Predicting length of stay (LOS) in a hospital post-sugery

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe amount of time a patient stays in the hospital after a surgery has been an issue that hospital management faces, a longer stay in the recovery room involves a high cost to the hospital and consumes a lot of hospital resources, manpower and equipment. The amount of time is difficult to predict precisely since there are many external and internal factors that account for a longer or shorter stay and it is difficult for a team to consider all these factors and make this estimation manually. With the advancement of machine learning methods and models this prediction can be made automatically. The aim of this study was to create a predicting model that look at the patient data and the procedure data and predicts the amount of time the patient will stay after the surgery to make the current prediction of the length of stay by the hospital more accurate and compliment the current surgery scheduling and discharge system. To achieve the objective, a data mining approach was implemented. Python Language was used, with particular emphasis on Scikit-Learn, pandas and Seaborn packages. Tables from a relational database were processed and extracted to build a dataset. Exploratory data analysis was performed, and several model configurations were tested. The main differences that separate the models are outlier treatment, sampling techniques, feature scalers, feature engineering and type of algorithm – Linear Regression, Decision Trees Regressor, Multilayer Perceptron Regressor, Random Forest Regressor, Light Gradient Boosting Machine Regressor and Gradient Boosting Regressor. A total of 32993 hospital episodes were observed on this study. Out of these, 2006 were eliminated due to some data anomalies, namely, values that were wrong or impossible. The data was split in training and test data. Several model configurations were tested. The main differences that separate the models are outlier treatment, feature scalers, feature engineering and the type of algorithm. The best performing model had a score of 0.73 R2 which was obtained by using the Light Gradient Boosting Machine Regressor Algorithm using outlier removal, Robust Scaling and using all the features in the dataset

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MA

Knowledge-based incremental induction of clinical algorithms

The current approaches for the induction of medical procedural knowledge suffer from several drawbacks: the structures produced may not be explicit medical structures, they are only based on statistical measures that do not necessarily respect medical criteria which can be essential to guarantee medical correct structures, or they are not prepared to deal with the incremental arrival of new data. In this thesis we propose a methodology to automatically induce medically correct clinical algorithms (CAs) from hospital databases. These CAs are represented according to the SDA knowledge model. The methodology considers relevant background knowledge and it is able to work in an incremental way. The methodology has been tested in the domains of hypertension, diabetes mellitus and the comborbidity of both diseases. As a result, we propose a repository of background knowledge for these pathologies and provide the SDA diagrams obtained. Later analyses show that the results are medically correct and comprehensible when validated with health care professionals

Internet banking fraud detection using prudent analysis

The threat posed by cybercrime to individuals, banks and other online financial service providers is real and serious. Through phishing, unsuspecting victims’ Internet banking usernames and passwords are stolen and their accounts robbed. In addressing this issue, commercial banks and other financial institutions use a generically similar approach in their Internet banking fraud detection systems. This common approach involves the use of a rule-based system combined with an Artificial Neural Network (ANN). The approach used by commercial banks has limitations that affect their efficiency in curbing new fraudulent transactions. Firstly, the banks’ security systems are focused on preventing unauthorized entry and have no way of conclusively detecting an imposter using stolen credentials. Also, updating these systems is slow and their maintenance is labour-intensive and ultimately costly to the business. A major limitation of these rule-bases is brittleness; an inability to recognise the limits of their knowledge. To address the limitations highlighted above, this thesis proposes, develops and evaluates a new system for use in Internet banking fraud detection using Prudence Analysis, a technique through which a system can detect when its knowledge is insufficient for a given case. Specifically, the thesis proposes the following contributions:Doctor of Philosoph