Machine Learning at the Clinical Bedside—The Ghost in the Machine

Abstract

settings, in which lack of resources means that patients are given an appointment up to 3 months after the initial script. Too often, both clinicians and patients may settle for better without an understanding of what best looks like. Dose optimization requires several visits, clinician time, and patient education. This initial investment might lead to more patients achieving remission over just improvement and might mitigate our current rates of adherence and persistence, which are poor. 7 Ching et al 1 have done the pediatric field a great service by asking the right questions and by neatly outlining the relevant studies on formulation, maximum dose range used, and mean dose. Clinical trials can do a better job of describing their titration process and outlining the rationale for dosing limits in their manuscripts. As we move toward personalized or precision approaches to ADHD treatment, identifying the factors associated with the variability in dose response effects is a crucial next step.

Predictability and Stability Testing to Assess Clinical Decision Instrument Performance for Children After Blunt Torso Trauma

“…However, these complex machine-learning models lack the interpretability to integrate judgment, thus not allowing review nor the recognition of bias, which may build mistrust in the user. 30 Therefore, we use interpretable models with visual representation to allow stability analysis and ensure the integration of clinical judgment within the CDI. 28…”

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

“…Here lies a key challenge for machine learning tools, especially for techniques like neural networks, which provide more "black box" predictions. 73 The integration of such black box predictions in clinical decision making is problematic because it means a departure from the paradigm of evidence-based medicine. 74 Additionally, shared decision making between the patient and physician also requires that decisions supported by machine-learning tools can also be explained.…”

“…74 Additionally, shared decision making between the patient and physician also requires that decisions supported by machine-learning tools can also be explained. 67,73 Therefore, explicability for both the physician and the patient is likely a requirement for meaningful contributions to the decision process. Ongoing efforts to improve the explicability of complex machine-learning models are, therefore, crucial to support their clinical acceptance and implementation.…”

“…In addition to being easy to use, the advice of the model should be explicable by the clinician. Here lies a key challenge for machine learning tools, especially for techniques like neural networks, which provide more “black box” predictions . The integration of such black box predictions in clinical decision making is problematic because it means a departure from the paradigm of evidence‐based medicine .…”

“…The integration of such black box predictions in clinical decision making is problematic because it means a departure from the paradigm of evidence‐based medicine . Additionally, shared decision making between the patient and physician also requires that decisions supported by machine‐learning tools can also be explained . Therefore, explicability for both the physician and the patient is likely a requirement for meaningful contributions to the decision process.…”