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Article ; Online: Applying a user-centred design machine learning toolkit to an autism spectrum disorder use case.

Plasek, Joseph M / Zhou, Li

BMJ health & care informatics

2023 Volume 30, Issue 1

MeSH term(s)	Humans ; Autism Spectrum Disorder/therapy ; Machine Learning ; Algorithms ; User-Centered Design
Language	English
Publishing date	2023-05-08
Publishing country	England
Document type	Editorial
ISSN	2632-1009
ISSN (online)	2632-1009
DOI	10.1136/bmjhci-2023-100765
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Applying a user-centred design machine learning toolkit to an autism spectrum disorder use case

Li Zhou / Joseph M Plasek

BMJ Health & Care Informatics, Vol 30, Iss

2023 Volume 1

Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Investigating the Association Between Dynamic Driving Pressure and Mortality in COVID-19-Related Acute Respiratory Distress Syndrome: A Joint Modeling Approach Using Real-Time Continuously-Monitored Ventilation Data.

Tan, Daniel J / Plasek, Joseph M / Hou, Peter C / Baron, Rebecca M / Atkinson, Benjamin J / Zhou, Li

Critical care explorations

2024 Volume 6, Issue 3, Page(s) e1043

Abstract: Importance and objectives: COVID-19-related acute respiratory distress syndrome (ARDS) is associated with high mortality and often necessitates invasive mechanical ventilation (IMV). Previous studies on non-COVID-19 ARDS have shown driving pressure to ... ...

Abstract	Importance and objectives: COVID-19-related acute respiratory distress syndrome (ARDS) is associated with high mortality and often necessitates invasive mechanical ventilation (IMV). Previous studies on non-COVID-19 ARDS have shown driving pressure to be robustly associated with ICU mortality; however, those studies relied on "static" driving pressure measured periodically and manually. As "continuous" automatically monitored driving pressure is becoming increasingly available and reliable with more advanced mechanical ventilators, we aimed to examine the effect of this "dynamic" driving pressure in COVID-19 ARDS throughout the entire ventilation period. Design setting and participants: This retrospective, observational study cohort study evaluates the association between driving pressure and ICU mortality in patients with concurrent COVID-19 and ARDS using multivariate joint modeling. The study cohort ( Measurements and main results: Of 544 included patients, 171 (31.4%) died in the ICU. Increased dynamic ΔP was associated with increased risk in the hazard of ICU mortality (hazard ratio [HR] 1.035; 95% credible interval, 1.004-1.069) after adjusting for other relevant dynamic respiratory biomarkers. A significant increase in risk in the hazard of death was found for every hour of exposure to high intensities of driving pressure (≥ 15 cm H Conclusions: Limiting patients' exposure to high intensities of driving pressure even while under lung-protective ventilation may represent a critical step in improving ICU survival in patients with COVID-19 ARDS. Time-series IMV data could be leveraged to enhance real-time monitoring and decision support to optimize ventilation strategies at the bedside.
Language	English
Publishing date	2024-03-05
Publishing country	United States
Document type	Journal Article
ISSN	2639-8028
ISSN (online)	2639-8028
DOI	10.1097/CCE.0000000000001043
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Large language models for biomedicine: foundations, opportunities, challenges, and best practices.

Sahoo, Satya S / Plasek, Joseph M / Xu, Hua / Uzuner, Özlem / Cohen, Trevor / Yetisgen, Meliha / Liu, Hongfang / Meystre, Stéphane / Wang, Yanshan

Journal of the American Medical Informatics Association : JAMIA

2024

Abstract: Objectives: Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and ...

Abstract	Objectives: Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Target audience: Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. Scope: We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.
Language	English
Publishing date	2024-04-24
Publishing country	England
Document type	Journal Article
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocae074
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Article ; Online: Scalable Feature Engineering from Electronic Free Text Notes to Supplement Confounding Adjustment of Claims-Based Pharmacoepidemiologic Studies.

Wyss, Richard / Plasek, Joseph M / Zhou, Li / Bessette, Lily G / Schneeweiss, Sebastian / Rassen, Jeremy A / Tsacogianis, Theodore / Lin, Kueiyu Joshua

Clinical pharmacology and therapeutics

2023 Volume 113, Issue 4, Page(s) 832–838

Abstract: Natural language processing (NLP) tools turn free-text notes (FTNs) from electronic health records (EHRs) into data features that can supplement confounding adjustment in pharmacoepidemiologic studies. However, current applications are difficult to scale. ...

