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Diagnostic Error
Published in Pat Croskerry, Karen S. Cosby, Mark L. Graber, Hardeep Singh, Diagnosis, 2017
The physical examination begins with the measurement of vital signs that reflect the overall health of the patient (blood pressure, heart rate, respiratory rate, temperature, and pulse oximetry). Triage systems rely almost exclusively on these basic measurements. These time-honored measurements are subject to significant interobserver variability and poor reproducibility [37–39]. Blood pressure measurements by primary care providers in Geneva overestimated the incidence of hypertension in 32% of normal volunteers [39]. In an ED population, independent measures of respiratory rates differed by 35%, and heart rates by 10%–15% [37]. Across different practice settings, a range of providers using different measuring devices at different moments in time may show significant variation. However, clinicians intuitively know this, and are pretty good at discriminating between normal or abnormal, or at least agreeing on which measurements require intervention [37]. Experienced clinicians tend to rely more on clinical context, serial measurements, trends over time, and combinations of findings (e.g., blood pressure and heart rate) to assess how meaningful any single value is.
Clinical Reasoning and Diagnostic Errors
Published in Paul Cerrato, John Halamka, Reinventing Clinical Decision Support, 2020
At this early stage in the diagnostic process, the list can be very long or quite short depending on how complex or obvious the patient’s presentation is. The clinician will also perform a physical examination, including routine vital signs such as blood pressure, respirations, pulses, body temperature, and weight. Once all these data points are gathered, lab tests, imaging studies, or various invasive procedures may be ordered if the diagnosis has not yet become obvious.
Leung-Malik Features and Adaboost Perform Classification of Alzheimer’s Disease Stages
Published in IETE Journal of Research, 2022
Shaik Basheera, M. Satya Sai Ram
Alzheimer’s disease is now the sixth largest cause of mortality in the United States, according to the most recent health survey. The most common kind of dementia that is associated with ageing is Alzheimer’s disease (AD). The first indication of Alzheimer’s disease is memory loss, which may disrupt a person’s routine. The hippocampus becomes smaller as Alzheimer’s disease progresses, the ventricular system enlarges and produces more cerebral spinal fluid (CSF), the volume of white matter (WM) in the brain decreases, and the volume of grey matter (GM) in the brain reduces. The Apolipoprotein e4 gene in the family and the individual’s own genetic make-up have a role in this (APOE4). AD is a progressive, fatal neurodegenerative disease characterized by the progressive death of brain cells and the loss of their connections to other neurons. The most important step is to get a diagnosis of Alzheimer’s disease at an early stage. The medical history of the patient, a neurological examination, a physical examination, and an assessment of the person’s memory and thinking using a unique questionnaire that was particularly devised by the physicians all contribute to the process of diagnosing Alzheimer’s disease in a patient. In addition to that, they use a rating for dementia and the Mini-Mental State Examination (MMSE). The National Institute on Aging and the Alzheimer’s Association were the first organizations to define clinical criteria for the diagnosis of Alzheimer’s disease. In addition to the clinical methods described above, a variety of imaging modalities are used to arrive at a diagnosis of Alzheimer’s disease stage.
A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis
Published in Cogent Engineering, 2021
Saeedreza Arab, Kianaz Rezaee, Ghazaleh Moghaddam
A prototype expert system in gastroenterology was developed in Itay. This system is capable of assisting physicians in the diagnosis of gastrointestinal disorders. The system consists of approximately 400 rules representing deductive knowledge. The prototype essentially works by doctor keys in the patient symptom data, general information, medical history and physical examination through a user interface. The system responds by identifying the organ and the disease by suggesting clinical examinations. The domain comprises of chronic pathologies of the digestive tract. Consideration has been given to the greater risk illnesses and those most frequently experienced in everyday practice. Knowledge in the scheme is structured as a search-tree of feasible diagnostics, with more general concepts at the top level and more particular concepts at the bottom level. Starting with the initial kernel of the system, errors in the regulations and the search strategy have been recognized by carrying out a sequence of tests on a big amount of already resolved instances related to illnesses in the implemented sub-domain (Terribile et al., 1991).
Quantifying vehicle control from physiology in type 1 diabetes
Published in Traffic Injury Prevention, 2019
Pranamesh Chakraborty, Jennifer Merickel, Viraj Shah, Anuj Sharma, Chinmay Hegde, Cyrus Desouza, Andjela Drincic, Pujitha Gunaratne, Matthew Rizzo
All participants completed a physical examination and a full medical history at induction. Medical conditions and medications that presented a significant confounding effect on driver behavior or are known to worsen diabetes were excluded. For examples, patients with kidney failure were excluded because they are not able to process insulin and therefore have a high propensity for hypoglycemia (Sandholm et al. 2012). Excluded medical conditions included neuropathy, pulmonary disease, major psychiatric disorders, neurologic conditions, vestibular disease, sleep disorders, current substance abuse, visual field defects, and thyroid or kidney diseases. Excluded medications were narcotics, anxiolytics, anticonvulsants, sedating antihistamines, and major psychoactive medication. All participants had safe vision for driving as per the Nebraska Department of Motor Vehicle (DMV) standards (binocular, corrected or uncorrected <20/40). DM drivers had received a diagnosis of T1D, used insulin at least daily, and had self-reported at least bi-weekly hypoglycemic episodes.