Personalized machine learning approach to predict candidemia in medical wards

Ripoli, Andrea; Sozio, Emanuela; Sbrana, Francesco; Bertolino, Giacomo; Pallotto, Carlo; Cardinali, Gianluigi; Meini, Simone; Pieralli, Filippo; Azzini, Anna Maria; Concia, Ercole; Viaggi, Bruno; Tascini, Carlo

doi:10.1007/s15010-020-01488-3

Cited by 24 publications

(23 citation statements)

References 47 publications

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“…Different features including medical history, current complaints, measurement of vital signs, baseline testing for blood count, kidney and liver function tests, and inflammatory markers were selected in this work and SVM achieved the best performance and the best accuracy rates of 0.92%. Random forest regression, one of the best machine learning algorithms, is applied by Ripoli et al (2020) [48], to envisage the candidemia possibility among patients admitted in IMWs. They collected demographic and clinical data of patients and reached C-statistics = 0.874 ± 0.003, sensitivity=84.24% ± 0.67%, and specificity =91% ± 2.63%.In their work, in-hospital MHIA therapy has been proven a frequent risk factor for candidemia as well as other factors such as TPN, previous hospitalization, previous antibiotic therapy, and CVC or PICC.5.…”

Section: Literature Reviewmentioning

confidence: 99%

The prediction of mortality influential variables in an intensive care unit: a case study

Khajehali

Tarokh

2021

Pers Ubiquit Comput

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The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients' demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.

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Section: Literature Reviewmentioning

confidence: 99%

The prediction of mortality influential variables in an intensive care unit: a case study

Khajehali

Tarokh

2021

Pers Ubiquit Comput

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“…It has been widely used in the medical field, from radiology to surgery, from oncology to intensive care [5][6][7][8][9]. Supervised machine learning-based systems have been employed to predict patient deterioration risk [10,11], heart failure onset [12,13], acute kidney injury [14], delirium [15], sepsis [16][17][18][19] and mortality [20,21]. Unsupervised ML, on the other hand, has been used to analyze, cluster and manage large amounts of data that lie beyond clinicians' ability to handle them.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Antibiotic Management

et al. 2022

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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.

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“…Computer science and data analysis technology provide new methods and ideas for the prediction of hypertension outcomes. On the one hand, machine learning, data mining, and information sciences have been widely used in various fields of medicine and achieved good results [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], providing technical support for this study. On the other hand, the application of electronic medical records and databases, automatic and electronic medical equipment, and increasing emphasis placed on medical data by hospitals and medical institutions all promote the digitalization of medical information.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost

Chang

Xiao

et al. 2021

Diagnostics

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For patients with hypertension, serious complications, such as myocardial infarction, a common cause of heart failure, occurs in the late stage of hypertension. Hypertension outcomes can lead to complications, including death. Hypertension outcomes threaten patients’ lives and need to be predicted. In our research, we reviewed the hypertension medical data from a tertiary-grade A class hospital in Beijing, and established a hypertension outcome prediction model with the machine learning theory. We first proposed a gain sequence forward tabu search feature selection (GSFTS-FS) method, which can search the optimal combination of medical variables that affect hypertension outcomes. Based on this, the XGBoost algorithm established a prediction model because of its good stability. We verified the proposed method by comparing other commonly used models in similar works. The proposed GSFTS-FS improved the performance by about 10%. The proposed prediction method has the best performance and its AUC value, accuracy, F1 value, and recall of 10-fold cross-validation were 0.96. 0.95, 0.88, and 0.82, respectively. It also performed well on test datasets with 0.92, 0.94, 0.87, and 0.80 for AUC, accuracy, F1, and recall, respectively. Therefore, the XGBoost with GSFTS-FS can accurately and effectively predict the occurrence of outcomes for patients with hypertension, and can provide guidance for doctors in clinical diagnoses and medical decision-making.

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Personalized machine learning approach to predict candidemia in medical wards

Cited by 24 publications

References 47 publications

The prediction of mortality influential variables in an intensive care unit: a case study

The prediction of mortality influential variables in an intensive care unit: a case study

Machine Learning and Antibiotic Management

Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost

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