Infection Control & Hospital Epidemiology
1459
Fig. 4. Illustration of an unsupervised clustering task; a model finds similar data points and groups them together.
this type of analysis are subtype detection of patients with hospital-acquired infections and finding similar patient sub- groups to assign patients to clinical trials. Typical machine- learning methods used for unsupervised learning are k-means clustering or probabilistic Gaussian mixture models. Machine-learning techniques have become increasingly pop-
ular in the last years in the field of healthcare epidemiology due to the huge amount and diversity of routine electronic data that is available in healthcare. However, there still exists a gap between theoretical machine learning research and clinical research. Researchers developing novel machine learning techniques usually have a background in computer science, mathematics or physics. They perform cutting-edge research, develop novel algorithms, and may even apply them to healthcare data. How- ever, they do not have comprehensive knowledge of the data generating process in daily clinical routine. Healthcare profes- sionals, on the other hand, have a deep understanding of the clinical problems and of the quality of specific healthcare data. Today, they are often able to apply some machine learning methods by themselves using standard statistical software packages (eg, R). However, some healthcare professionals may not be aware of the underlying assumptions and limitations of the models, which might lead to statistical unsound models or overfitted models. These 2 research areas complement each other; the advancement of digital healthcare epidemiology to a new level requires mutual understanding, communication, and collabora- tions between these fields.
Machine Learning: Recent Applications in Digital Healthcare Epidemiology
Numerous recent reports illustrate the first applications of machine learning in digital healthcare epidemiology, most fre- quently to make predictions based on routine healthcare data (Table 1). This goal is achievable by machine learning, particu- larly the analysis of large and diverse data assemblages (some- times involving thousands of variables), which could complicate more human-guided modeling approaches.7 In a prototypical, retrospective study based on electronic medical records data from the University of Michigan Hospitals
and the Massachusetts General Hospital, Oh et al20 used a data- driven approach to build hospital-specific models to estimate daily patient risk for Clostridioides difficile infection (CDI) using L2 regularized logistic regression. These machine-learning models were built and internally validated based on data from >150,000 adult admissions and involved several thousand time-invariant and time-varying variables; they resulted in a good predictive performance with areas under the receiver operating character- istic curve ranging from 0.75 to 0.82. At both institutions, the models identified half of true-positive cases at least 5 days prior to diagnosis of CDI. As part of a surveillance or decision support tool, these models could help to rapidly identify new cases of CDI, before microbiological test become available, or to select patients who should be tested for the presence of C. difficile. Furthermore, a range of institution-specific predictors were identified (eg, specific departments), which could stimulate additional investi- gations by health care epidemiologist to generate (causal) hypothesis about risk factors for occurrence or spread of CDI. This study illustrates well the paradigm-shift in healthcare from building ‘one-size-fits-all’ prediction models toward the applica- tion of more patient-centered analytical approaches that may result in many different data-driven prediction models. Such a flexible approach can incorporate heterogeneous and changing routine variables, which could complicate the development and application of prediction models that are generalizable across clinics; not all variables can be readily mapped when originating from different systems. Moreover, this approach allows an insti- tution to adjust their models during follow-up, and such flexibility is needed because variables and respective coding practices are subject to change in electronic medical records and because calibration drift may be observed for models derived from regression analysis and machine learning.21 Even using state-of-the-art machine learning algorithms based
on a wide array of potential predictors, some outcomes of interest may not be accurately predicted. This was illustrated recently in a retrospective study by Escobar et al22 of granular data from electronic medical records of 21 Kaiser Permanente Northern California hospitals. In this study, none of the conventional and machine learning models discriminated well for prediction of recurrent CDI. Such study results may exemplify that despite the extensive electronic medical records available in this study, rele- vant predictors may not be readily identified by machine learning or may not even be available in the records. Furthermore, the external validity and clinical effectiveness of
most machine-learning prediction models, like most prediction models and scores in medicine, are unclear. Especially for development and application of predictive models, models should be carefully evaluated in a way that mirrors clinical practice.23 Compared to conventional prediction models, machine learning is sometimes a ‘black box’ approach, such that selection of pre- dictor variables by machine-learning algorithms may not be transparent and can be counterintuitive.20 However, predictive modeling via machine learning (like other statistical techniques) does not require including only causal predictors, because accu- rate prediction models can be derived from an abundance of variables and proxy measures of causal factors that may not be causally related to the outcome of interest (eg, brain natriuretic peptide being a marker for heart failure). Compared to prediction tasks, little has been reported about
using machine learning in healthcare epidemiology to draw causal inferences and to identify independent risk factors (ie, causal modeling), which requires careful consideration of bias,
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