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Article ; Online: ChartWalk: Navigating large collections of text notes in electronic health records for clinical chart review.

Sultanum, Nicole / Naeem, Farooq / Brudno, Michael / Chevalier, Fanny

IEEE transactions on visualization and computer graphics

2022 Volume 29, Issue 1, Page(s) 1244–1254

Abstract: Before seeing a patient for the first time, healthcare workers will typically conduct a comprehensive clinical chart review of the patient's electronic health record (EHR). Within the diverse documentation pieces included there, text notes are among the ... ...

Abstract	Before seeing a patient for the first time, healthcare workers will typically conduct a comprehensive clinical chart review of the patient's electronic health record (EHR). Within the diverse documentation pieces included there, text notes are among the most important and thoroughly perused segments for this task; and yet they are among the least supported medium in terms of content navigation and overview. In this work, we delve deeper into the task of clinical chart review from a data visualization perspective and propose a hybrid graphics+text approach via ChartWalk, an interactive tool to support the review of text notes in EHRs. We report on our iterative design process grounded in input provided by a diverse range of healthcare professionals, with steps including: (a) initial requirements distilled from interviews and the literature, (b) an interim evaluation to validate design decisions, and (c) a task-based qualitative evaluation of our final design. We contribute lessons learned to better support the design of tools not only for clinical chart reviews but also other healthcare-related tasks around medical text analysis.
MeSH term(s)	Humans ; Electronic Health Records ; Computer Graphics ; Data Visualization
Language	English
Publishing date	2022-12-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2022.3209444
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Rabindranath, Madhumitha / Naghibzadeh, Maryam / Zhao, Xun / Holdsworth, Sandra / Brudno, Michael / Sidhu, Aman / Bhat, Mamatha

Transplantation

2023

Abstract: Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: ... ...

Abstract	Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.
Language	English
Publishing date	2023-12-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	208424-7
ISSN	1534-6080 ; 0041-1337
ISSN (online)	1534-6080
ISSN	0041-1337
DOI	10.1097/TP.0000000000004876
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Article ; Online: Assessment of Machine Learning-Based Medical Directives to Expedite Care in Pediatric Emergency Medicine.

Singh, Devin / Nagaraj, Sujay / Mashouri, Pouria / Drysdale, Erik / Fischer, Jason / Goldenberg, Anna / Brudno, Michael

JAMA network open

2022 Volume 5, Issue 3, Page(s) e222599

Abstract: Importance: Increased wait times and long lengths of stay in emergency departments (EDs) are associated with poor patient outcomes. Systems to improve ED efficiency would be useful. Specifically, minimizing the time to diagnosis by developing novel ... ...

Abstract	Importance: Increased wait times and long lengths of stay in emergency departments (EDs) are associated with poor patient outcomes. Systems to improve ED efficiency would be useful. Specifically, minimizing the time to diagnosis by developing novel workflows that expedite test ordering can help accelerate clinical decision-making. Objective: To explore the use of machine learning-based medical directives (MLMDs) to automate diagnostic testing at triage for patients with common pediatric ED diagnoses. Design, setting, and participants: Machine learning models trained on retrospective electronic health record data were evaluated in a decision analytical model study conducted at the ED of the Hospital for Sick Children Toronto, Canada. Data were collected on all patients aged 0 to 18 years presenting to the ED from July 1, 2018, to June 30, 2019 (77 219 total patient visits). Exposure: Machine learning models were trained to predict the need for urinary dipstick testing, electrocardiogram, abdominal ultrasonography, testicular ultrasonography, bilirubin level testing, and forearm radiographs. Main outcomes and measures: Models were evaluated using area under the receiver operator curve, true-positive rate, false-positive rate, and positive predictive values. Model decision thresholds were determined to limit the total number of false-positive results and achieve high positive predictive values. The time difference between patient triage completion and test ordering was assessed for each use of MLMD. Error rates were analyzed to assess model bias. In addition, model explainability was determined using Shapley Additive Explanations values. Results: There was a total of 42 238 boys (54.7%) included in model development; mean (SD) age of the children was 5.4 (4.8) years. Models obtained high area under the receiver operator curve (0.89-0.99) and positive predictive values (0.77-0.94) across each of the use cases. The proposed implementation of MLMDs would streamline care for 22.3% of all patient visits and make test results available earlier by 165 minutes (weighted mean) per affected patient. Model explainability for each MLMD demonstrated clinically relevant features having the most influence on model predictions. Models also performed with minimal to no sex bias. Conclusions and relevance: The findings of this study suggest the potential for clinical automation using MLMDs. When integrated into clinical workflows, MLMDs may have the potential to autonomously order common ED tests early in a patient's visit with explainability provided to patients and clinicians.
MeSH term(s)	Adolescent ; Child ; Child, Preschool ; Emergency Service, Hospital ; Humans ; Infant ; Infant, Newborn ; Machine Learning ; Male ; Pediatric Emergency Medicine ; Retrospective Studies ; Triage/methods
Language	English
Publishing date	2022-03-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2022.2599
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Developing OCHROdb, a comprehensive quality checked database of open chromatin regions from sequencing data.