Abstract	Natural language processing (NLP) tools turn free-text notes (FTNs) from electronic health records (EHRs) into data features that can supplement confounding adjustment in pharmacoepidemiologic studies. However, current applications are difficult to scale. We used unsupervised NLP to generate high-dimensional feature spaces from FTNs to improve prediction of drug exposure and outcomes compared with claims-based analyses. We linked Medicare claims with EHR data to generate three cohort studies comparing different classes of medications on the risk of various clinical outcomes. We used "bag-of-words" to generate features for the top 20,000 most prevalent terms from FTNs. We compared machine learning (ML) prediction algorithms using different sets of candidate predictors: Set1 (39 researcher-specified variables), Set2 (Set1 + ML-selected claims codes), and Set3 (Set1 + ML-selected NLP-generated features), vs. Set4 (Set1 + 2 + 3). When modeling treatment choice, we observed a consistent pattern across the examples: ML models utilizing Set4 performed best followed by Set2, Set3, then Set1. When modeling the outcome risk, there was little to no improvement beyond models based on Set1. Supplementing claims data with NLP-generated features from free text notes improved prediction of prescribing choices but had little or no improvement on clinical risk prediction. These findings have implications for strategies to improve confounding using EHR data in pharmacoepidemiologic studies.
MeSH term(s)	Aged ; United States ; Humans ; Medicare ; Electronic Health Records ; Cohort Studies ; Natural Language Processing ; Algorithms
Language	English
Publishing date	2023-01-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	123793-7
ISSN	1532-6535 ; 0009-9236
ISSN (online)	1532-6535
ISSN	0009-9236
DOI	10.1002/cpt.2826
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Predicting Falls Among Community-Dwelling Older Adults: A Demonstration of Applied Machine Learning.

Yang, Rumei / Plasek, Joseph M / Cummins, Mollie R / Sward, Katherine A

Computers, informatics, nursing : CIN

2020 Volume 39, Issue 5, Page(s) 273–280

Abstract: Data science skills are increasingly needed by informatics nurses and nurse scientists, but techniques such as machine learning can be daunting for those with clinical, rather than computer science or technical, backgrounds. With the increasing quantity ... ...

Abstract	Data science skills are increasingly needed by informatics nurses and nurse scientists, but techniques such as machine learning can be daunting for those with clinical, rather than computer science or technical, backgrounds. With the increasing quantity of publicly available population-level datasets, identification of factors that predict clinical outcomes is possible using machine learning algorithms. This study demonstrates how to apply a machine learning approach to nursing-relevant questions, specifically an approach to predict falls among community-dwelling older adults, based on data from the 2014 Behavioral Risk Factor Surveillance System. A random forest algorithm, a common approach to machine learning, was compared to a logistic regression model. Explanations of how to interpret the models and their associated performance characteristics are included to serve as a tutorial to readers. Machine learning methods constitute an increasingly important approach for nursing as population-level data are increasingly being made available to the public.
MeSH term(s)	Accidental Falls/prevention & control ; Aged ; Algorithms ; Humans ; Independent Living ; Logistic Models ; Machine Learning
Language	English
Publishing date	2020-11-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2078463-6
ISSN	1538-9774 ; 1538-2931
ISSN (online)	1538-9774
ISSN	1538-2931
DOI	10.1097/CIN.0000000000000688
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.B 3083: Show issues

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Article ; Online: Rethinking Data Sharing at the Dawn of a Health Data Economy: A Viewpoint.

Tang, Chunlei / Plasek, Joseph M / Bates, David W

Journal of medical Internet research

2018 Volume 20, Issue 11, Page(s) e11519

Abstract: A health data economy has begun to form, but its rise has been tempered by the profound lack of sharing of both data and data products such as models, intermediate results, and annotated training corpora, and this severely limits the potential for ... ...

Abstract	A health data economy has begun to form, but its rise has been tempered by the profound lack of sharing of both data and data products such as models, intermediate results, and annotated training corpora, and this severely limits the potential for triggering economic cluster effects. Economic cluster effects represent a means to elicit benefit from economies of scale from internal data innovations and are beneficial because they may mitigate challenges from external sources. Within institutions, data product sharing is needed to spark data entrepreneurship and data innovation, and cross-institutional sharing is also critical, especially for rare conditions.
MeSH term(s)	Economics, Medical ; Humans ; Information Dissemination/methods
Language	English
Publishing date	2018-11-22
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/11519
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Enhancing Early Detection of Cognitive Decline in the Elderly through Ensemble of NLP Techniques: A Comparative Study Utilizing Large Language Models in Clinical Notes.

Du, Xinsong / Novoa-Laurentiev, John / Plasek, Joseph M / Chuang, Ya-Wen / Wang, Liqin / Chang, Frank / Datta, Surabhi / Paek, Hunki / Lin, Bin / Wei, Qiang / Wang, Xiaoyan / Wang, Jingqi / Ding, Hao / Manion, Frank J / Du, Jingcheng / Zhou, Li

medRxiv : the preprint server for health sciences

2024

Abstract: Background: Early detection of cognitive decline in elderly individuals facilitates clinical trial enrollment and timely medical interventions. This study aims to apply, evaluate, and compare advanced natural language processing techniques for ... ...

