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Classification of Seizures and Epilepsy
Published in Stanley R. Resor, Henn Kutt, The Medical Treatment of Epilepsy, 2020
Classifications of symptoms and of diseases should serve a practical purpose; they should not be academic exercises or ends in themselves. In general, medical classification schemes should facilitate communication among professionals and between physician and patient while aiding diagnosis and improving therapy. Thus, classification of seizures and epilepsy is necessary for developing optimal therapeutic and investigative strategies. Accurate classification of seizures is critical to a physiological understanding of epileptic phenomena; to rational prescribing practices that base selection of antiepileptic drugs (AEDs), in part, on accurate diagnosis of seizure type; and to scientific investigations that require delineation of clinical and electroencephalographic (EEG) phenotypes. Furthermore, meaningful classification has improved recognition that different seizures and forms of epilepsy have different natural histories with different requirements for when and for how long to treat.
The Temporal Body
Published in Roger Cooter, John Pickstone, Medicine in the Twentieth Century, 2020
By the late nineteenth century, the practice of medicine, particularly in hospitals, was primarily concerned with the identification and treatment of pathological lesions. As such, clinical medicine was little concerned with the age of the body beyond the likelihood of involutionary changes aiding the progress of the disease. But the identification of diseases characteristic of particular age groups enabled a new medical classification that differed from the atemporal nosography of organ system or pathological process. Life stage could join the various symptom indicators in the differential diagnosis: as the type of pain might distinguish respiratory from cardiac pathology, so too might the age of the patient. Thus it became possible to rearrange the medical typology of disease, previously based on underlying spatial lesion, into a temporal order in which, because of their respective probabilities, diseases could be assigned to children, adults and the old.
Coaching and sport for persons with disabilities
Published in Michael Horvat, Ronald V. Croce, Caterina Pesce, Ashley Fallaize, Developmental and Adapted Physical Education, 2019
Michael Horvat, Ronald V. Croce, Caterina Pesce, Ashley Fallaize
Medical classification systems generally are designed to determine the degree to which a medical condition that causes disability (e.g., spinal cord injury) is likely to impact the individual’s potential to perform. An example of a medical classification system is one that classifies athletes who are blind or visually impaired (Table 24.1). Classification systems variously can be complex, and listing the specifics of classification systems across the sport for persons with disabilities spectrum transcends the scope of this chapter. For further classification information, contact the appropriate sports organization or association.
Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Nasrin Amini, Mahdi Mahdavi, Hadi Choubdar, Atefeh Abedini, Ahmad Shalbaf, Reza Lashgari
The seven traditional classifiers algorithms used in the current study, RF, SVM, LR, KNN, ANN, bagging, and boosting are popular and have been among the most successful classification methods in the previous studies in various fields (Shalbaf et al. 2020; Valizadeh et al. 2021). The advantages of these widely used methods, such as high performance, great classification rate, and high-speed calculation, make them applicable for medical classification problems. Specifically, the RF classifier has several superiorities compared to other statistical classifiers, namely the ability to model complex interactions, the participation of different predictors, higher generalization due to the inherent randomicity in the feature and sample selection procedure in each iteration, and interpretability.
Determinants of racial and ethnic disparities in utilization of hospital-based care for asthma among Medi-Cal children in Los Angeles
Published in Journal of Asthma, 2022
Yonsu Kim, Matthew Pirritano, Katrina Miller Parrish
Then, we selected 69 118 children (ages 0–18) whose race/ethnicity, gender, age, and medication use data were identified. The utilization data consist of date of service, type of service (outpatient, inpatient, and ED visits), provider, PCP indicator, severity of illness (SOI: 1 through 4), CCS disease category, and member’s Independent Provider Association (IPA), all of which are associated with de-identified demographics such as age, race/ethnicity, gender, and geographic information. As a medical classification, SOI is assigned to the patients based on their total medical records. We also retrieved the individual’s medication record from our pharmacy database (2014-2018) and calculated the asthma medication ratio (AMR: the ratio of controller medications relative to controller and reliever medications) over the study period at the individual level.
Prevalence, types, and combinations of multiple problems among recipients of work disability benefits
Published in Disability and Rehabilitation, 2022
Kor A. Brongers, Tialda Hoekstra, Pepijn D. D. M. Roelofs, Sandra Brouwer
Data on diagnoses were retrieved from the register data provided by UWV. When clients apply for disability benefits, insurance physicians use the Dutch Classification of Occupational Health and Social Insurance (CAS) to categorise diagnoses, derived from the International Statistical Classification of Disease and Related Health Problems [17] (ICD-10). The CAS is based on the International Statistical Classification of Disease and Related Health Problems (ICD-10), a medical classification list from the World Health Organization [18]. During the medical disability assessment, insurance physicians can list up to three disorders. In this study, we used only the primary diagnose, the one causing the most important limitations to being able to work according to the insurance physician. For generalisability reasons, diagnoses were clustered into four groups: somatic diseases (e.g., cardiovascular disorders and lumbar disc disorders), intellectual disabilities (e.g., mild mental retardation (IQ range 50–69)), psychiatric disorders (e.g., depressive episodes), and developmental diseases (e.g., autism spectrum disorders).