Explainable artificial intelligence model to predict acute critical illness from electronic health records

3 December 2019

S. Lauritsen

Mads Kristensen

Mathias Vassard Olsen

Morten Skaarup Larsen

K. M. Lauritsen

Marianne Johansson Jørgensen

Jeppe Lange

B. Thiesson

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Papers citing "Explainable artificial intelligence model to predict acute critical illness from electronic health records"

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Title
DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation Munib Mesinovic Soheila Molaei Peter Watkinson Tingting Zhu AI4TS 68 0 0 28 Mar 2025
Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care Yuxiao Cheng Xinxin Song Ziqian Wang Qin Zhong Kunlun He J. Suo OOD CML 140 0 0 04 Feb 2025
Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices Santino Nanini Mariem Abid Yassir Mamouni Arnaud Wiedemann Philippe Jouvet Stephane Bourassa 74 0 0 30 Oct 2024
Reading ability detection using eye-tracking data with LSTM-based few-shot learning Nanxi Li Hongjiang Wang Zehui Zhan 65 0 0 13 Sep 2024
Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques Negin Ashrafi A. Abdollahi Maryam Pishgar 43 7 0 02 Aug 2024
Applications of Explainable artificial intelligence in Earth system science Feini Huang Shijie Jiang Lu Li Yongkun Zhang Ye Zhang Ruqing Zhang Qingliang Li Danxi Li Wei Shangguan Yongjiu Dai 75 2 0 12 Jun 2024
Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults Tongze Zhang Tammy Chung Anind Dey Sang Won Bae 78 3 0 22 Apr 2024
CLIP in Medical Imaging: A Comprehensive Survey Zihao Zhao Yuxiao Liu Han Wu Yonghao Li Sheng Wang L. Teng Disheng Liu Zhiming Cui Qian Wang Dinggang Shen CLIP MedIm LM&MA VLM 145 43 0 12 Dec 2023
The role of causality in explainable artificial intelligence Gianluca Carloni Andrea Berti Sara Colantonio CML XAI 72 12 0 18 Sep 2023
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities Munib Mesinovic Peter Watkinson Ting Zhu FaML 64 3 0 16 Aug 2023
Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted? A. Brankovic David Cook Jessica Rahman Wenjie Huang Sankalp Khanna 66 1 0 21 Jun 2023
Interpreting Deep Neural Networks with the Package innsight Niklas Koenen Marvin N. Wright FAtt 104 6 0 19 Jun 2023
Echoes of Biases: How Stigmatizing Language Affects AI Performance Yizhi Liu Weiguang Wang G. Gao Ritu Agarwal 61 2 0 17 May 2023
NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients Tucker Stewart Katherine Stern G. O’Keefe Ankur Teredesai Juhua Hu 26 0 0 25 Apr 2023
MLOps Spanning Whole Machine Learning Life Cycle: A Survey Fang Zhengxin Yuan Yi Zhang Jingyu Liu Yue Mu Yuechen ... Xu Xiwei Wang Jeff Wang Chen Zhang Shuai Chen Shiping 49 4 0 13 Apr 2023
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When? M. Rubaiyat Hossain Mondal Prajoy Podder 86 64 0 10 Apr 2023
A Brief Review of Explainable Artificial Intelligence in Healthcare Zahra Sadeghi R. Alizadehsani M. Cifci Samina Kausar Rizwan Rehman ... A. Shoeibi H. Moosaei Milan Hladík Saeid Nahavandi P. Pardalos 23 14 0 04 Apr 2023
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges T. Shaik Xiaohui Tao Niall Higgins Lin Li R. Gururajan Xujuan Zhou U. Acharya 76 209 0 19 Jan 2023
Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research Akm Bahalul Haque A. Najmul Islam Patrick Mikalef XAI 56 106 0 24 Nov 2022
Using explainability to design physics-aware CNNs for solving subsurface inverse problems J. Crocker Krishna Kumar B. Cox 69 9 0 16 Nov 2022
WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values Amin Nayebi Sindhu Tipirneni Chandan K. Reddy Brandon Foreman V. Subbian FAtt AI4TS 43 22 0 11 Nov 2022
Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria Theresa Bender J. Beinecke D. Krefting Carolin Müller Henning Dathe T. Seidler Nicolai Spicher Anne-Christin Hauschild FAtt 36 27 0 03 Nov 2022
Explainable AI for clinical and remote health applications: a survey on tabular and time series data Flavio Di Martino Franca Delmastro AI4TS 64 98 0 14 Sep 2022
Modelling Patient Trajectories Using Multimodal Information J. F. Silva S. Matos 40 7 0 09 Sep 2022
Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making Mingxuan Liu Yilin Ning Han Yuan M. Ong Nan Liu FAtt 35 1 0 08 Jun 2022
Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare Marissa Radensky Dustin Burson Rajya Bhaiya Daniel S. Weld 61 0 0 27 Apr 2022
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models Han Yuan Mingxuan Liu Lican Kang Chenkui Miao Ying Wu FAtt 56 8 0 24 Apr 2022
Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation Juan Lu R. Hutchens J. Hung M. Bennamoun B. McQuillan ... K. Murray J. Stewart B. Chow Frank M. Sanfilippo Girish Dwivedi 28 17 0 02 Feb 2022
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records Shuai Niu Qing Yin Yunya Song Yike Guo Xian Yang 51 13 0 18 Jan 2022
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review F. Giuste Wenqi Shi Yuanda Zhu Tarun Naren Monica Isgut Ying Sha L. Tong Mitali S. Gupte May D. Wang 94 74 0 23 Dec 2021
IAC: A Framework for Enabling Patient Agency in the Use of AI-Enabled Healthcare Chinasa T. Okolo Michelle González Amador 35 0 0 29 Oct 2021
Simple black-box universal adversarial attacks on medical image classification based on deep neural networks K. Koga Kazuhiro Takemoto AAML 60 12 0 11 Aug 2021
Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff Michael J. Naylor C. French Samantha R. Terker Uday Kamath 82 10 0 12 Jul 2021
Offline reinforcement learning with uncertainty for treatment strategies in sepsis Ran Liu J. Greenstein J. Fackler Jules Bergmann M. Bembea R. Winslow OffRL 117 7 0 09 Jul 2021
Explainable AI, but explainable to whom? Julie Gerlings Millie Søndergaard Jensen Arisa Shollo 71 43 0 10 Jun 2021
Coalitional strategies for efficient individual prediction explanation Gabriel Ferrettini Elodie Escriva Julien Aligon Jean-Baptiste Excoffier C. Soulé-Dupuy 61 18 0 01 Apr 2021
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond Guang Yang Qinghao Ye Jun Xia 141 504 0 03 Feb 2021
The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards S. Lauritsen B. Thiesson Marianne Johansson Jørgensen A. Riis U. Espelund J. Weile Jeppe Lange 62 3 0 26 Jan 2021
Reviewing the Need for Explainable Artificial Intelligence (xAI) Julie Gerlings Arisa Shollo Ioanna D. Constantiou 61 73 0 02 Dec 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models Christoph Molnar Gunnar Konig J. Herbinger Timo Freiesleben Susanne Dandl Christian A. Scholbeck Giuseppe Casalicchio Moritz Grosse-Wentrup B. Bischl FAtt AI4CE 85 138 0 08 Jul 2020
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review F. Sado C. K. Loo W. S. Liew Matthias Kerzel S. Wermter 75 52 0 21 Apr 2020