Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: a systematic review
Importance Various model reporting guidelines have been proposed to ensure clinical
prediction models are reliable and fair. However, no consensus exists about which model …
prediction models are reliable and fair. However, no consensus exists about which model …
[HTML][HTML] Use characteristics and triage acuity of a digital symptom checker in a large integrated health system: population-based descriptive study
Background Pressure on the US health care system has been increasing due to a
combination of aging populations, rising health care expenditures, and most recently, the …
combination of aging populations, rising health care expenditures, and most recently, the …
Medalign: A clinician-generated dataset for instruction following with electronic medical records
The ability of large language models (LLMs) to follow natural language instructions with
human-level fluency suggests many opportunities in healthcare to reduce administrative …
human-level fluency suggests many opportunities in healthcare to reduce administrative …
A survey of extant organizational and computational setups for deploying predictive models in health systems
Objective Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now
feasible for many health systems, yet little is known about effective strategies of system …
feasible for many health systems, yet little is known about effective strategies of system …
[HTML][HTML] Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare
Background Diagnostic codes are commonly used as inputs for clinical prediction models, to
create labels for prediction tasks, and to identify cohorts for multicenter network studies …
create labels for prediction tasks, and to identify cohorts for multicenter network studies …
[HTML][HTML] User-centred design for machine learning in health care: a case study from care management
MG Seneviratne, RC Li, M Schreier… - BMJ Health & Care …, 2022 - ncbi.nlm.nih.gov
Objectives Few machine learning (ML) models are successfully deployed in clinical practice.
One of the common pitfalls across the field is inappropriate problem formulation: designing …
One of the common pitfalls across the field is inappropriate problem formulation: designing …
Estimate the hidden deployment cost of predictive models to improve patient care
Although examples of algorithms designed to improve healthcare delivery abound, for many,
clinical integration will not be achieved. The deployment cost of machine learning models is …
clinical integration will not be achieved. The deployment cost of machine learning models is …
Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks
Objective Development of electronic health records (EHR)-based machine learning models
for pediatric inpatients is challenged by limited training data. Self-supervised learning using …
for pediatric inpatients is challenged by limited training data. Self-supervised learning using …
A natural language processing model to identify confidential content in adolescent clinical notes
N Rabbani, M Bedgood, C Brown… - Applied Clinical …, 2023 - thieme-connect.com
Background The 21st Century Cures Act mandates the immediate, electronic release of
health information to patients. However, in the case of adolescents, special consideration is …
health information to patients. However, in the case of adolescents, special consideration is …
Low adherence to existing model reporting guidelines by commonly used clinical prediction models
Objective To assess whether the documentation available for commonly used machine
learning models developed by an electronic health record (EHR) vendor provides …
learning models developed by an electronic health record (EHR) vendor provides …