Clinical Needs Assessment of a Machine Learning-Based Asthma Management Tool: User-Centered Design Approach.

Background: Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages.

Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions.

The integration of Quality Management System (QMS) principles into the life cycle of development, deployment, and utilization of machine learning (ML) and artificial intelligence (AI) technologies within healthcare settings holds the potential to close the AI translation gap by establishing a robust framework that accelerates the safe, ethical, and effective delivery of AI/ML in day-to-day patient care. Healthcare organizations (HCOs) can implement these principles effectively by embracing an enterprise QMS analogous to those in regulated industries. By establishing a QMS explicitly tailored to health AI technologies, HCOs can comply with evolving regulations and minimize redundancy and rework while aligning their internal governance practices with their steadfast commitment to scientific rigor and medical excellence.

A Technical Performance Study and Proposed Systematic and Comprehensive Evaluation of an ML-based CDS Solution for Pediatric Asthma.

Achieving optimal care for pediatric asthma patients depends on giving clinicians efficient access to pertinent patient information. Unfortunately, adherence to guidelines or best practices has shown to be challenging, as relevant information is often scattered throughout the patient record in both structured data and unstructured clinical notes. Furthermore, in the absence of supporting tools, the onus of consolidating this information generally falls upon the clinician.

Assessment of Proteomic Measures Across Serious Psychiatric Illness.

The diagnoses of serious psychiatric illnesses, such as schizophrenia, schizoaffective disorder, and bipolar disorder, rely on the subjective recall and interpretation of often overlapping symptoms, and are not based on the objective pathophysiology of the illnesses. The subjectivity of symptom reporting and interpretation contributes to the delay of accurate diagnoses and limits effective treatment of these illnesses. Proteomics, the study of the types and quantities of proteins an organism produces, may offer an objective biological approach to psychiatric diagnosis.

Attenuation of high sweet solution preference by mood stabilizers: a possible mouse model for the increased reward-seeking domain of mania.

The lack of appropriate animal models for bipolar disorder (BPD) is a major factor hindering the research of its pathophysiology and the development of new drug treatments. In line with the notion that BPD might represent a heterogeneous group of disorders, it was suggested that models for specific domains of BPD should be developed and then integrated. The present study tested sweet solution preference as a rodent model for increased reward seeking, a central component of manic behavior and a possible endophenotype of the disorder.