Shooshtari, Parisa / Feng, Samantha / Nelakuditi, Viswateja / Asakereh, Reza / Hosseini Naghavi, Nader / Foong, Justin / Brudno, Michael / Cotsapas, Chris

Scientific reports

2023 Volume 13, Issue 1, Page(s) 8106

Abstract: International consortia, including ENCODE, Roadmap Epigenomics, Genomics of Gene Regulation and Blueprint Epigenome have made large-scale datasets of open chromatin regions publicly available. While these datasets are extremely useful for studying ... ...

Abstract	International consortia, including ENCODE, Roadmap Epigenomics, Genomics of Gene Regulation and Blueprint Epigenome have made large-scale datasets of open chromatin regions publicly available. While these datasets are extremely useful for studying mechanisms of gene regulation in disease and cell development, they only identify open chromatin regions in individual samples. A uniform comparison of accessibility of the same regulatory sites across multiple samples is necessary to correlate open chromatin accessibility and expression of target genes across matched cell types. Additionally, although replicate samples are available for majority of cell types, a comprehensive replication-based quality checking of individual regulatory sites is still lacking. We have integrated 828 DNase-I hypersensitive sequencing samples, which we have uniformly processed and then clustered their regulatory regions across all samples. We checked the quality of open-chromatin regions using our replication test. This has resulted in a comprehensive, quality-checked database of Open CHROmatin (OCHROdb) regions for 194 unique human cell types and cell lines which can serve as a reference for gene regulatory studies involving open chromatin. We have made this resource publicly available: users can download the whole database, or query it for their genomic regions of interest and visualize the results in an interactive genome browser.
MeSH term(s)	Humans ; Chromatin/genetics ; Gene Expression Regulation ; Genomics ; Regulatory Sequences, Nucleic Acid ; Epigenomics/methods
Chemical Substances	Chromatin
Language	English
Publishing date	2023-05-18
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-26791-x
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Article: The promises and challenges of clinical AI in community paediatric medicine.

Singh, Devin / Nagaraj, Sujay / Daniel, Ryan / Flood, Colleen / Kulik, Dina / Flook, Robert / Goldenberg, Anna / Brudno, Michael / Stedman, Ian

Paediatrics & child health

2023 Volume 28, Issue 4, Page(s) 212–217

Abstract: The widespread adoption of virtual care technologies has quickly reshaped healthcare operations and delivery, particularly in the context of community medicine. In this paper, we use the virtual care landscape as a point of departure to envision the ... ...

