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  1. Article ; Online: The dawn of multimodal artificial intelligence in nephrology.

    Shickel, Benjamin / Bihorac, Azra

    Nature reviews. Nephrology

    2023  Volume 20, Issue 2, Page(s) 79–80

    MeSH term(s) Humans ; Artificial Intelligence ; Nephrology
    Language English
    Publishing date 2023-12-15
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 2490366-8
    ISSN 1759-507X ; 1759-5061
    ISSN (online) 1759-507X
    ISSN 1759-5061
    DOI 10.1038/s41581-023-00799-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6334: Show issues Location:
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    Zs.MG 107: Show issues

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  2. Article ; Online: The dilemma of consent for AI in healthcare.

    Balch, Jeremy A / Evans, Barbara J / Shickel, Benjamin / Bihorac, Azra / Upchurch, Gilbert R / Loftus, Tyler J

    Surgery

    2024  Volume 175, Issue 5, Page(s) 1456–1457

    MeSH term(s) Humans ; Health Facilities ; Delivery of Health Care ; Informed Consent
    Language English
    Publishing date 2024-02-27
    Publishing country United States
    Document type Editorial
    ZDB-ID 202467-6
    ISSN 1532-7361 ; 0039-6060
    ISSN (online) 1532-7361
    ISSN 0039-6060
    DOI 10.1016/j.surg.2024.01.019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Digital Health Transformers and Opportunities for Artificial Intelligence-Enabled Nephrology.

    Shickel, Benjamin / Loftus, Tyler J / Ren, Yuanfang / Rashidi, Parisa / Bihorac, Azra / Ozrazgat-Baslanti, Tezcan

    Clinical journal of the American Society of Nephrology : CJASN

    2023  Volume 18, Issue 4, Page(s) 527–529

    MeSH term(s) Humans ; Artificial Intelligence ; Nephrology
    Language English
    Publishing date 2023-02-09
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2226665-3
    ISSN 1555-905X ; 1555-9041
    ISSN (online) 1555-905X
    ISSN 1555-9041
    DOI 10.2215/CJN.0000000000000085
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 6584: Show issues Location:
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  4. Article ; Online: Reinforcement Learning for Clinical Applications.

    Khezeli, Kia / Siegel, Scott / Shickel, Benjamin / Ozrazgat-Baslanti, Tezcan / Bihorac, Azra / Rashidi, Parisa

    Clinical journal of the American Society of Nephrology : CJASN

    2023  Volume 18, Issue 4, Page(s) 521–523

    MeSH term(s) Humans ; Reinforcement, Psychology ; Learning
    Language English
    Publishing date 2023-02-08
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2226665-3
    ISSN 1555-905X ; 1555-9041
    ISSN (online) 1555-905X
    ISSN 1555-9041
    DOI 10.2215/CJN.0000000000000084
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Sequential Interpretability

    Shickel, Benjamin / Rashidi, Parisa

    Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data

    2020  

    Abstract: Deep learning continues to revolutionize an ever-growing number of critical application areas including healthcare, transportation, finance, and basic sciences. Despite their increased predictive power, model transparency and human explainability remain ... ...

    Abstract Deep learning continues to revolutionize an ever-growing number of critical application areas including healthcare, transportation, finance, and basic sciences. Despite their increased predictive power, model transparency and human explainability remain a significant challenge due to the "black box" nature of modern deep learning models. In many cases the desired balance between interpretability and performance is predominately task specific. Human-centric domains such as healthcare necessitate a renewed focus on understanding how and why these frameworks are arriving at critical and potentially life-or-death decisions. Given the quantity of research and empirical successes of deep learning for computer vision, most of the existing interpretability research has focused on image processing techniques. Comparatively, less attention has been paid to interpreting deep learning frameworks using sequential data. Given recent deep learning advancements in highly sequential domains such as natural language processing and physiological signal processing, the need for deep sequential explanations is at an all-time high. In this paper, we review current techniques for interpreting deep learning techniques involving sequential data, identify similarities to non-sequential methods, and discuss current limitations and future avenues of sequential interpretability research.
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-04-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  6. Article ; Online: Potentials and Challenges of Pervasive Sensing in the Intensive Care Unit.

