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  1. Article ; Online: To err is divine?

    Swails, Jennifer L / Bernstam, Elmer V

    Medical education

    2023  Volume 57, Issue 5, Page(s) 389–391

    Language English
    Publishing date 2023-03-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 195274-2
    ISSN 1365-2923 ; 0308-0110
    ISSN (online) 1365-2923
    ISSN 0308-0110
    DOI 10.1111/medu.15063
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Why is biomedical informatics hard? A fundamental framework.

    Johnson, Todd R / Bernstam, Elmer V

    Journal of biomedical informatics

    2023  Volume 140, Page(s) 104327

    Abstract: Building on previous work to define the scientific discipline of biomedical informatics, we present a framework that categorizes fundamental challenges into groups based on data, information, and knowledge, along with the transitions between these levels. ...

    Abstract Building on previous work to define the scientific discipline of biomedical informatics, we present a framework that categorizes fundamental challenges into groups based on data, information, and knowledge, along with the transitions between these levels. We define each level and argue that the framework provides a basis for separating informatics problems from non-informatics problems, identifying fundamental challenges in biomedical informatics, and provides guidance regarding the search for general, reusable solutions to informatics problems. We distinguish between processing data (symbols) and processing meaning. Computational systems, that are the basis for modern information technology (IT), process data. In contrast, many important challenges in biomedicine, such as providing clinical decision support, require processing meaning, not data. Biomedical informatics is hard because of the fundamental mismatch between many biomedical problems and the capabilities of current technology.
    MeSH term(s) Medical Informatics ; Knowledge ; Decision Support Systems, Clinical
    Language English
    Publishing date 2023-03-08
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2057141-0
    ISSN 1532-0480 ; 1532-0464
    ISSN (online) 1532-0480
    ISSN 1532-0464
    DOI 10.1016/j.jbi.2023.104327
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review.

    Li, Linda T / Haley, Lauren C / Boyd, Alexandra K / Bernstam, Elmer V

    Journal of biomedical informatics

    2023  Volume 147, Page(s) 104531

    Abstract: Introduction: The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic ... ...

    Abstract Introduction: The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges.
    Methods: We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine.
    Results: We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education.
    Conclusion: We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Benchmarking ; Machine Learning ; Medicine
    Language English
    Publishing date 2023-10-25
    Publishing country United States
    Document type Systematic Review ; Journal Article
    ZDB-ID 2057141-0
    ISSN 1532-0480 ; 1532-0464
    ISSN (online) 1532-0480
    ISSN 1532-0464
    DOI 10.1016/j.jbi.2023.104531
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Confidence-based laboratory test reduction recommendation algorithm.

    Huang, Tongtong / Li, Linda T / Bernstam, Elmer V / Jiang, Xiaoqian

    BMC medical informatics and decision making

    2023  Volume 23, Issue 1, Page(s) 93

    Abstract: Background: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.: Methods: We collected internal patient data from a ... ...

    Abstract Background: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.
    Methods: We collected internal patient data from a teaching hospital in Houston and external patient data from the MIMIC III database. The study used a conservative definition of unnecessary laboratory tests, which was defined as stable (i.e., stability) and below the lower normal bound (i.e., normality). Considering that machine learning models may yield less reliable results when trained on noisy inputs containing low-quality information, we estimated prediction confidence to assess the reliability of predicted outcomes. We adopted a "select and predict" design philosophy to maximize prediction performance by selectively considering samples with high prediction confidence for recommendations. Our model accommodated irregularly sampled observational data to make full use of variable correlations (i.e., with other laboratory test values) and temporal dependencies (i.e., previous laboratory tests performed within the same encounter) in selecting candidates for training and prediction.
    Results: The proposed model demonstrated remarkable Hgb prediction performance, achieving a normality AUC of 95.89% and a Hgb stability AUC of 95.94%, while recommending a reduction of 9.91% of Hgb tests that were deemed unnecessary. Additionally, the model could generalize well to external patients admitted to another hospital.
    Conclusions: This study introduces a novel deep learning model with the potential to significantly reduce healthcare costs and improve patient outcomes by identifying unnecessary laboratory tests for hospitalized patients.
    MeSH term(s) Humans ; Reproducibility of Results ; Machine Learning ; Algorithms ; Hospitalization ; Electronic Health Records
    Language English
    Publishing date 2023-05-10
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-023-02187-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Predicting multiple sclerosis severity with multimodal deep neural networks.

    Zhang, Kai / Lincoln, John A / Jiang, Xiaoqian / Bernstam, Elmer V / Shams, Shayan

    BMC medical informatics and decision making

    2023  Volume 23, Issue 1, Page(s) 255

    Abstract: Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of ... ...

    Abstract Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to data insufficiency or model simplicity. In this paper, we proposed the idea of using patients' multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity in the future. Our contribution has two main facets. First, we describe a pioneering effort to integrate structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient's MS severity. The proposed pipeline demonstrates up to 19% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides valuable insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes.
    MeSH term(s) Humans ; Multiple Sclerosis/diagnostic imaging ; Neural Networks, Computer ; Machine Learning ; Algorithms ; Neuroimaging
    Language English
    Publishing date 2023-11-09
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-023-02354-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: External Validation of a Laboratory Prediction Algorithm for the Reduction of Unnecessary Labs in the Critical Care Setting.