Abstract	The widespread adoption of virtual care technologies has quickly reshaped healthcare operations and delivery, particularly in the context of community medicine. In this paper, we use the virtual care landscape as a point of departure to envision the promises and challenges of artificial intelligence (AI) in healthcare. Our analysis is directed towards community care practitioners interested in learning more about how AI can change their practice along with the critical considerations required to integrate AI into their practice. We highlight examples of how AI can enable access to new sources of clinical data while augmenting clinical workflows and healthcare delivery. AI can help optimize how and when care is delivered by community practitioners while also improving practice efficiency, accessibility, and the overall quality of care. Unlike virtual care, however, AI is still missing many of the key enablers required to facilitate adoption into the community care landscape and there are challenges we must consider and resolve for AI to successfully improve healthcare delivery. We discuss several critical considerations, including data governance in the clinic setting, healthcare practitioner education, regulation of AI in healthcare, clinician reimbursement, and access to both technology and the internet.
Language	English
Publishing date	2023-03-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2106767-3
ISSN	1918-1485 ; 1205-7088
ISSN (online)	1918-1485
ISSN	1205-7088
DOI	10.1093/pch/pxac080
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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Shortcut learning in medical AI hinders generalization: method for estimating AI model generalization without external data.

Ong Ly, Cathy / Unnikrishnan, Balagopal / Tadic, Tony / Patel, Tirth / Duhamel, Joe / Kandel, Sonja / Moayedi, Yasbanoo / Brudno, Michael / Hope, Andrew / Ross, Heather / McIntosh, Chris

NPJ digital medicine

2024 Volume 7, Issue 1, Page(s) 124

Abstract: Healthcare datasets are becoming larger and more complex, necessitating the development of accurate and generalizable AI models for medical applications. Unstructured datasets, including medical imaging, electrocardiograms, and natural language data, are ...

Abstract	Healthcare datasets are becoming larger and more complex, necessitating the development of accurate and generalizable AI models for medical applications. Unstructured datasets, including medical imaging, electrocardiograms, and natural language data, are gaining attention with advancements in deep convolutional neural networks and large language models. However, estimating the generalizability of these models to new healthcare settings without extensive validation on external data remains challenging. In experiments across 13 datasets including X-rays, CTs, ECGs, clinical discharge summaries, and lung auscultation data, our results demonstrate that model performance is frequently overestimated by up to 20% on average due to shortcut learning of hidden data acquisition biases (DAB). Shortcut learning refers to a phenomenon in which an AI model learns to solve a task based on spurious correlations present in the data as opposed to features directly related to the task itself. We propose an open source, bias-corrected external accuracy estimate, P
Language	English
Publishing date	2024-05-14
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-024-01118-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Automatically disambiguating medical acronyms with ontology-aware deep learning.

Skreta, Marta / Arbabi, Aryan / Wang, Jixuan / Drysdale, Erik / Kelly, Jacob / Singh, Devin / Brudno, Michael

Nature communications

2021 Volume 12, Issue 1, Page(s) 5319

Abstract: Modern machine learning (ML) technologies have great promise for automating diverse clinical and research workflows; however, training them requires extensive hand-labelled datasets. Disambiguating abbreviations is important for automated clinical note ... ...

Abstract	Modern machine learning (ML) technologies have great promise for automating diverse clinical and research workflows; however, training them requires extensive hand-labelled datasets. Disambiguating abbreviations is important for automated clinical note processing; however, broad deployment of ML for this task is restricted by the scarcity and imbalance of labeled training data. In this work we present a method that improves a model's ability to generalize through novel data augmentation techniques that utilizes information from biomedical ontologies in the form of related medical concepts, as well as global context information within the medical note. We train our model on a public dataset (MIMIC III) and test its performance on automatically generated and hand-labelled datasets from different sources (MIMIC III, CASI, i2b2). Together, these techniques boost the accuracy of abbreviation disambiguation by up to 17% on hand-labeled data, without sacrificing performance on a held-out test set from MIMIC III.
MeSH term(s)	Biomedical Research ; Data Mining/methods ; Datasets as Topic ; Deep Learning ; Humans ; Terminology as Topic
Language	English
Publishing date	2021-09-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-25578-4
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Article: An introduction to the Lagan alignment toolkit.

Brudno, Michael

Methods in molecular biology (Clifton, N.J.)

2007 Volume 395, Page(s) 205–220

Abstract: The Lagan Toolkit is a software package for comparison of genomic sequences. It includes the CHAOS local alignment program, LAGAN global alignment program for two, or more sequences and Shuffle-LAGAN, a "glocal" alignment method that handles genomic ... ...

