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1.
J Am Med Inform Assoc ; 27(3): 343-354, 2020 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-31943009

RESUMO

OBJECTIVE: To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. MATERIALS AND METHODS: We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. RESULTS: HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. DISCUSSION: HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. CONCLUSION: We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.

2.
Am J Ophthalmol ; 208: 30-40, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31323204

RESUMO

PURPOSE: To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHRs). DESIGN: Development and evaluation of machine learning models. METHODS: Structured EHR data of 385 POAG patients from a single academic institution were incorporated into models using multivariable logistic regression, random forests, and artificial neural networks. Leave-one-out cross-validation was performed. Mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Youden index were calculated for each model to evaluate performance. Systemic variables driving predictions were identified and interpreted. RESULTS: Multivariable logistic regression was most effective at discriminating patients with progressive disease requiring surgery, with an AUC of 0.67. Higher mean systolic blood pressure was associated with significantly increased odds of needing glaucoma surgery (odds ratio [OR] = 1.09, P < .001). Ophthalmic medications (OR = 0.28, P < .001), non-opioid analgesic medications (OR = 0.21, P = .002), anti-hyperlipidemic medications (OR = 0.39, P = .004), macrolide antibiotics (OR = 0.40, P = .03), and calcium blockers (OR = 0.43, P = .03) were associated with decreased odds of needing glaucoma surgery. CONCLUSIONS: Existing systemic data in the EHR has some predictive value in identifying POAG patients at risk of progression to surgical intervention, even in the absence of eye-specific data. Blood pressure-related metrics and certain medication classes emerged as predictors of glaucoma progression. This approach provides an opportunity for future development of automated risk prediction within the EHR based on systemic data to assist with clinical decision-making.

3.
J Am Med Inform Assoc ; 26(5): 462-478, 2019 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-30907419

RESUMO

OBJECTIVES: To introduce healthcare or biomedical blockchain applications and their underlying blockchain platforms, compare popular blockchain platforms using a systematic review method, and provide a reference for selection of a suitable blockchain platform given requirements and technical features that are common in healthcare and biomedical research applications. TARGET AUDIENCE: Healthcare or clinical informatics researchers and software engineers who would like to learn about the important technical features of different blockchain platforms to design and implement blockchain-based health informatics applications. SCOPE: Covered topics include (1) a brief introduction to healthcare or biomedical blockchain applications and the benefits to adopt blockchain; (2) a description of key features of underlying blockchain platforms in healthcare applications; (3) development of a method for systematic review of technology, based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, to investigate blockchain platforms for healthcare and medicine applications; (4) a review of 21 healthcare-related technical features of 10 popular blockchain platforms; and (5) a discussion of findings and limitations of the review.

4.
BMC Med ; 17(1): 68, 2019 03 27.
Artigo em Inglês | MEDLINE | ID: mdl-30914045

RESUMO

Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin - the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as 'revolutionary' and 'disruptive', is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is 'fit-for-purpose' is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.


Assuntos
Tecnologia Biomédica , Redes de Comunicação de Computadores , Assistência à Saúde/tendências , Sistemas de Informação Administrativa , Informática Médica , Tecnologia Biomédica/métodos , Tecnologia Biomédica/organização & administração , Tecnologia Biomédica/tendências , Redes de Comunicação de Computadores/organização & administração , Redes de Comunicação de Computadores/normas , Redes de Comunicação de Computadores/provisão & distribução , Redes de Comunicação de Computadores/tendências , Data Warehousing/métodos , Data Warehousing/tendências , Assistência à Saúde/métodos , Assistência à Saúde/organização & administração , Processamento Eletrônico de Dados/métodos , Processamento Eletrônico de Dados/organização & administração , Processamento Eletrônico de Dados/tendências , Utilização de Equipamentos e Suprimentos/organização & administração , Utilização de Equipamentos e Suprimentos/tendências , Ensaios de Triagem em Larga Escala/normas , Humanos , Sistemas de Informação Administrativa/normas , Sistemas de Informação Administrativa/tendências , Informática Médica/métodos , Informática Médica/organização & administração , Informática Médica/tendências , Registros Médicos/normas
5.
J Am Med Inform Assoc ; 26(5): 392-403, 2019 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-30892656

RESUMO

OBJECTIVE: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the "server" role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way. MATERIALS AND METHODS: We propose a framework that includes both server and "client" roles to preserve correctness. We adopt a blockchain network to obtain the benefits of decentralization, by alternating the roles for each site to ensure computational fairness. Also, we developed GloreChain (Grid Binary LOgistic REgression on Permissioned BlockChain) as a concrete example, and compared it to a centralized algorithm on 3 healthcare or genomic datasets to evaluate predictive correctness, number of learning iterations and execution time. RESULTS: GloreChain performs exactly the same as the centralized method in terms of correctness and number of iterations. It inherits the advantages of blockchain, at the cost of increased time to reach a consensus model. DISCUSSION: Our framework is general or flexible and can also address intrinsic challenges of blockchain networks. Further investigations will focus on higher-dimensional datasets, additional use cases, privacy-preserving quality concerns, and ethical, legal, and social implications. CONCLUSIONS: Our framework provides a promising potential for institutions to learn a predictive model based on healthcare or genomic data in a privacy-preserving and decentralized way.

