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Article ; Online: Big Data Analyses in Health and Opportunities for Research in Radiology.

Aphinyanaphongs, Yindalon

2017 Volume 21, Issue 1, Page(s) 32–36

Abstract: This article reviews examples of big data analyses in health care with a focus on radiology. We review the defining characteristics of big data, the use of natural language processing, traditional and novel data sources, and large clinical data ... ...

Abstract	This article reviews examples of big data analyses in health care with a focus on radiology. We review the defining characteristics of big data, the use of natural language processing, traditional and novel data sources, and large clinical data repositories available for research. This article aims to invoke novel research ideas through a combination of examples of analyses and domain knowledge.
MeSH term(s)	Data Interpretation, Statistical ; Humans ; Radiology/statistics & numerical data
Language	English
Publishing date	2017-02
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1360919-1
ISSN	1098-898X ; 1089-7860
ISSN (online)	1098-898X
ISSN	1089-7860
DOI	10.1055/s-0036-1597255
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Article ; Online: Marketing and US Food and Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical Devices: A Systematic Review.

Clark, Phoebe / Kim, Jayne / Aphinyanaphongs, Yindalon

JAMA network open

2023 Volume 6, Issue 7, Page(s) e2321792

Abstract: Importance: The marketing of health care devices enabled for use with artificial intelligence (AI) or machine learning (ML) is regulated in the US by the US Food and Drug Administration (FDA), which is responsible for approving and regulating medical ... ...

Abstract	Importance: The marketing of health care devices enabled for use with artificial intelligence (AI) or machine learning (ML) is regulated in the US by the US Food and Drug Administration (FDA), which is responsible for approving and regulating medical devices. Currently, there are no uniform guidelines set by the FDA to regulate AI- or ML-enabled medical devices, and discrepancies between FDA-approved indications for use and device marketing require articulation. Objective: To explore any discrepancy between marketing and 510(k) clearance of AI- or ML-enabled medical devices. Evidence review: This systematic review was a manually conducted survey of 510(k) approval summaries and accompanying marketing materials of devices approved between November 2021 and March 2022, conducted between March and November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Analysis focused on the prevalence of discrepancies between marketing and certification material for AI/ML enabled medical devices. Findings: A total of 119 FDA 510(k) clearance summaries were analyzed in tandem with their respective marketing materials. The devices were taxonomized into 3 individual categories of adherent, contentious, and discrepant devices. A total of 15 devices (12.61%) were considered discrepant, 8 devices (6.72%) were considered contentious, and 96 devices (84.03%) were consistent between marketing and FDA 510(k) clearance summaries. Most devices were from the radiological approval committees (75 devices [82.35%]), with 62 of these devices (82.67%) adherent, 3 (4.00%) contentious, and 10 (13.33%) discrepant; followed by the cardiovascular device approval committee (23 devices [19.33%]), with 19 of these devices (82.61%) considered adherent, 2 contentious (8.70%) and 2 discrepant (8.70%). The difference between these 3 categories in cardiovascular and radiological devices was statistically significant (P < .001). Conclusions and relevance: In this systematic review, low adherence rates within committees were observed most often in committees with few AI- or ML-enabled devices. and discrepancies between clearance documentation and marketing material were present in one-fifth of devices surveyed.
MeSH term(s)	United States ; Humans ; Artificial Intelligence ; United States Food and Drug Administration ; Device Approval ; Machine Learning ; Marketing ; Software
Language	English
Publishing date	2023-07-03
Publishing country	United States
Document type	Systematic Review ; Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2023.21792
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Prediction of Resuscitation for Pediatric Sepsis from Data Available at Triage.

Stella, Peter / Haines, Elizabeth / Aphinyanaphongs, Yindalon

AMIA ... Annual Symposium proceedings. AMIA Symposium

2022 Volume 2021, Page(s) 1129–1138

Abstract: Pediatric sepsis imposes a significant burden of morbidity and mortality among children. While the speedy application of existing supportive care measures can substantially improve outcomes, further improvements in delivering that care require tools that ...

