the physician, thereby simplifying the information retrieval and knowledge discovery necessary to bridge the gap between acquiring raw data and making an informed diagnosis.

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Context-Sensitive Correlation of Implicitly Related Data: An Episode Creation Methodology

Cited by 4 publications

References 19 publications

Collaborations between Public Health and Computer Science: A Path Worth Pursuing

Collaborations between Public Health and Computer Science: A Path Worth Pursuing

Towards Automatic Diabetes Case Detection and ABCS Protocol Compliance Assessment

Problem-centric Organization and Visualization of Patient Imaging and Clinical Data

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