Comparing four methods for predicting septic shock in children hospitalized with sepsis, Johns Hopkins researchers have found a newer machine-learning approach superior to an older one as well as to two conventional methods.
Psychology researchers have demonstrated a way to finetune diagnoses of major depression and generalized anxiety disorder by analyzing freely elaborated thoughts and feelings using machine learning and natural language processing.
Clinical nutritionists won’t be left out of the medical AI revolution, as researchers are exploring use cases for augmented diet optimization, food image recognition, risk prediction and diet pattern analysis.
The system hit 88% accuracy at optimizing stimulation settings, as confirmed by brain-response patterns on neuroimaging as well as visibly observable symptom improvement in patients with Parkinson’s disease.
AI is poised to help settle an argument that’s been roiling academic psychiatry for more than a century: Are bipolar disorder and schizophrenia two distinct diagnoses—or points along a single continuum?
A novel AI-based model for clinical decision support has bested established machine-learning models at predicting how patients with type-2 diabetes mellitus will respond to various categories of therapeutic drugs.
Deep neural networks are capable of tying oncological findings from genetic testing with those from medical imaging and biopsy analysis to not only validate previously discovered connections among and between the three fields but also uncover new ones.