Abstract	Pediatric sepsis imposes a significant burden of morbidity and mortality among children. While the speedy application of existing supportive care measures can substantially improve outcomes, further improvements in delivering that care require tools that go beyond recognizing sepsis and towards predicting its development. Machine learning techniques have great potential as predictive tools, but their application to pediatric sepsis has been stymied by several factors, particularly the relative rarity of its occurrence. We propose an alternate approach which focuses on predicting the provision of resuscitative care, rather than sepsis diagnoses or criteria themselves. Using three years of Emergency Department data from a large academic medical center, we developed a boosted tree model that predicts resuscitation within 6 hours of triage, and significantly outperforms existing rule-based sepsis alerts.
MeSH term(s)	Child ; Emergency Service, Hospital ; Humans ; Machine Learning ; Retrospective Studies ; Sepsis/diagnosis ; Sepsis/therapy ; Triage/methods
Language	English
Publishing date	2022-02-21
Publishing country	United States
Document type	Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.

Harish, Keerthi B / Price, W Nicholson / Aphinyanaphongs, Yindalon

JMIR formative research

2022 Volume 6, Issue 4, Page(s) e33970

Abstract: Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market ...

Abstract	Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and devices, a longstanding culture of open-source products in other industries, and the beginnings of machine learning-friendly regulatory pathways together allow for the development and deployment of open-source machine learning models. Such tools have distinct advantages including enhanced product integrity, customizability, and lower cost, leading to increased access. However, significant questions regarding engineering concerns about implementation infrastructure and model safety, a lack of incentives from intellectual property protection, and nebulous liability rules significantly complicate the ability to develop such open-source models. Ultimately, the reconciliation of open-source machine learning and the proprietary information-driven health care environment requires that policymakers, regulators, and health care organizations actively craft a conducive market in which innovative developers will continue to both work and collaborate.
Language	English
Publishing date	2022-04-11
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/33970
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Challenges in translating mortality risk to the point of care.

Major, Vincent J / Aphinyanaphongs, Yindalon

BMJ quality & safety

2019 Volume 28, Issue 12, Page(s) 959–962

MeSH term(s)	Feasibility Studies ; Humans ; Inpatients ; Point-of-Care Systems ; Translating
Language	English
Publishing date	2019-09-03
Publishing country	England
Document type	Editorial ; Comment
ZDB-ID	2592912-4
ISSN	2044-5423 ; 2044-5415
ISSN (online)	2044-5423
ISSN	2044-5415
DOI	10.1136/bmjqs-2019-009858
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Article ; Online: Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites.

Major, Vincent J / Aphinyanaphongs, Yindalon

BMC medical informatics and decision making

2020 Volume 20, Issue 1, Page(s) 214

Abstract: Background: Automated systems that use machine learning to estimate a patient's risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for ... ...

Abstract	Background: Automated systems that use machine learning to estimate a patient's risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for implemented systems. Methods: A prognostic study included adult admissions at a multi-site, academic medical center between 2015 and 2017. A predictive model for all-cause mortality (including initiation of hospice care) within 60 days of admission was developed. Model generalizability is assessed in temporal validation in the context of potential demographic bias. A subsequent prospective cohort study was conducted at the same sites between October 2018 and June 2019. Model performance during prospective validation was quantified with areas under the receiver operating characteristic and precision recall curves stratified by site. Prospective results include timeliness, positive predictive value, and the number of actionable predictions. Results: Three years of development data included 128,941 inpatient admissions (94,733 unique patients) across sites where patients are mostly white (61%) and female (60%) and 4.2% led to death within 60 days. A random forest model incorporating 9614 predictors produced areas under the receiver operating characteristic and precision recall curves of 87.2 (95% CI, 86.1-88.2) and 28.0 (95% CI, 25.0-31.0) in temporal validation. Performance marginally diverges within sites as the patient mix shifts from development to validation (patients of one site increases from 10 to 38%). Applied prospectively for nine months, 41,728 predictions were generated in real-time (median [IQR], 1.3 [0.9, 32] minutes). An operating criterion of 75% positive predictive value identified 104 predictions at very high risk (0.25%) where 65% (50 from 77 well-timed predictions) led to death within 60 days. Conclusion: Temporal validation demonstrates good model discrimination for 60-day mortality. Slight performance variations are observed across demographic subpopulations. The model was implemented prospectively and successfully produced meaningful estimates of risk within minutes of admission.
MeSH term(s)	Adolescent ; Adult ; Aged ; Aged, 80 and over ; Electronic Health Records ; Female ; Hospitalization ; Humans ; Machine Learning ; Male ; Middle Aged ; Mortality ; Patient Admission ; Prognosis ; Prospective Studies ; Young Adult
Keywords	covid19
Language	English
Publishing date	2020-09-07
Publishing country	England
Document type	Journal Article ; Validation Study
ISSN	1472-6947
ISSN (online)	1472-6947
DOI	10.1186/s12911-020-01235-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites

