Predicting Infections Using Computational Intelligence – A Systematic Review
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 th...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8990083/ |
| _version_ | 1818875821233799168 |
|---|---|
| author | Alejandro Baldominos Adrian Puello Hasan Ogul Tunc Asuroglu Ricardo Colomo-Palacios |
| author_facet | Alejandro Baldominos Adrian Puello Hasan Ogul Tunc Asuroglu Ricardo Colomo-Palacios |
| author_sort | Alejandro Baldominos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T13:32:35Z |
| format | Article |
| id | doaj.art-69998f20dd074d78a4a3ac7aaa1dec46 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:32:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-69998f20dd074d78a4a3ac7aaa1dec462022-12-21T20:19:18ZengIEEEIEEE Access2169-35362020-01-018310833110210.1109/ACCESS.2020.29730068990083Predicting Infections Using Computational Intelligence – A Systematic ReviewAlejandro Baldominos0https://orcid.org/0000-0002-8906-7572Adrian Puello1https://orcid.org/0000-0002-3003-3212Hasan Ogul2https://orcid.org/0000-0002-6043-1860Tunc Asuroglu3https://orcid.org/0000-0003-4153-0764Ricardo Colomo-Palacios4https://orcid.org/0000-0002-1555-9726Computer Science Department, Universidad Carlos III de Madrid, Leganés, SpainMedical Research Department, Dominican Republic National Cancer Institute, Santo Domingo, Dominican RepublicFaculty of Computer Science, Østfold University College, Halden, NorwayDepartment of Computer Engineering, Başkent University, Başlca Kampüsü, Ankara, TurkeyFaculty of Computer Science, Østfold University College, Halden, NorwayInfections 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.https://ieeexplore.ieee.org/document/8990083/Computational intelligenceexpert systemsinfection predictionmachine learningphysiological signalssystematic literature review |
| spellingShingle | Alejandro Baldominos Adrian Puello Hasan Ogul Tunc Asuroglu Ricardo Colomo-Palacios Predicting Infections Using Computational Intelligence – A Systematic Review IEEE Access Computational intelligence expert systems infection prediction machine learning physiological signals systematic literature review |
| title | Predicting Infections Using Computational Intelligence – A Systematic Review |
| title_full | Predicting Infections Using Computational Intelligence – A Systematic Review |
| title_fullStr | Predicting Infections Using Computational Intelligence – A Systematic Review |
| title_full_unstemmed | Predicting Infections Using Computational Intelligence – A Systematic Review |
| title_short | Predicting Infections Using Computational Intelligence – A Systematic Review |
| title_sort | predicting infections using computational intelligence x2013 a systematic review |
| topic | Computational intelligence expert systems infection prediction machine learning physiological signals systematic literature review |
| url | https://ieeexplore.ieee.org/document/8990083/ |
| work_keys_str_mv | AT alejandrobaldominos predictinginfectionsusingcomputationalintelligencex2013asystematicreview AT adrianpuello predictinginfectionsusingcomputationalintelligencex2013asystematicreview AT hasanogul predictinginfectionsusingcomputationalintelligencex2013asystematicreview AT tuncasuroglu predictinginfectionsusingcomputationalintelligencex2013asystematicreview AT ricardocolomopalacios predictinginfectionsusingcomputationalintelligencex2013asystematicreview |