Uncertainty In Disorders Research Articles

Artificial intelligence and machine learning in disorders of consciousness

Purpose of review As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients’ response to novel therapeutic interventions. Recent findings Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation). Summary Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques.

Does Patient-centered Care Change Genital Surgery Decisions? The Strategic Use of Clinical Uncertainty in Disorders of Sex Development Clinics.

Genital surgery in children with ambiguous or atypical genitalia has been marred by controversies about the appropriateness and timing of surgery, generating clinical uncertainty about decision making. Since 2006, medical experts and patient advocates have argued for putting the child's needs central as patient-centered care. Based on audio recordings of 31 parent-clinician interactions in three clinics of disorders of sex development, we analyze how parents and clinicians decide on genital surgery. We find that clinicians and parents aim for parent-centered rather than infant-centered care. Parents receive ambivalent messages about surgery: while clinicians express caution, they also present the surgery as beneficial. We examine how parents and clinicians reach agreement about surgery-differentiating parents who push strongly for surgery from parents who do not express any preconceived preferences about surgery and parents who resist surgery. We conclude that clinicians use clinical uncertainty about surgery strategically to direct parents toward perceived clinically appropriate decisions.

The Sources of Uncertainty in Disorders of Consciousness

This article considers recent developments in neurotechnology and their potential to either mitigate or exacerbate the uncertainty of diagnosis and prognosis of DoCs, as well as the potential impact of replacing the diagnosis of vegetative state with unresponsive wakefulness syndrome. The main reasons for abolishing the term “vegetative state” are (1) the unacceptably high misclassification rate of patients with covert awareness, (2) the undesirable prescriptive connotations of the term, and (3) the unwarranted prognostic connotations. While improving prognosis and diagnosis may help surrogates as they face decisions concerning treatment, more accurate diagnosis and prognosis will not by themselves provide decisive answers. Significant diagnostic and prognostic uncertainties are likely to persist and may in fact be ineliminable. We argue that this uncertainty has to be acknowledged, communicated, and factored together with patient values, desires, and preferences in ethically complex, value-laden decisions about the treatment of brain-injured patients.