Vincent J. Major / Yindalon Aphinyanaphongs

BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-

2020 Volume 10

Abstract: Abstract Background Automated systems that use machine learning to estimate a patient’s risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for ... ...

Abstract	Abstract Background Automated systems that use machine learning to estimate a patient’s risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for implemented systems. Methods A prognostic study included adult admissions at a multi-site, academic medical center between 2015 and 2017. A predictive model for all-cause mortality (including initiation of hospice care) within 60 days of admission was developed. Model generalizability is assessed in temporal validation in the context of potential demographic bias. A subsequent prospective cohort study was conducted at the same sites between October 2018 and June 2019. Model performance during prospective validation was quantified with areas under the receiver operating characteristic and precision recall curves stratified by site. Prospective results include timeliness, positive predictive value, and the number of actionable predictions. Results Three years of development data included 128,941 inpatient admissions (94,733 unique patients) across sites where patients are mostly white (61%) and female (60%) and 4.2% led to death within 60 days. A random forest model incorporating 9614 predictors produced areas under the receiver operating characteristic and precision recall curves of 87.2 (95% CI, 86.1–88.2) and 28.0 (95% CI, 25.0–31.0) in temporal validation. Performance marginally diverges within sites as the patient mix shifts from development to validation (patients of one site increases from 10 to 38%). Applied prospectively for nine months, 41,728 predictions were generated in real-time (median [IQR], 1.3 [0.9, 32] minutes). An operating criterion of 75% positive predictive value identified 104 predictions at very high risk (0.25%) where 65% (50 from 77 well-timed predictions) led to death within 60 days. Conclusion Temporal validation demonstrates good model discrimination for 60-day mortality. Slight performance variations are observed across demographic ...
Keywords	Mortality prediction ; Palliative care ; Supportive care ; End-of-life care ; Advance directives ; Medical informatics ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	310
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Big Data Analyses in Health and Opportunities for Research in Radiology

Aphinyanaphongs, Yindalon

Seminars in Musculoskeletal Radiology

(Measuring Value, Outcomes, and Cost-Effectiveness in MSK Radiology: A Primer)

2017 Volume 21, Issue 01, Page(s) 32–36

Series title	Measuring Value, Outcomes, and Cost-Effectiveness in MSK Radiology: A Primer
Abstract	This article reviews examples of big data analyses in health care with a focus on radiology. We review the defining characteristics of big data, the use of natural language processing, traditional and novel data sources, and large clinical data repositories available for research. This article aims to invoke novel research ideas through a combination of examples of analyses and domain knowledge.
Keywords	big data ; radiology ; natural language processing
Language	English
Publishing date	2017-02-01
Publisher	Thieme Medical Publishers
Publishing place	Stuttgart ; New York
Document type	Article
ZDB-ID	1360919-1
ISSN	1098-898X ; 1089-7860
ISSN (online)	1098-898X
ISSN	1089-7860
DOI	10.1055/s-0036-1597255
Database	Thieme publisher's database

In stock of ZB MED Cologne/Königswinter

Zs.A 4887: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format.