    Davoudi, Anis / Shickel, Benjamin / Tighe, Patrick James / Bihorac, Azra / Rashidi, Parisa

    Frontiers in digital health

    2022  Volume 4, Page(s) 773387

    Abstract: Patients in critical care settings often require continuous and multifaceted monitoring. However, current clinical monitoring practices fail to capture important functional and behavioral indices such as mobility or agitation. Recent advances in non- ... ...

    Abstract Patients in critical care settings often require continuous and multifaceted monitoring. However, current clinical monitoring practices fail to capture important functional and behavioral indices such as mobility or agitation. Recent advances in non-invasive sensing technology, high throughput computing, and deep learning techniques are expected to transform the existing patient monitoring paradigm by enabling and streamlining granular and continuous monitoring of these crucial critical care measures. In this review, we highlight current approaches to pervasive sensing in critical care and identify limitations, future challenges, and opportunities in this emerging field.
    Language English
    Publishing date 2022-05-17
    Publishing country Switzerland
    Document type Journal Article ; Review
    ISSN 2673-253X
    ISSN (online) 2673-253X
    DOI 10.3389/fdgth.2022.773387
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Spatially Aware Transformer Networks for Contextual Prediction of Diabetic Nephropathy Progression from Whole Slide Images.

    Shickel, Benjamin / Lucarelli, Nicholas / Rao, Adish / Yun, Donghwan / Moon, Kyung Chul / Han, Seung Seok / Sarder, Pinaki

    Proceedings of SPIE--the International Society for Optical Engineering

    2023  Volume 12471

    Abstract: Diabetic nephropathy (DN) in the context of type 2 diabetes is the leading cause of end-stage renal disease (ESRD) in the United States. DN is graded based on glomerular morphology and has a spatially heterogeneous presentation in kidney biopsies that ... ...

    Abstract Diabetic nephropathy (DN) in the context of type 2 diabetes is the leading cause of end-stage renal disease (ESRD) in the United States. DN is graded based on glomerular morphology and has a spatially heterogeneous presentation in kidney biopsies that complicates pathologists' predictions of disease progression. Artificial intelligence and deep learning methods for pathology have shown promise for quantitative pathological evaluation and clinical trajectory estimation; but, they often fail to capture large-scale spatial anatomy and relationships found in whole slide images (WSIs). In this study, we present a transformer-based, multi-stage ESRD prediction framework built upon nonlinear dimensionality reduction, relative Euclidean pixel distance embeddings between every pair of observable glomeruli, and a corresponding spatial self-attention mechanism for a robust contextual representation. We developed a deep transformer network for encoding WSI and predicting future ESRD using a dataset of 56 kidney biopsy WSIs from DN patients at Seoul National University Hospital. Using a leave-one-out cross-validation scheme, our modified transformer framework outperformed RNNs, XGBoost, and logistic regression baseline models, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.97 (95% CI: 0.90-1.00) for predicting two-year ESRD, compared with an AUC of 0.86 (95% CI: 0.66-0.99) without our relative distance embedding, and an AUC of 0.76 (95% CI: 0.59-0.92) without a denoising autoencoder module. While the variability and generalizability induced by smaller sample sizes are challenging, our distance-based embedding approach and overfitting mitigation techniques yielded results that suggest opportunities for future spatially aware WSI research using limited pathology datasets.
    Language English
    Publishing date 2023-04-06
    Publishing country United States
    Document type Journal Article
    ISSN 0277-786X
    ISSN 0277-786X
    DOI 10.1117/12.2655266
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Resident Operative Autonomy and Attending Verbal Feedback Differ by Resident and Attending Gender.

    Filiberto, Amanda C / Abbott, Kenneth L / Shickel, Benjamin / George, Brian C / Cochran, Amalia L / Sarosi, George A / Upchurch, Gilbert R / Loftus, Tyler J

    Annals of surgery open : perspectives of surgical history, education, and clinical approaches

    2023  Volume 4, Issue 1, Page(s) e256

    Abstract: Objectives: This study tests the null hypotheses that overall sentiment and gendered words in verbal feedback and resident operative autonomy relative to performance are similar for female and male residents.: Background: Female and male surgical ... ...

