Predicting The Discharge of Patients Via Machine Learning Based Discharge Predictive Model

The primary objective of the work is to create a discharge roster for patients by employing various machine learning techniques and to predict the discharge of a patient. The performance of the proposed discharge predictive model is measured through various performance measures. The research work is carried out based on the dataset formed with actual data of patients in hospital. The machine learning (ML) based Discharge Predictive Model is developed by combining well known ML algorithms like K-Nearest Neighbour (KNN) algorithm, Random Forests algorithm and Light Gradient Boosting algorithm with optimum parameters, various feature combinations and pre-processing techniques. The performance of the proposed model is measured in terms of accuracy and it is compared with various existing techniques like SVM and NN. The result of the comparison study exhibit that the proposed predictive learning model attained enhanced accuracy than other ML techniques

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

Background:The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body:While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for Emergency ML. Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion:This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume

A novel and reliable framework of patient deterioration prediction in Intensive Care Unit based on long short-term memory-recurrent neural network

The clinical investigation explored that early recognition and intervention are crucial for preventing clinical deterioration in patients in Intensive Care units (ICUs). Deterioration of patients is predictable and can be preventable if early risk factors are recognized and developed in the clinical setting. Timely detection of deterioration in ICU patients may also lead to better health management. In this paper, a new model was proposed based on Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) to predict deterioration of ICU patients. An optimisation model based on a modified genetic algorithm (GA) has also been proposed in this study to optimize the observation window, prediction window, and the number of neurons in hidden layers to increase accuracy, AUROC, and minimize test loss. The experimental results demonstrate that the prediction model proposed in this study acquired a significantly better classification performance compared with many other studies that used deep learning models in their works. Our proposed model was evaluated for two tasks: mortality and sudden transfer of patients to ICU. Our results show that the proposed model could predict deterioration before one hour of onset and outperforms other models. In this study, the proposed predictive model is implemented using the state-of-the-art graphical processing unit (GPU) virtual machine provided by Google Colaboratory. Moreover, the study uses a novel time-series approach, which is minute-by-minute. This novel approach enables the proposed model to obtain highly accurate results (i.e., an AUROC of 0.933 and an accuracy of 0.921). This study utilizes the individual and combined effectiveness of different types of variables (i.e., vital signs, laboratory measurements, GCS, and demographic data). In this study, data was extracted from MIMIC-III database. The ad-hoc frameworks proposed by previous studies can be improved by the novel and reliable prediction framework proposed in this research, which will result in predictions of more accurate performance. The proposed predictive model could reduce the required observation window (i.e., a reduction of 83%) for the prediction task while improving the performance. In fact, the proposed significant small size of observation window could obtain higher results which outperformed all previous works that utilize different sizes of observation window (i.e., 48 hours and 24 hours). Moreover, this research demonstrates the ability of the proposed predictive model to achieve accurate results (>80%) on 'raw' data in an experimental work. This shows that the rule-based pre-processing of clinical features is unnecessary for deep learning predictive models

Machine Learning Methods for Septic Shock Prediction

Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model is built by obtaining a risk score from fitting the Cox Hazard model on multiple input features. The score is added to the list of features and the Random Forest ensemble classifier is trained to produce the model. The Cox Enhanced Random Forest (CERF) proposed method is evaluated by comparing its predictive accuracy to those of extant methods

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science\u27s ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploratio

GENERALIZABLE MODELS FOR PREDICTION OF PHYSIOLOGICAL DECOMPENSATION FROM MULTIVARIATE AND MULTISCALE PHYSIOLOGICAL TIME SERIES USING DEEP LEARNING AND TRANSFER LEARNING TECHNIQUES

The goal of this thesis is to develop generalizable machine learning models for early prediction of physiological decomposition from multivariate and multiscale physiological time series data. A combination of recent advances in machine learning and the increased availability of more granular physiological time series data (due to increased adoption of electronic medical records in US hospitals) has encouraged the development of more accurate prediction models for the critically ill patients. One such physiological decompensation prediction task we consider in our work is the early prediction of onset of sepsis. Sepsis is a syndromic, life-threatening condition that arises when the body's response to infection injures its own internal organs. While there are effective protocols for treating sepsis (e.g. administration of broad-spectrum antibiotics, Intravenous fluids, and vasopressors) once it has been diagnosed, there still exists challenges in reliably identifying septic patients early in their course. The purpose of this work is to explore the feasibility of utilizing low-resolution electronic medical record data and high-resolution physiological time series data to develop accurate prediction models for onset of sepsis in critically ill patients. To achieve this objective - We first investigate the connection between heart rate (HR) and blood pressure (MAP) time series - as captured through quantification of the structure of their corresponding network representation - for early signs of sepsis. We will then explore the utility of recurrent neural network models for accurate prediction of onset of sepsis. Finally, we combine ideas from adversarial domain adaptation, representation learning and conformal prediction to develop a generalizable prediction model that can adapt well to new target populations (without the requirement of obtaining gold-standard labels).Ph.D

Secondary Analysis of Electronic Health Records

Health Informatics; Ethics; Data Mining and Knowledge Discovery; Statistics for Life Sciences, Medicine, Health Science

Mechanical ventilation and weaning: Roles and competencies of intensive care nurses and patients' experiences of breathing.

The papers III and IV of this thesis are not available in Munin. Paper III: Haugdahl, H. S., Storli, S. L., Meland, B., Dybwik, K., Romild, U., Klepstad, P.: “Underestimation of Patient Breathlessness by Nurses and Physicians During a Spontaneous Breathing Trial”. Available in American Journal of Respiratory and Critical Care Medicine 2015, 192(12):1440-1448. Paper IV: Haugdahl, H. S., Dahlberg, H., Klepstad, P., Storli, S. L.: “The Breath of Life. Patients’Experiences of Breathing During and After Mechanical Ventilation”. (Manuscript).Breathlessness is an under-recognized problem in intensive care. The overall aims of this study were to explore the roles and competencies of nurses in mechanical ventilation (MV) and weaning, and to explore patients’ experiences of breathing during and after mechanical ventilation. A multimethod design included: survey data from leaders in Norwegian ICUs, interviews and field observations of intensive care nurses in concrete weaning situations, a prospective observational study of 100 mechanically ventilated patients’ self report of breathlessness and, a qualitatively driven sequential mixed method design combining prospective observational breathlessness data during MV and data from follow-up interviews. We found that breathlessness was prevalent among mechanically ventilated patients (62%), and underestimated by nurses and physicians, regardless of expertise or experiences. MV patients’ experiences of breathing were not necessarily a separate experience, but intertwined with the whole illness experience and existential dimensions of life. The nurses’ roles in MV and weaning are their continuous presence and vigilance detection of early changes in the patients’ condition. To acknowledge the presence and impact of breathlessness seems important. Knowing the patient and facilitating well-being was a crucial part of competence in weaning and opened up for establishing trust and confidence, which were necessary to reach into the patients’ world and “pull” the patient back to life, to the “here and now”. This “pulling” was connected to “pushing” the patient further in the weaning process. A potential link between breathlessness and post-intensive care syndrome is an argument for patients’ own reports of breathing to form part of nursing interventions and follow up supporting the patients’ quest for meaning. To enhance the quality of care in MV and weaning, intensive care nurses have an important role in the interprofessional team in order to discuss, reflect and learn how to assess and respond to patients’ experiences of breathing

TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING

The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses