A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients

Morton, April; Marzban, Eman N.; Giannoulis, Georgios; Patel, Ayush; Aparasu, Rajender R.; Kakadiaris, Ioannis A.

doi:10.1109/icmla.2014.76

Cited by 57 publications

(35 citation statements)

References 16 publications

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“…Previous models have included variables that, while important to understanding the context of the patient regarding discharge, most often represented nonactionable or nonmodifiable factors, such as age, chief concern, day of the week, insurance status, and race/ethnicity. 15,16 During this model's development, we gathered the input of physicians, nurses, case managers, physical therapists, nutritionists, and others responsible for the care of the patient. These discussions led to the generation of additional features in the model and provided critical guidance as to which barriers were most actionable by the team and helpful to display.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care

et al. 2019

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IMPORTANCE Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and requires coordination among surgeons, nurses, case managers, and others. This is difficult to achieve without early identification and systemwide transparency of discharge candidates and their respective barriers to discharge. OBJECTIVE To validate the performance of a clinically interpretable feedforward neural network model that could improve the discharge process by predicting which patients would be discharged within 24 hours and their clinical and nonclinical barriers. DESIGN, SETTING, AND PARTICIPANTS This prognostic study included adult patients discharged from inpatient surgical care from May 1, 2016, to August 31, 2017, at a quaternary care teaching hospital. Model performance was assessed with standard cross-validation techniques. The model's performance was compared with a baseline model using historical procedure median length of stay to predict discharges. In prospective cohort analysis, the feedforward neural network model was used to make predictions on general surgical care floors with 63 beds. If patients were not discharged when predicted, the causes of delay were recorded. MAIN OUTCOMES AND MEASURES The primary outcome was the out-of-sample area under the receiver operating characteristic curve of the model. Secondary outcomes included the causes of discharge delay and the number of avoidable bed-days. RESULTS The model was trained on 15 201 patients (median [interquartile range] age, 60 [46-70] years; 7623 [50.1%] men) discharged from inpatient surgical care. The estimated out-of-sample area under the receiver operating characteristic curve of the model was 0.840 (SD, 0.008; 95% CI, 0.839-0.844). Compared with the baseline model, the neural network model had higher sensitivity (52.5% vs 56.6%) and specificity (51.7% vs 82.6%). The neural network model identified 65 barriers to discharge. In the prospective study of 605 patients, causes of delays included clinical barriers (41 patients [30.1%]), variation in clinical practice (30 patients [22.1%]), and nonclinical reasons (65 patients [47.8%]). Summing patients who were not discharged owing to variation in clinical practice and nonclinical reasons, 128 bed-days, or 1.2 beds per day, were classified as avoidable. CONCLUSIONS AND RELEVANCE This cohort study found that a neural network model could predict daily inpatient surgical care discharges and their barriers. The model identified systemic causes of discharge delays. Such models should be studied for their ability to increase the timeliness of discharges.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care

et al. 2019

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“…As future admission requests appear to be a more complicated problem within an effective and long-term healthcare system planning, accurate prediction of in-hospital stay duration would allow, in short-term, for efficient human resources allocation and facilities occupancy. LoS prediction is a substantial problem which attracted research community's attention since the 1960s [12,13] by employing statistical methods. Since then, several scientific fields have risen, providing mathematical and computing classification and prediction techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Morton et al [13] compared and discussed the performance of various machine learning algorithms for the prediction of short-term vs. long-term LoS of hospitalized diabetic patients, where short-term is defined as less than 3 days. In their framework, they used 10,000 patients' records from the HCUP Nationwide Inpatient Sample database where each record contains several features including demographics, hospital information, admission type, number of diagnoses, health insurance status, total hospital charges and risk/severity measures.…”

Section: Related Workmentioning

confidence: 99%

“…It is worth mentioning, that two-level classification schemes are heuristic pattern recognition tools that are anticipated to yield better classification accuracy than single-level ones at the expense of a certain complication of the classification structure [20][21][22][23]. To the best of our knowledge, in the literature there has not been proposed any similar approach for the prediction of LoS while all proposed prediction models are single-level classifiers based on several classification algorithms (see [13][14][15][16][17]).…”

Section: Two-level Classifiermentioning

confidence: 99%

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Decision Support Software for Forecasting Patient’s Length of Stay

et al. 2018

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Length of stay of hospitalized patients is generally considered to be a significant and critical factor for healthcare policy planning which consequently affects the hospital management plan and resources. Its reliable prediction in the preadmission stage could further assist in identifying abnormality or potential medical risks to trigger additional attention for individual cases. Recently, data mining and machine learning constitute significant tools in the healthcare domain. In this work, we introduce a new decision support software for the accurate prediction of hospitalized patients’ length of stay which incorporates a novel two-level classification algorithm. Our numerical experiments indicate that the proposed algorithm exhibits better classification performance than any examined single learning algorithm. The proposed software was developed to provide assistance to the hospital management and strengthen the service system by offering customized assistance according to patients’ predicted hospitalization time.

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“…In the LOS estimation based on historical data,using quantitative analysis methods most, including univariate survival function fitting method (2) , multivariate regression method [17][18][19] , and supervised machine learning method (20)(21)(22) . Using the survival function to fit the LOS distribution of a certain type of patient (23) , can obtain the probability of the patient's LOS and the statistics, mean and variance in the sample group.…”

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confidence: 99%

Estimation of LOS level in hospitalization with multiple attributes: taking patients with kidney disease as an example

Zhang

Shen

2019

Preprint

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Aim: Thinking of the relationship between Multiple attributes and length of stay(LOS).This paper provide LOS level prediction method with applying a classification estimation of multi-avariables statistic,also provides suggestions of diversion management for kidney disease patients. Methods: We use three steps to finish the estimation. Firstly, Using the correlation-coefficient between the variable and LOS to find some sensitive factors; secondly,using projection pursuit clustering analysis to find the weight of each factor, then calculation the weighting value to discriminant the LOS level.thirdly, using mean test to give the multi-level classification of LOS based on the most sensitive factor. Results: We use 12547 kidney disease patients’ data in the year 2016 from a large hospital in the Department of Nephrology to applying the estimation method. The correlations results shows that the influence of variables on LOS are ranked as disease types>age>cost.The means test results demonstrate that patients with kidney disease are divided into 3 levels between 2-20 days of los. Short-term patients (los=2-9) are mainly treated with regular treatment; medium-term patients (los=10-15) are accompanied by emergency and acute attacks; long-term patients (los=16-20) need more treatments and long hospital observation period.Tested by multi-attribute multi-level discriminant model, Indicated that the kidney patients sample data can be modeled well by the builded models. Conclusion: We build the multi-variable multi-level discriminant model for LOS estimation, which is successively applied in kidney disease. The research offers a new way for LOS estimation through the Multi-variables. Key words:LOS; Multi-variable multi-level; Projection pursuit; Clustering and discriminant

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A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients

Cited by 57 publications

References 16 publications

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care

Decision Support Software for Forecasting Patient’s Length of Stay

Estimation of LOS level in hospitalization with multiple attributes: taking patients with kidney disease as an example

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