Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach

Alsinglawi, Belal; Alnajjar, Fady; Mubin, Omar; Novoa, Mauricio; Alorjani, Mohammed; Karajeh, Ola; Darwish, Omar

doi:10.1109/embc44109.2020.9175889

Cited by 32 publications

(38 citation statements)

References 19 publications

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“…Newborns and babies undergoing cardiac surgery [33] Babies admitted for gastroenteritis [43] Pediatric patients [34] Pediatric victims of ATV accidents [22] Nº of articles [24] those environments, regression analysis was the predominant forecasting technique. In opposition, LOS data from Emergency departments were exclusively modeled using Machine Learning techniques.…”

Section: Resultsmentioning

confidence: 99%

“…Half of the studies in our corpus used the procedure to validate model results, seeking its generalization. Cross-validation approaches are divided into three categories: traditional holdout [35], [20], [3], [41], [38], [27], [34], [2], temporal holdout [19], [23], [28], [39], [2], and k-fold [42], [28].…”

Section: Resultsmentioning

confidence: 99%

“…[30] X A large number of studies use the coding of categorical variables (n=8) through the use of dummy variables [18], [3], [2], [24], and speci c codings that vary according to needs [29], [26], [19], [33]. Two studies use data rescaling through normalization [34] and linear rescaling [35]. Other data preparation methods include the categorization of variables [36], [31], [33], and feature engineering [19], [3], [2], which creates new variables by combining the ones available in the dataset.…”

Section: Resultsmentioning

confidence: 99%

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Forecasting the Length-of-Stay of Pediatric Patients in Hospitals: A Scoping Review

Medeiros

Fogliatto

ROCHA

et al. 2021

Preprint

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Background: Healthcare management faces complex challenges in allocating hospital resources and predicting patients' length-of-stay (LOS) is critical in effectively managing those resources. This work aims to identify approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS–P), and patients' populations and environments used to develop the models. Methods: Based on a scoping review, 28 studies were identified and analyzed. Methods reported in the literature were classified according to the stage in which they are used in the modeling process: (i) pre-processing of data, (ii) variable selection, and (iii) cross-validation. Results: Forecasting models are most often applied to newborn patients and, consequently, in neonatal intensive care units. Regression analysis is the most widely used modeling approach; techniques associated with Machine Learning are still incipient and mostly used in emergency departments to model patients in specific situations.Conclusions: The studies' main benefits include informing family members about the patient's expected discharge date and enabling hospital resources' allocation and planning. Main research gaps are associated with the lack of generalization of forecasting models and limited reported applicability in hospital management. This study also provides a practical guide to methods applied for LOS–P forecasting and a future research agenda.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Forecasting the Length-of-Stay of Pediatric Patients in Hospitals: A Scoping Review

Medeiros

Fogliatto

ROCHA

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…At a more personalized level, it will be possible to determine the persons whose profile is at risk of suffering from certain diseases in relation to their usual activity, their consumption profile, their genomics, etc. There are many recent works supporting this approach, particularly by applying machine learning techniques, such as predicting heart diseases [ 10 ], stays in intensive care units [ 11 ], and cervical cancer detection [ 12 ], among many others.…”

Section: Background and Objectivesmentioning

confidence: 99%

Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers

Gómez–Pulido

Pulido

Rodríguez‐Puyol

et al. 2021

IJERPH

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A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

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“…While features engineering is a manual process in the traditional classifiers (shallow learning), the DNN consists of layers, and each layer has a number of neurons that mimic the functionality of the neurons in the human brain. Hence, DNN has the ability to automatically learning features [24]. Noting that we have used three layers, with 10 neurons in each layer.…”

Section: Classificationmentioning

confidence: 99%

A Classifier to Detect Informational vs. Non-Informational Heart Attack Tweets

et al. 2021

Self Cite

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Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing “heart attack” and then select from those the ones with useful information. Informational tweets are those which express real heart attack issues, e.g., “Yesterday morning, my grandfather had a heart attack while he was walking around the garden.” On the other hand, there are non-informational tweets such as “Dropped my iPhone for the first time and almost had a heart attack.” The starting point was to manually classify around 7000 tweets as either informational (11%) or non-informational (89%), thus yielding a labeled dataset to use in devising a machine learning classifier that can be applied to our large collection of over 20 million tweets. Tweets were cleaned and converted to a vector representation, suitable to be fed into different machine-learning algorithms: Deep neural networks, support vector machine (SVM), J48 decision tree and naïve Bayes. Our experimentation aimed to find the best algorithm to use to build a high-quality classifier. This involved splitting the labeled dataset, with 2/3 used to train the classifier and 1/3 used for evaluation besides cross-validation methods. The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). In addition, it obtained the highest F1-scores with (73.6%) and (97.4%) for informational and non-informational classes, respectively.

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Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach

Cited by 32 publications

References 19 publications

Forecasting the Length-of-Stay of Pediatric Patients in Hospitals: A Scoping Review

Forecasting the Length-of-Stay of Pediatric Patients in Hospitals: A Scoping Review

Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers

A Classifier to Detect Informational vs. Non-Informational Heart Attack Tweets

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