Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure

Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar characteristics which could also help in predicting patient outcomes like length of stay. These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients using a probabilistic framework and to predict length of stay of these patients using data-driven artificial intelligence-based methods, we apply natural language processing technique, topic modeling, to identify the themes present in diagnostic codes and in procedure reports of 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling identified twelve themes each in diagnostic codes and procedure reports which revealed information about different phenotypes related to various perspectives about heart failure, to study patients\u27 profiles and to discover new relationships among medical concepts. Each theme had a set of keywords and each clinical note was labeled with two themes - one corresponding to its diagnostic code and the other corresponding to its procedure reports along with their percentage contribution. We used these themes and their percentage contribution to predict length of stay. We found that the themes discovered in diagnostic codes and procedure reports using topic modeling together were able to predict length of stay of the patients with an accuracy of 61.1% and an Area under the Receiver Operating Characteristic Curve (ROC AUC) value of 0.828

Deep Learning in the Prediction of Clinically Significant Outcomes in Stroke and General Medicine Patients

Background The need for novel strategies to improve outcome prediction and the categorisation of unstructured medical data will increase as the demands on hospitals, associated with the increasing age and complexity of admitted patients, continues to rise. Stroke is a highly specialised field, in which key performance indicators and discharge planning have an important role. General medicine is a field that encompasses a wide variety of multisystem and undifferentiated illnesses. It is possible that machine learning, in particular deep learning, may be able to assist with the prediction of clinically significant outcomes both in areas with highly specialised assessment and treatment considerations (such as stroke), as well as fields with a diverse mix of medical conditions and comorbidities (such as general medicine). Method This thesis comprised of studies using machine learning to predict clinically significant outcomes in stroke and general medicine inpatients. Initially a systematic review was conducted to evaluate the existing literature regarding the prediction of one such clinically significant outcome, length of stay, in medical inpatients. Derivation and validation studies were conducted to develop models for stroke inpatients to aid with the prediction of discharge independence, survival to discharge, discharge destination and length of stay. Stroke key performance indicator-automated extraction and clinical coding categorisation were undertaken in studies employing techniques including natural language processing. Natural language processing was applied to general medicine free-text data in pilot, derivation, and validation studies in the prediction of outcomes including discharge timing. Results The systematic review identified a particular lack of prospective validation studies for machine learning models developed to aid with length of stay prediction in medical inpatients. The stroke model derivation, prospective and external validation studies demonstrated the successful use of machine learning models in the prediction of outcomes relevant to discharge planning for stroke patients. For example, an area under the receiver operator curve (AUC) of 0.85 and 0.87 was achieved for the prediction of independence at the time of discharge in the prospective and external validation datasets respectively. The automated collection of stroke key performance indicators and the application of natural language processing to stroke clinical coding also demonstrated performance as high as an AUC of 0.95-1.00 in key performance indicator classification tasks. The general medicine pilot, derivation, prospective and external validation studies demonstrated the development and success of artificial neural networks in the prediction of discharge within the next 48 hours (AUC 0.78 and 0.74 in the prospective and external validation datasets respectively). Conclusions Machine learning models (including deep learning) can successfully predict clinically significant outcomes in stroke and general medicine patients.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202

Three essays on optimization of the intensive care unit (ICU) management decisions

The intensive care unit (ICU) is one of the most crucial resources in the hospital. Improper ICU management causes many negative effects in the ICU itself and in other connected departments along the patient care path. This dissertation presents three papers on optimizing the ICU management decisions. The first paper provides the first structured and comprehensive review of ICU problems in OR/MS. The relevant papers are discussed based on a new framework. The second paper proposes a discrete-time MDP model to find admission and early discharge policies that minimize these negative consequences. By minimizing the medical consequences, the approach demonstrated significantly outperforms a myopic policy as applied by most hospitals in practice. The third paper compares eleven different management policies based on different KPIs by a simulation study. In comparison to the baseline case running on a FCFS rule, any management policy is superior regardless of the evaluation criteria