Abstract	Background: Early detection of cognitive decline in elderly individuals facilitates clinical trial enrollment and timely medical interventions. This study aims to apply, evaluate, and compare advanced natural language processing techniques for identifying signs of cognitive decline in clinical notes. Methods: This study, conducted at Mass General Brigham (MGB), Boston, MA, included clinical notes from the 4 years prior to initial mild cognitive impairment (MCI) diagnosis in 2019 for patients ≥ 50 years. Note sections regarding cognitive decline were labeled manually. A random sample of 4,949 note sections filtered with cognitive functions-related keywords were used for traditional AI model development, and 200 random subset were used for LLM and prompt development; another random sample of 1996 note sections without keyword filtering were used for testing. Prompt templates for large language models (LLM), Llama 2 on Amazon Web Service and GPT-4 on Microsoft Azure, were developed with multiple prompting approaches to select the optimal LLM-based method. Baseline comparisons were made with XGBoost and a hierarchical attention-based deep neural network model. An ensemble of the three models was then constructed using majority vote. Results: GPT-4 demonstrated superior accuracy and efficiency to Llama 2. The ensemble model outperformed individual models, achieving a precision of 90.3%, recall of 94.2%, and F1-score of 92.2%. Notably, the ensemble model demonstrated a marked improvement in precision (from a 70%-79% range to above 90%) compared to the best performing single model. Error analysis revealed 63 samples were wrongly predicted by at least one model; however, only 2 cases (3.2%) were mutual errors across all models, indicating diverse error profiles among them. Conclusion: Our findings indicate that LLMs and traditional models exhibit diverse error profiles. The ensemble of LLMs and locally trained machine learning models on EHR data was found to be complementary, enhancing performance and improving diagnostic accuracy.
Language	English
Publishing date	2024-04-05
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.04.03.24305298
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications.

Hua, Yining / Jiang, Hang / Lin, Shixu / Yang, Jie / Plasek, Joseph M / Bates, David W / Zhou, Li

Journal of the American Medical Informatics Association : JAMIA

2022 Volume 29, Issue 10, Page(s) 1668–1678

Abstract: Objective: Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on ... ...

Abstract	Objective: Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter across time. Methods: This retrospective study included 609 189 US-based tweets between January 29, 2020 and November 30, 2021 on 4 drugs that gained wide public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people's stances on each drug. Results: Time-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the 2 major US political parties was significantly different (P < .001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%). Conclusion: Our study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug.
MeSH term(s)	COVID-19/drug therapy ; Cytidine/analogs & derivatives ; Delivery of Health Care ; Humans ; Hydroxychloroquine/therapeutic use ; Hydroxylamines ; Ivermectin ; Off-Label Use ; Pandemics ; Public Opinion ; Retrospective Studies ; Social Media
Chemical Substances	Hydroxylamines ; Hydroxychloroquine (4QWG6N8QKH) ; Cytidine (5CSZ8459RP) ; Ivermectin (70288-86-7) ; molnupiravir (YA84KI1VEW)
Language	English
Publishing date	2022-07-01
Publishing country	England
Document type	Journal Article
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocac114
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 4128: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 312: Show issues

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Article ; Online: Characterizing terminology applied by authors and database producers to informatics literature on consumer engagement with wearable devices.

Alpi, Kristine M / Martin, Christie L / Plasek, Joseph M / Sittig, Scott / Smith, Catherine Arnott / Weinfurter, Elizabeth V / Wells, Jennifer K / Wong, Rachel / Austin, Robin R

Journal of the American Medical Informatics Association : JAMIA

2023 Volume 30, Issue 7, Page(s) 1284–1292

Abstract: Objective: Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on ... ...

Abstract	Objective: Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies. Materials and methods: To retrieve articles from PubMed that addressed patient/consumer engagement with wearables, we developed a search strategy of textwords and Medical Subject Headings (MeSH). To refine our methodology, we used a random sample of 200 articles from 2016 to 2018. A descriptive analysis of articles (N = 2522) from 2019 identified 308 (12.2%) CHI-related articles, for which we characterized their assigned terminology. We visualized the 100 most frequent terms assigned to the articles from MeSH, author keywords, CINAHL, and Engineering Databases (Compendex and Inspec together). We assessed the overlap of CHI terms among sources and evaluated terms related to consumer engagement. Results: The 308 articles were published in 181 journals, more in health journals (82%) than informatics (11%). Only 44% were indexed with the MeSH term "wearable electronic devices." Author keywords were common (91%) but rarely represented consumer engagement with device data, eg, self-monitoring (n = 12, 0.7%) or self-management (n = 9, 0.5%). Only 10 articles (3%) had terminology from all sources (authors, PubMed, CINAHL, Compendex, and Inspec). Discussion: Our main finding was that consumer engagement was not well represented in health and engineering database thesauri. Conclusions: Authors of CHI studies should indicate consumer/patient engagement and the specific technology investigated in titles, abstracts, and author keywords to facilitate discovery by readers and expand vocabularies and indexing.
MeSH term(s)	Humans ; Medical Subject Headings ; PubMed ; Vocabulary, Controlled ; Consumer Health Informatics ; Patient Participation
Language	English
Publishing date	2023-05-16
Publishing country	England
Document type	Journal Article
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocad082
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 4128: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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