    Abstract Objectives: This study tests the null hypotheses that overall sentiment and gendered words in verbal feedback and resident operative autonomy relative to performance are similar for female and male residents.
    Background: Female and male surgical residents may experience training differently, affecting the quality of learning and graduated autonomy.
    Methods: A longitudinal, observational study using a Society for Improving Medical Professional Learning collaborative dataset describing resident and attending evaluations of resident operative performance and autonomy and recordings of verbal feedback from attendings from surgical procedures performed at 54 US general surgery residency training programs from 2016 to 2021. Overall sentiment, adjectives, and gendered words in verbal feedback were quantified by natural language processing. Resident operative autonomy and performance, as evaluated by attendings, were reported on 5-point ordinal scales. Performance-adjusted autonomy was calculated as autonomy minus performance.
    Results: The final dataset included objective assessments and dictated feedback for 2683 surgical procedures. Sentiment scores were higher for female residents (95 [interquartile range (IQR), 4-100] vs 86 [IQR 2-100];
    Conclusions: Sentiment and gendered words in verbal feedback and performance-adjusted operative autonomy differed for female and male general surgery residents. These findings suggest a need to ensure that trainees are given appropriate and equitable operative autonomy and feedback.
    Language English
    Publishing date 2023-02-02
    Publishing country United States
    Document type Journal Article
    ISSN 2691-3593
    ISSN (online) 2691-3593
    DOI 10.1097/AS9.0000000000000256
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks.

    Shickel, Benjamin / Loftus, Tyler J / Ruppert, Matthew / Upchurch, Gilbert R / Ozrazgat-Baslanti, Tezcan / Rashidi, Parisa / Bihorac, Azra

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 1224

    Abstract: Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machine ... ...

    Abstract Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machine learning models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests and XGBoost for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data. Our study indicated several significant results across experimental settings that suggest the utility of deep learning for capturing more precise representations of patient health for augmented surgical decision support. Multi-task learning improved efficiency by reducing computational resources without compromising predictive performance. Integrated gradients interpretability mechanisms identified potentially modifiable risk factors for each complication. Monte Carlo dropout methods provided a quantitative measure of prediction uncertainty that has the potential to enhance clinical trust. Multi-task learning, interpretability mechanisms, and uncertainty metrics demonstrated potential to facilitate effective clinical implementation.
    MeSH term(s) Humans ; Longitudinal Studies ; Uncertainty ; Neural Networks, Computer ; Postoperative Complications/etiology ; Machine Learning
    Language English
    Publishing date 2023-01-21
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-27418-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Spatially Aware Transformer Networks for Contextual Prediction of Diabetic Nephropathy Progression from Whole Slide Images.

    Shickel, Benjamin / Lucarelli, Nicholas / Rao, Adish S / Yun, Donghwan / Moon, Kyung Chul / Han, Seung Seok / Sarder, Pinaki

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: Diabetic nephropathy (DN) in the context of type 2 diabetes is the leading cause of end-stage renal disease (ESRD) in the United States. DN is graded based on glomerular morphology and has a spatially heterogeneous presentation in kidney biopsies that ... ...

    Abstract Diabetic nephropathy (DN) in the context of type 2 diabetes is the leading cause of end-stage renal disease (ESRD) in the United States. DN is graded based on glomerular morphology and has a spatially heterogeneous presentation in kidney biopsies that complicates pathologists' predictions of disease progression. Artificial intelligence and deep learning methods for pathology have shown promise for quantitative pathological evaluation and clinical trajectory estimation; but, they often fail to capture large-scale spatial anatomy and relationships found in whole slide images (WSIs). In this study, we present a transformer-based, multi-stage ESRD prediction framework built upon nonlinear dimensionality reduction, relative Euclidean pixel distance embeddings between every pair of observable glomeruli, and a corresponding spatial self-attention mechanism for a robust contextual representation. We developed a deep transformer network for encoding WSI and predicting future ESRD using a dataset of 56 kidney biopsy WSIs from DN patients at Seoul National University Hospital. Using a leave-one-out cross-validation scheme, our modified transformer framework outperformed RNNs, XGBoost, and logistic regression baseline models, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.97 (95% CI: 0.90-1.00) for predicting two-year ESRD, compared with an AUC of 0.86 (95% CI: 0.66-0.99) without our relative distance embedding, and an AUC of 0.76 (95% CI: 0.59-0.92) without a denoising autoencoder module. While the variability and generalizability induced by smaller sample sizes are challenging, our distance-based embedding approach and overfitting mitigation techniques yielded results that sugest opportunities for future spatially aware WSI research using limited pathology datasets.
    Language English
    Publishing date 2023-02-23
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.02.20.23286044
    Database MEDical Literature Analysis and Retrieval System OnLINE

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