    Li, Linda T / Huang, Tongtong / Bernstam, Elmer V / Jiang, Xiaoqian

    The American journal of medicine

    2022  Volume 135, Issue 6, Page(s) 769–774

    Abstract: Background: Unnecessary laboratory tests contribute to iatrogenic harm and are a major source of waste in the health care system. We previously developed a machine learning algorithm to help clinicians identify unnecessary laboratory tests, but it has ... ...

    Abstract Background: Unnecessary laboratory tests contribute to iatrogenic harm and are a major source of waste in the health care system. We previously developed a machine learning algorithm to help clinicians identify unnecessary laboratory tests, but it has not been externally validated. In this study, we externally validate our machine learning algorithm.
    Methods: To externally validate the machine learning algorithm that was originally trained on the Medical Information Mart for Intensive Care (MIMIC) III database, we tested the algorithm in a separate institution. We identified and abstracted data for all patients older than 18 years admitted to the intensive care unit at Memorial Hermann Hospital in Houston, Texas (MHH) from January 1, 2020 to November 13, 2020. Using the transfer learning style, we performed external validation of the machine learning algorithm.
    Results: A total of 651 MHH patients were included. The model performed well in predicting abnormality (area under the curve [AUC] 0.98 for MIMIC III and 0.89 for MHH). The model performed similarly in predicting transitions from normal laboratory range to abnormal (AUC 0.71 for MIMIC III and 0.70 for MHH). The performance of the model in predicting the actual laboratory value was also similar in the MIMIC III (accuracy 0.41) and MHH data (0.45).
    Conclusions: We externally validated the machine learning model and showed that the model performed similarly, supporting the generalizability to other settings. While this model demonstrated good performance for predicting abnormal labs and transitions, it does not perform well enough for prediction of laboratory values in most clinical applications.
    MeSH term(s) Algorithms ; Area Under Curve ; Critical Care ; Humans ; Intensive Care Units ; Machine Learning
    Language English
    Publishing date 2022-01-31
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 80015-6
    ISSN 1555-7162 ; 1873-2178 ; 0002-9343 ; 1548-2766
    ISSN (online) 1555-7162 ; 1873-2178
    ISSN 0002-9343 ; 1548-2766
    DOI 10.1016/j.amjmed.2021.12.020
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ua VI Zs.289: Show issues Location:
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  7. Article ; Online: Generalized and transferable patient language representation for phenotyping with limited data.

    Si, Yuqi / Bernstam, Elmer V / Roberts, Kirk

    Journal of biomedical informatics

    2021  Volume 116, Page(s) 103726

    Abstract: The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and ... ...

    Abstract The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 respiratory diseases, and 17 genitourinary diseases. We find multi-task pre-training increases learning efficiency and achieves consistently high performance across the majority of phenotypes. Most important, the multi-task pre-training is almost always either the best-performing model or performs tolerably close to the best-performing model, a property we refer to as robust. All these results lead us to conclude that this multi-task transfer learning architecture is a robust approach for developing generalized and transferable patient language representations for numerous phenotypes.
    MeSH term(s) Humans ; Language ; Natural Language Processing
    Language English
    Publishing date 2021-03-09
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2057141-0
    ISSN 1532-0480 ; 1532-0464
    ISSN (online) 1532-0480
    ISSN 1532-0464
    DOI 10.1016/j.jbi.2021.103726
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Enoxaparin may be associated with lower rates of mortality than unfractionated heparin in neurocritical and surgical patients.

    Samuel, Sophie / To, Catherine / Ling, Yaobin / Zhang, Kai / Jiang, Xiaoqian / Bernstam, Elmer V

    Journal of thrombosis and thrombolysis

    2023  Volume 55, Issue 3, Page(s) 439–448

    Abstract: Unfractionated heparin (UFH) and low molecular weight heparin (LMWH) are often administered to prevent venous thromboembolism (VTE) in critically ill patients. However, the preferred prophylactic agent (UFH or LMWH) is not known. We compared the all- ... ...

