Browsing by Subject "Family history"

Now showing 1 - 4 of 4
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    Advanced natural language processing and temporal mining for clinical discovery
    (2015-08-17) Mehrabi, Saeed; Jones, Josette F.; Palakal, Mathew J.; Chien, Stanley Yung-Ping; Liu, Xiaowen; Schmidt, C. Max
    There has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients' family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations.
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    Delay Discounting in At-Risk Preadolescents: Brain Mechanisms and Behavior
    (2021-12) Butcher, Tarah J; Oberlin, Brandon; Lapish, Christopher; Hulvershorn, Leslie
    It is well documented that adolescent substance use is associated with deficits in brain function and behavior. However, possible deficits that predate substance use initiation remain poorly characterized in preadolescents at-risk for developing substance use disorder (SUD). To characterize potential brain and behavioral differences that predate substance use, substance naïve preadolescents, ages 11–12, were recruited into three groups to complete functional magnetic resonance imaging delay discounting: (1) High-risk youth (n=35) with a family history of SUD and externalizing psychiatric disorders, (2) psychiatric controls (n=35) with no family history of SUD, but equivalent externalizing psychiatric disorders as high-risk youth, and (3) healthy controls (n=29) with no family history of SUD and minimal psychopathology. While no behavioral differences between groups were identified, there were group differences in posterior cingulate cortex (PCC) function during decision making. Specifically, the high-risk group showed stronger deactivation of the PCC than healthy controls. These results suggest that high-risk preadolescents may need to suppress activity of key nodes of the default mode network (a task negative network) to a greater extent to properly allocate attention to the task.
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    Ethanol-Associated Changes in Glutamate Reward Neurocircuitry: A Minireview of Clinical and Preclinical Genetic Findings
    (Elsevier, 2016) Bell, Richard L.; Hauser, Sheketha R.; McClintick, Jeanette; Rahman, Shafiqur; Edenberg, Howard J.; Szumlinski, Karen K.; McBride, William J.; Department of Psychiatry, IU School of Medicine
    Herein, we have reviewed the role of glutamate, the major excitatory neurotransmitter in the brain, in a number of neurochemical, -physiological, and -behavioral processes mediating the development of alcohol dependence. The findings discussed include results from both preclinical as well as neuroimaging and postmortem clinical studies. Expression levels for a number of glutamate-associated genes and/or proteins are modulated by alcohol abuse and dependence. These changes in expression include metabotropic receptors and ionotropic receptor subunits as well as different glutamate transporters. Moreover, these changes in gene expression parallel the pharmacologic manipulation of these same receptors and transporters. Some of these gene expression changes may have predated alcohol abuse and dependence because a number of glutamate-associated polymorphisms are related to a genetic predisposition to develop alcohol dependence. Other glutamate-associated polymorphisms are linked to age at the onset of alcohol-dependence and initial level of response/sensitivity to alcohol. Finally, findings of innate and/or ethanol-induced glutamate-associated gene expression differences/changes observed in a genetic animal model of alcoholism, the P rat, are summarized. Overall, the existing literature indicates that changes in glutamate receptors, transporters, enzymes, and scaffolding proteins are crucial for the development of alcohol dependence and there is a substantial genetic component to these effects. This indicates that continued research into the genetic underpinnings of these glutamate-associated effects will provide important novel molecular targets for treating alcohol abuse and dependence.
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    A Genetic Animal Model of Alcoholism for Screening Medications to Treat Addiction
    (Elsevier, 2016) Bell, Richard L.; Hauser, S.; Rodd, Z. A.; Liang, T.; Sari, Y.; McClintick, J.; Rahman, S.; Engleman, E. A.; Department of Psychiatry, IU School of Medicine
    The purpose of this review is to present up-to-date pharmacological, genetic, and behavioral findings from the alcohol-preferring P rat and summarize similar past work. Behaviorally, the focus will be on how the P rat meets criteria put forth for a valid animal model of alcoholism with a highlight on its use as an animal model of polysubstance abuse, including alcohol, nicotine, and psychostimulants. Pharmacologically and genetically, the focus will be on the neurotransmitter and neuropeptide systems that have received the most attention: cholinergic, dopaminergic, GABAergic, glutamatergic, serotonergic, noradrenergic, corticotrophin releasing hormone, opioid, and neuropeptide Y. Herein, we sought to place the P rat's behavioral and neurochemical phenotypes, and to some extent its genotype, in the context of the clinical literature. After reviewing the findings thus far, this chapter discusses future directions for expanding the use of this genetic animal model of alcoholism to identify molecular targets for treating drug addiction in general.