Zaretsky, Jonah / Kim, Jeong Min / Baskharoun, Samuel / Zhao, Yunan / Austrian, Jonathan / Aphinyanaphongs, Yindalon / Gupta, Ravi / Blecker, Saul B / Feldman, Jonah

JAMA network open

2024 Volume 7, Issue 3, Page(s) e240357

Abstract: Importance: By law, patients have immediate access to discharge notes in their medical records. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models (LLMs [eg, GPT-4]) have the ... ...

Abstract	Importance: By law, patients have immediate access to discharge notes in their medical records. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models (LLMs [eg, GPT-4]) have the potential to transform these notes into patient-friendly language and format. Objective: To determine whether an LLM can transform discharge summaries into a format that is more readable and understandable. Design, setting, and participants: This cross-sectional study evaluated a sample of the discharge summaries of adult patients discharged from the General Internal Medicine service at NYU (New York University) Langone Health from June 1 to 30, 2023. Patients discharged as deceased were excluded. All discharge summaries were processed by the LLM between July 26 and August 5, 2023. Interventions: A secure Health Insurance Portability and Accountability Act-compliant platform, Microsoft Azure OpenAI, was used to transform these discharge summaries into a patient-friendly format between July 26 and August 5, 2023. Main outcomes and measures: Outcomes included readability as measured by Flesch-Kincaid Grade Level and understandability using Patient Education Materials Assessment Tool (PEMAT) scores. Readability and understandability of the original discharge summaries were compared with the transformed, patient-friendly discharge summaries created through the LLM. As balancing metrics, accuracy and completeness of the patient-friendly version were measured. Results: Discharge summaries of 50 patients (31 female [62.0%] and 19 male [38.0%]) were included. The median patient age was 65.5 (IQR, 59.0-77.5) years. Mean (SD) Flesch-Kincaid Grade Level was significantly lower in the patient-friendly discharge summaries (6.2 [0.5] vs 11.0 [1.5]; P < .001). PEMAT understandability scores were significantly higher for patient-friendly discharge summaries (81% vs 13%; P < .001). Two physicians reviewed each patient-friendly discharge summary for accuracy on a 6-point scale, with 54 of 100 reviews (54.0%) giving the best possible rating of 6. Summaries were rated entirely complete in 56 reviews (56.0%). Eighteen reviews noted safety concerns, mostly involving omissions, but also several inaccurate statements (termed hallucinations). Conclusions and relevance: The findings of this cross-sectional study of 50 discharge summaries suggest that LLMs can be used to translate discharge summaries into patient-friendly language and formats that are significantly more readable and understandable than discharge summaries as they appear in electronic health records. However, implementation will require improvements in accuracy, completeness, and safety. Given the safety concerns, initial implementation will require physician review.
MeSH term(s)	United States ; Adult ; Humans ; Female ; Male ; Middle Aged ; Aged ; Inpatients ; Artificial Intelligence ; Cross-Sectional Studies ; Patient Discharge ; Electronic Health Records ; Language
Language	English
Publishing date	2024-03-04
Publishing country	United States
Document type	Journal Article
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2024.0357
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations.

Jethani, Neil / Sudarshan, Mukund / Aphinyanaphongs, Yindalon / Ranganath, Rajesh

Proceedings of machine learning research

2020 Volume 130, Page(s) 1459–1467

Abstract: While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce the cost of providing interpretations by learning a global selector model ... ...

Abstract	While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce the cost of providing interpretations by learning a global selector model that returns feature importances for a single instance of data. The selector model is trained to optimize the fidelity of the interpretations, as evaluated by a predictor model for the target. Popular methods learn the selector and predictor model in concert, which we show allows predictions to be encoded within interpretations. We introduce EVAL-X as a method to quantitatively evaluate interpretations and REAL-X as an amortized explanation method, which learn a predictor model that approximates the true data generating distribution given any subset of the input. We show EVAL-X can detect when predictions are encoded in interpretations and show the advantages of REAL-X through quantitative and radiologist evaluation.
Language	English
Publishing date	2020-07-17
Publishing country	United States
Document type	Journal Article
ISSN	2640-3498
ISSN (online)	2640-3498
Database	MEDical Literature Analysis and Retrieval System OnLINE

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