    Abstract Unfractionated heparin (UFH) and low molecular weight heparin (LMWH) are often administered to prevent venous thromboembolism (VTE) in critically ill patients. However, the preferred prophylactic agent (UFH or LMWH) is not known. We compared the all-cause mortality rate in patients receiving UFH to LMWH for VTE prophylaxis. We conducted a retrospective propensity score adjusted analysis of patients admitted to neuro-critical, surgical, or medical intensive care units. Patients were included if they were screened with venous duplex ultrasonography or computed tomography angiography for detection of VTE. The primary outcome was all-cause mortality. Secondary outcomes included the prevalence of VTE, deep vein thrombosis (DVT), pulmonary embolism (PE), and hospital length of stay (LOS). Initially 2228 patients in the cohort were included for analysis, 1836 (82%) patients received UFH, and 392 (18%) patients received enoxaparin. After propensity score matching, a well-balanced cohort of 618 patients remained in the study (309 patients receiving UFH; 309 patients receiving enoxaparin). The use of UFH for VTE prophylaxis in ICU patients was associated with similar rates of all-cause mortality compared with enoxaparin [RR 0.73; 95% CI 0.43-1.24, p = 0.310]. There were no differences in the prevalence of DVT, prevalence of PE or hospital LOS between the two groups, DVT [RR 0.93; 95% CI 0.56-1.53, p = 0.889], PE [RR 1.50; 95% CI 0.78-2.90, p = 0.296] and LOS [9 ± 9 days vs 9 ± 8; p = 0.857]. A trend toward mortality benefit was observed in NICU [RR 0.37; 95% CI 0.13-1.07, p = 0.062] and surgical patients [RR 0.43; 95% CI 0.17-1.02, p = 0.075] favoring the enoxaparin group. The use of UFH for VTE prophylaxis in ICU patients was associated with similar rates of VTE, all-cause mortality and LOS compared to enoxaparin. In subgroup analysis, neuro-critical and surgical patients who received UFH had a higher rate of mortality than those who received enoxaparin.
    MeSH term(s) Humans ; Heparin/therapeutic use ; Enoxaparin/therapeutic use ; Heparin, Low-Molecular-Weight/therapeutic use ; Anticoagulants/therapeutic use ; Venous Thromboembolism/drug therapy ; Venous Thromboembolism/prevention & control ; Venous Thromboembolism/etiology ; Retrospective Studies ; Pulmonary Embolism/drug therapy
    Chemical Substances Heparin (9005-49-6) ; Enoxaparin ; Heparin, Low-Molecular-Weight ; Anticoagulants
    Language English
    Publishing date 2023-01-10
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1230645-9
    ISSN 1573-742X ; 0929-5305
    ISSN (online) 1573-742X
    ISSN 0929-5305
    DOI 10.1007/s11239-022-02755-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4352: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  9. Article ; Online: Predicting multiple sclerosis severity with multimodal deep neural networks

    Kai Zhang / John A. Lincoln / Xiaoqian Jiang / Elmer V. Bernstam / Shayan Shams

    BMC Medical Informatics and Decision Making, Vol 23, Iss 1, Pp 1-

    2023  Volume 17

    Abstract: Abstract Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed ...

    Abstract Abstract Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to data insufficiency or model simplicity. In this paper, we proposed the idea of using patients’ multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity in the future. Our contribution has two main facets. First, we describe a pioneering effort to integrate structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient’s MS severity. The proposed pipeline demonstrates up to 19% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides valuable insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes.
    Keywords Multimodal deep learning ; Multiple sclerosis ; Expanded disability status scale ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Automatic classification of scanned electronic health record documents.

    Goodrum, Heath / Roberts, Kirk / Bernstam, Elmer V

    International journal of medical informatics

    2020  Volume 144, Page(s) 104302

    Abstract: Objectives: Electronic Health Records (EHRs) contain scanned documents from a variety of sources such as identification cards, radiology reports, clinical correspondence, and many other document types. We describe the distribution of scanned documents ... ...

    Abstract Objectives: Electronic Health Records (EHRs) contain scanned documents from a variety of sources such as identification cards, radiology reports, clinical correspondence, and many other document types. We describe the distribution of scanned documents at one health institution and describe the design and evaluation of a system to categorize documents into clinically relevant and non-clinically relevant categories as well as further sub-classifications. Our objective is to demonstrate that text classification systems can accurately classify scanned documents.
    Methods: We extracted text using Optical Character Recognition (OCR). We then created and evaluated multiple text classification machine learning models, including both "bag of words" and deep learning approaches. We evaluated the system on three different levels of classification using both the entire document as input, as well as the individual pages of the document. Finally, we compared the effects of different text processing methods.
    Results: A deep learning model using ClinicalBERT performed best. This model distinguished between clinically-relevant documents and not clinically-relevant documents with an accuracy of 0.973; between intermediate sub-classifications with an accuracy of 0.949; and between individual classes with an accuracy of 0.913.
    Discussion: Within the EHR, some document categories such as "external medical records" may contain hundreds of scanned pages without clear document boundaries. Without further sub-classification, clinicians must view every page or risk missing clinically-relevant information. Machine learning can automatically classify these scanned documents to reduce clinician burden.
    Conclusion: Using machine learning applied to OCR-extracted text has the potential to accurately identify clinically-relevant scanned content within EHRs.
    MeSH term(s) Electronic Health Records ; Humans ; Machine Learning ; Natural Language Processing
    Language English
    Publishing date 2020-10-17
    Publishing country Ireland
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1466296-6
    ISSN 1872-8243 ; 1386-5056
    ISSN (online) 1872-8243
    ISSN 1386-5056
    DOI 10.1016/j.ijmedinf.2020.104302
    Database MEDical Literature Analysis and Retrieval System OnLINE

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