6.
J Biomed Inform ; 82: 63-69, 2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29679685

RESUMO

BACKGROUND: Big clinical note datasets found in electronic health records (EHR) present substantial opportunities to train accurate statistical models that identify patterns in patient diagnosis and outcomes. However, near-to-exact duplication in note texts is a common issue in many clinical note datasets. We aimed to use a scalable algorithm to de-duplicate notes and further characterize the sources of duplication. METHODS: We use an approximation algorithm to minimize pairwise comparisons consisting of three phases: (1) Minhashing with Locality Sensitive Hashing; (2) a clustering method using tree-structured disjoint sets; and (3) classification of near-duplicates (exact copies, common machine output notes, or similar notes) via pairwise comparison of notes in each cluster. We use the Jaccard Similarity (JS) to measure similarity between two documents. We analyzed two big clinical note datasets: our institutional dataset and MIMIC-III. RESULTS: There were 1,528,940 notes analyzed from our institution. The de-duplication algorithm completed in 36.3 h. When the JS threshold was set at 0.7, the total number of clusters was 82,371 (total notes = 304,418). Among all JS thresholds, no clusters contained pairs of notes that were incorrectly clustered. When the JS threshold was set at 0.9 or 1.0, the de-duplication algorithm captured 100% of all random pairs with their JS at least as high as the set thresholds from the validation set. Similar performance was noted when analyzing the MIMIC-III dataset. CONCLUSIONS: We showed that among the EHR from our institution and from the publicly-available MIMIC-III dataset, there were a significant number of near-to-exact duplicated notes.


Assuntos
Coleta de Dados , Registros Eletrônicos de Saúde , Informática Médica/métodos , Algoritmos , Análise por Conglomerados , Computadores , Bases de Dados Factuais , Conjuntos de Dados como Assunto , Humanos , Aprendizado de Máquina , Processamento de Linguagem Natural , Obesidade Mórbida/diagnóstico , Obesidade Mórbida/epidemiologia , Reconhecimento Automatizado de Padrão
7.
J Am Med Inform Assoc ; 24(6): 1211-1220, 2017 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-29016974

RESUMO

Objectives: To introduce blockchain technologies, including their benefits, pitfalls, and the latest applications, to the biomedical and health care domains. Target Audience: Biomedical and health care informatics researchers who would like to learn about blockchain technologies and their applications in the biomedical/health care domains. Scope: The covered topics include: (1) introduction to the famous Bitcoin crypto-currency and the underlying blockchain technology; (2) features of blockchain; (3) review of alternative blockchain technologies; (4) emerging nonfinancial distributed ledger technologies and applications; (5) benefits of blockchain for biomedical/health care applications when compared to traditional distributed databases; (6) overview of the latest biomedical/health care applications of blockchain technologies; and (7) discussion of the potential challenges and proposed solutions of adopting blockchain technologies in biomedical/health care domains.


Assuntos
Segurança Computacional , Mineração de Dados , Informática Médica , Algoritmos , Comércio , Confidencialidade , Mineração de Dados/economia
8.
AMIA Jt Summits Transl Sci Proc ; 2016: 225-34, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27570676

RESUMO

Recommendation of related articles is an important feature of the PubMed. The PubMed Related Citations (PRC) algorithm is the engine that enables this feature, and it leverages information on 22 million citations. We analyzed the performance of the PRC algorithm on 4584 annotated articles from the 2005 Text REtrieval Conference (TREC) Genomics Track data. Our analysis indicated that the PRC highest weighted term was not always consistent with the critical term that was most directly related to the topic of the article. We implemented term expansion and found that it was a promising and easy-to-implement approach to improve the performance of the PRC algorithm for the TREC 2005 Genomics data and for the TREC 2014 Clinical Decision Support Track data. For term expansion, we trained a Skip-gram model using the Word2Vec package. This extended PRC algorithm resulted in higher average precision for a large subset of articles. A combination of both algorithms may lead to improved performance in related article recommendations.

10.
BMC Bioinformatics ; 17 Suppl 1: 1, 2016 Jan 11.
Artigo em Inglês | MEDLINE | ID: mdl-26817711

RESUMO

BACKGROUND: Numerous publicly available biomedical databases derive data by curating from literatures. The curated data can be useful as training examples for information extraction, but curated data usually lack the exact mentions and their locations in the text required for supervised machine learning. This paper describes a general approach to information extraction using curated data as training examples. The idea is to formulate the problem as cost-sensitive learning from noisy labels, where the cost is estimated by a committee of weak classifiers that consider both curated data and the text. RESULTS: We test the idea on two information extraction tasks of Genome-Wide Association Studies (GWAS). The first task is to extract target phenotypes (diseases or traits) of a study and the second is to extract ethnicity backgrounds of study subjects for different stages (initial or replication). Experimental results show that our approach can achieve 87% of Precision-at-2 (P@2) for disease/trait extraction, and 0.83 of F1-Score for stage-ethnicity extraction, both outperforming their cost-insensitive baseline counterparts. CONCLUSIONS: The results show that curated biomedical databases can potentially be reused as training examples to train information extractors without expert annotation or refinement, opening an unprecedented opportunity of using "big data" in biomedical text mining.


Assuntos
/métodos , Curadoria de Dados , Mineração de Dados/métodos , Bases de Dados Factuais , Doença/genética , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Medição de Risco
11.
AMIA Annu Symp Proc ; 2016: 1880-1889, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-28269947

RESUMO

Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.


Assuntos
Registros Eletrônicos de Saúde , Armazenamento e Recuperação da Informação/métodos , Processamento de Linguagem Natural , Coleta de Dados , Humanos
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