Introduction to Machine Learning in Digital Healthcare Epidemiology

Roth, Jan A; Battegay, Manuel; Juchler, Fabrice; Vogt, Julia E.; Widmer, Andreas F.

doi:10.1017/ice.2018.265

Cited by 70 publications

(41 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was recently pointed out that "methodological, ethical, and data security standards", when investigating ML and its application in healthcare, are greatly needed [5]. Looking at the heterogeneity of the articles included in our review we fully support this statement.…”

Section: Reporting Standardssupporting

confidence: 66%

See 1 more Smart Citation

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

Luz

Vollmer

Decruyenaere

et al. 2020

Clinical Microbiology and Infection

View full text Add to dashboard Cite

Background: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. Objectives: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. Sources: A Medline search was performed with the keywords artificial intelligence, machine learning, infection*, and infectious disease* for the years 2014e2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Content: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n ¼ 19), hospital-acquired infections (n ¼ 11), surgical site infections and other postoperative infections (n ¼ 11), microbiological test results (n ¼ 4), infections in general (n ¼ 2), musculoskeletal infections (n ¼ 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n ¼ 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Implications: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.

show abstract

Section: Reporting Standardssupporting

confidence: 66%

“…While unsupervised learning provides methods for clustering data, supervised learning is focused on classification. A detailed introduction to the background of ML in health care has recently been published [5].…”

Section: Introductionmentioning

confidence: 99%

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

Luz

Vollmer

Decruyenaere

et al. 2020

Clinical Microbiology and Infection

View full text Add to dashboard Cite

show abstract

“…11 Machine learning (ML), which lies at the intersection of computer science and statistics, provides such computationally powerful tools for the analysis of large and heterogeneous data sets and has become increasingly popular in different domains in the last decade, including medicine. [12][13][14][15][16][17] These methods are capable of analyzing large datasets almost in real time. This provides the opportunity to leverage ML in clinical pharmacology, and the combination of ML with PMX might lead to great scientific achievements.…”

mentioning

confidence: 99%

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

Koch

Pfister

Daunhawer

et al. 2020

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well‐recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

show abstract

“…We used three different (machine) learning algorithms to model artifacts in invasive blood pressure data, i.e. lasso penalized logistic regression, a single layer neural network and a support vector machine [12]. First, we optimized the chosen learning algorithm.…”

Section: Learning Algorithmsmentioning

confidence: 99%

Artifacts annotations in anesthesia blood pressure data by man and machine

Pasma

Wesselink

Buuren

et al. 2020

J Clin Monit Comput

View full text Add to dashboard Cite

Physiologic data from anesthesia monitors are automatically captured. Yet erroneous data are stored in the process as well. While this is not interfering with clinical care, research can be affected. Researchers should find ways to remove artifacts. The aim of the present study was to compare different artifact annotation strategies, and to assess if a machine learning algorithm is able to accept or reject individual data points. Non-cardiac procedures requiring invasive blood pressure monitoring were eligible. Two trained research assistants observed procedures live for artifacts. The same procedures were also retrospectively annotated for artifacts by a different person. We compared the different ways of artifact identifications and modelled artifacts with three different learning algorithms (lasso restrictive logistic regression, neural network and support vector machine). In 88 surgical procedures including 5711 blood pressure data points, the live observed incidence of artifacts was 2.1% and the retrospective incidence was 2.2%. Comparing retrospective with live annotation revealed a sensitivity of 0.32 and specificity of 0.98. The performance of the learning algorithms which we applied ranged from poor (kappa 0.053) to moderate (kappa 0.651). Manual identification of artifacts yielded different incidences in different situations, which were not comparable. Artifact detection in physiologic data collected during anesthesia could be automated, but the performance of the learning algorithms in the present study remained moderate. Future research should focus on optimization and finding ways to apply them with minimal manual work. The present study underlines the importance of an explicit definition for artifacts in database research.

show abstract

Introduction to Machine Learning in Digital Healthcare Epidemiology

Cited by 70 publications

References 46 publications

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

Artifacts annotations in anesthesia blood pressure data by man and machine

Contact Info

Product

Resources

About