Abstract
Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.
Highlights
Infectious diseases are the result of the invasion and multiplication of microorganisms in the body
In order to do so, we first enumerate the research questions that we want to answer through this study, describe the search strategy followed for retrieving source materials for the systematic literature review (SLR), explain the inclusion and exclusion criteria applied over those materials to filter out non-relevant works, and describe the process for extracting data for solving such research questions
In this paper we have designed and executed a systematic literature review (SLR) to find relevant works where machine learning and expert systems techniques are used for automatic diagnosis and prediction of infectious diseases
Summary
Infectious diseases are the result of the invasion and multiplication of microorganisms in the body. These microorganisms can be bacteria, viruses, fungi in the form of yeast, or any other microscopic organism. Infections can start anywhere and spread throughout the body. An infection can cause from fever to other health problems depending on the part of the body in which it occurs. Early diagnosis of an infection can allow medical teams to act quickly, providing a treatment (such as a prescription of antibiotics) that can revert the situation and stop the infection. Even if the outcome of the patient cannot be changed with medical care, early
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