Semi-supervised approach to event time annotation using longitudinal electronic health records

Liang, Liang; Hou, Jue; Uno, Hajime; Cho, Kelly; Cai, Tianxi

doi:10.1007/s10985-022-09557-5

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Further work in semi- and weakly-supervised deep learning methods is necessary. [143,144] Moreover, given the privacy constraints associated with EHRs and other health data sources, leveraging interoperable and multimodal data calls for advancements in federated learning methods that can accommodate distributed data sources stored locally across institutions. [145]…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

Yang

Varghese²,

Stephenson

et al. 2022

Preprint

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ObjectiveAccurate and rapid methods for phenotyping are a prerequisite to realizing the potential of electronic health records (EHRs) data for clinical and translational research. This study reviews the literature on machine learning (ML) approaches for phenotyping with respect to the phenotypes considered, the data sources and methods used, and the contributions within the wider context of EHR-based research.Materials and MethodsWe searched for relevant articles in PubMed and Web of Science published between January 1, 2018 and April 14, 2022. After screening, we collected data on 52 variables across 106 selected articles.ResultsML-based methods were developed for 156 unique phenotypes, primarily using EHR data from a single institution or health system. 72 of 106 articles leveraged unstructured data in clinical notes. In terms of methodology, supervised learning is the most prevalent ML paradigm (n = 64, 60.4%), with half of the articles employing deep learning. Semi-supervised and weakly-supervised approaches were applied to reduce the burden of obtaining gold-standard labeled data (n = 21, 19.8%), while unsupervised learning was used for phenotype discovery (n = 20, 18.9%). Federated learning has been applied to develop algorithms across multiple institutions while preserving data privacy (n = 2, 1.9%).DiscussionWhile the use of ML for phenotyping is growing, most articles applied traditional supervised ML to characterize the presence of common, chronic conditions.ConclusionContinued research in ML-based methods is warranted, with particular attention to the development of advanced methods for complex phenotypes and standards for reporting and evaluating phenotyping algorithms.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

Yang

Varghese²,

Stephenson

et al. 2022

Preprint

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“…SSL was used to determine true clinical event times such as time to cancer progression that are important for personalised prediction of risk and prescribing, but which are not readily available in large EHR datasets [ 49 ].…”

Section: Semi-supervised Learningmentioning

confidence: 99%

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Woodman,

Mangoni

2023

Aging Clin Exp Res

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The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

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“…Unsupervised [81,82], semisupervised [83,84], and supervised [85,86] Extraction of event time through incidence phenotyping…”

Section: Variable Curationmentioning

confidence: 99%

“…The longitudinal trajectories of EHR features (eg, diagnosis and procedures) relevant to the event of interest provide information on the event time through incidence phenotyping. Incidence phenotyping can be tackled using various unsupervised [81,82], semisupervised [83,84], and supervised [85,86] approaches.…”

Section: End Pointsmentioning

confidence: 99%

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies

Hou¹,

Zhao²,

Gronsbell³

et al. 2023

J Med Internet Res

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Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

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Semi-supervised approach to event time annotation using longitudinal electronic health records

Cited by 5 publications

References 29 publications

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies

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