Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: a systematic review

JH Lu, A Callahan, BS Patel, KE Morse… - JAMA network …, 2022 - jamanetwork.com
Importance Various model reporting guidelines have been proposed to ensure clinical
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

KE Morse, NP Ostberg, VG Jones, AS Chan - Journal of medical Internet …, 2020 - jmir.org
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 …

Medalign: A clinician-generated dataset for instruction following with electronic medical records

SL Fleming, A Lozano, WJ Haberkorn, JA Jindal… - arXiv preprint arXiv …, 2023 - arxiv.org
The ability of large language models (LLMs) to follow natural language instructions with
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

S Kashyap, KE Morse, B Patel… - Journal of the American …, 2021 - academic.oup.com
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 …

[HTML][HTML] Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare

LL Guo, KE Morse, C Aftandilian, E Steinberg… - BMC Medical Informatics …, 2024 - Springer
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 …

[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 …

Estimate the hidden deployment cost of predictive models to improve patient care

KE Morse, SC Bagley, NH Shah - Nature medicine, 2020 - nature.com
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 …

Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks

J Lemmon, LL Guo, E Steinberg… - Journal of the …, 2023 - academic.oup.com
Objective Development of electronic health records (EHR)-based machine learning models
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 …

Low adherence to existing model reporting guidelines by commonly used clinical prediction models

JH Lu, A Callahan, BS Patel, KE Morse, D Dash… - MedRXiv, 2021 - medrxiv.org
Objective To assess whether the documentation available for commonly used machine
learning models developed by an electronic health record (EHR) vendor provides …