Abstract	The Lagan Toolkit is a software package for comparison of genomic sequences. It includes the CHAOS local alignment program, LAGAN global alignment program for two, or more sequences and Shuffle-LAGAN, a "glocal" alignment method that handles genomic rearrangements in a global alignment framework. The alignment programs included in the Lagan Toolkit have been widely used to compare genomes of many organisms, from bacteria to large mammalian genomes. This chapter provides an overview of the algorithms used by the LAGAN programs to construct genomic alignments, explains how to build alignments using either the standalone program or the web server, and discusses some of the common pitfalls users encounter when using the toolkit.
MeSH term(s)	Algorithms ; Databases, Genetic ; Genome ; Internet ; Sequence Alignment
Language	English
Publishing date	2007-11-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	1064-3745
ISSN	1064-3745
DOI	10.1007/978-1-59745-514-5_13
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Doccurate: A Curation-Based Approach for Clinical Text Visualization.

Sultanum, Nicole / Singh, Devin / Brudno, Michael / Chevalier, Fanny

IEEE transactions on visualization and computer graphics

2018

Abstract: Before seeing a patient, physicians seek to obtain an overview of the patient's medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient ... ...

Abstract	Before seeing a patient, physicians seek to obtain an overview of the patient's medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient charts grow too large. Text visualization methods have been widely explored to manage this large scale through visual summaries that rely on information retrieval algorithms to structure text and make it amenable to visualization. However, the integration with such automated approaches comes with a number of limitations, including significant error rates and the need for healthcare providers to fine-tune algorithms without expert knowledge of their inner mechanics. In addition, several of these approaches obscure or substitute the original clinical text and therefore fail to leverage qualitative and rhetorical flavours of the clinical notes. These drawbacks have limited the adoption of text visualization and other summarization technologies in clinical practice. In this work we present Doccurate, a novel system embodying a curation-based approach for the visualization of large clinical text datasets. Our approach offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text. We discuss findings of a formal qualitative evaluation conducted with 6 domain experts, shedding light onto physicians' information needs, perceived strengths and limitations of automated tools, and the importance of customization while balancing efficiency. We also present use case scenarios to showcase Doccurate's envisioned usage in practice.
Language	English
Publishing date	2018-08-20
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2018.2864905
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Article ; Online: PhenoPad: Building AI enabled note-taking interfaces for patient encounters.

Wang, Jixuan / Yang, Jingbo / Zhang, Haochi / Lu, Helen / Skreta, Marta / Husić, Mia / Arbabi, Aryan / Sultanum, Nicole / Brudno, Michael

NPJ digital medicine

2022 Volume 5, Issue 1, Page(s) 12

Abstract: Current clinical note-taking approaches cannot capture the entirety of information available from patient encounters and detract from patient-clinician interactions. By surveying healthcare providers' current note-taking practices and attitudes toward ... ...

Abstract	Current clinical note-taking approaches cannot capture the entirety of information available from patient encounters and detract from patient-clinician interactions. By surveying healthcare providers' current note-taking practices and attitudes toward new clinical technologies, we developed a patient-centered paradigm for clinical note-taking that makes use of hybrid tablet/keyboard devices and artificial intelligence (AI) technologies. PhenoPad is an intelligent clinical note-taking interface that captures free-form notes and standard phenotypic information via a variety of modalities, including speech and natural language processing techniques, handwriting recognition, and more. The output is unobtrusively presented on mobile devices to clinicians for real-time validation and can be automatically transformed into digital formats that would be compatible with integration into electronic health record systems. Semi-structured interviews and trials in clinical settings rendered positive feedback from both clinicians and patients, demonstrating that AI-enabled clinical note-taking under our design improves ease and breadth of information captured during clinical visits without compromising patient-clinician interactions. We open source a proof-of-concept implementation that can lay the foundation for broader clinical use cases.
Language	English
Publishing date	2022-01-27
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-021-00555-9
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