Machine Learning and Antibiotic Management

Abstract

Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from "very low" to "very high"). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers.

Keywords: antibiotic therapy; clustering analysis; fuzzy logic; intensive care unit; machine learning.

Figure 1

Example of a fuzzy domain. Fuzzy subsets (named from F1 to F5) are represented as geometrical graphical charts.

Figure 2

An example of 14 monitoring hours with a sample of the monitoring parameters in a patient with normalized (fuzzified) data is presented. The dimensional view gives information on the data flow for each parameter during the observation period. Height of the curves in the chart are in units representing the normalized values for each parameter.

Figure 3

Changes in antimicrobial therapy clusters in a random patient in the population. Respective monitoring clusters in the same hour are charted. In the sample of 14 h here proposed the patient is in therapy cluster 1 (blue bars) for 7 h and starting at hour 8 is in cluster 2. Next to blue bars are red bars for monitoring clusters in the same hours.