Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

CLA Navarro, JAA Damen, T Takada, SWJ Nijman… - bmj, 2021 - bmj.com
Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …

Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models

B Saravi, F Hassel, S Ülkümen, A Zink… - Journal of Personalized …, 2022 - mdpi.com
Healthcare systems worldwide generate vast amounts of data from many different sources.
Although of high complexity for a human being, it is essential to determine the patterns and …

Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens

M Jacobs, J He, M F. Pradier, B Lam, AC Ahn… - Proceedings of the …, 2021 - dl.acm.org
Major depressive disorder is a debilitating disease affecting 264 million people worldwide.
While many antidepressant medications are available, few clinical guidelines support …

[HTML][HTML] Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges

XJ Cao, XQ Liu - World Journal of Psychiatry, 2022 - ncbi.nlm.nih.gov
Artificial intelligence-based technologies are gradually being applied to psych-iatric
research and practice. This paper reviews the primary literature concerning artificial …

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

A Brnabic, LM Hess - BMC medical informatics and decision making, 2021 - Springer
Background Machine learning is a broad term encompassing a number of methods that
allow the investigator to learn from the data. These methods may permit large real-world …

Artificial intelligence in neurosurgery: A state-of-the-art review from past to future

JA Tangsrivimol, E Schonfeld, M Zhang, A Veeravagu… - Diagnostics, 2023 - mdpi.com
In recent years, there has been a significant surge in discussions surrounding artificial
intelligence (AI), along with a corresponding increase in its practical applications in various …

Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients …

JHF Oosterhoff, ABMC Savelberg, AV Karhade… - European Journal of …, 2022 - Springer
Purpose Preoperative prediction of mortality in femoral neck fracture patients aged 65 years
or above may be valuable in the treatment decision-making. A preoperative clinical …

Predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients

AV Karhade, TD Cha, HA Fogel, SH Hershman… - The Spine Journal, 2020 - Elsevier
IMPORTANCE Preoperative determination of the potential for postoperative opioid
dependence in previously naïve patients undergoing elective spine surgery may facilitate …

Assessment of probable opioid use disorder using electronic health record documentation

SA Palumbo, KM Adamson, S Krishnamurthy… - JAMA network …, 2020 - jamanetwork.com
Importance Electronic health records are a potentially valuable source of information for
identifying patients with opioid use disorder (OUD). Objective To evaluate whether proxy …

Machine learning for the orthopaedic surgeon: uses and limitations

D Alsoof, CL McDonald, EO Kuris, AH Daniels - JBJS, 2022 - journals.lww.com
Machine Learning for the Orthopaedic Surgeon: Uses and Limit... : JBJS Machine Learning for
the Orthopaedic Surgeon: Uses and Limitations : JBJS JBJS Journal Case Connector Reviews …