Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily activities. Estimates indicate that AD may rank third as a cause of death for older people, after heart disease and cancer. Identification of individuals at risk for developing AD is imperative for testing therapeutic interventions. The objective of the study was to determine could diagnostics of AD from EMR data alone (without relying on diagnostic imaging) be significantly improved by applying clinical domain knowledge in data preprocessing and positive dataset selection rather than setting naïve filters. METHODS: Data were extracted from the repository of heterogeneous ambulatory EMR data, collected from primary care medical offices all over the U.S. Medical domain knowledge was applied to build a positive dataset from data relevant to AD. Selected Clinically Relevant Positive (SCRP) datasets were used as inputs to a Long-Short-Term Memory (LSTM) Recurrent Neural Network (RNN) deep learning model to predict will the patient develop AD. RESULTS: Risk scores prediction of AD using the drugs domain information in an SCRP AD dataset of 2,324 patients achieved high out-of-sample score - 0.98-0.99 Area Under the Precision-Recall Curve (AUPRC) when using 90% of SCRP dataset for training. AUPRC dropped to 0.89 when training the model using less than 1,500 cases from the SCRP dataset. The model was still significantly better than when using naïve dataset selection. CONCLUSION: The LSTM RNN method that used data relevant to AD performed significantly better when learning from the SCRP dataset than when datasets were selected naïvely. The integration of qualitative medical knowledge for dataset selection and deep learning technology provided a mechanism for significant improvement of AD prediction. Accurate and early prediction of AD is significant in the identification of patients for clinical trials, which can possibly result in the discovery of new drugs for treatments of AD. Also, the contribution of the proposed predictions of AD is a better selection of patients who need imaging diagnostics for differential diagnosis of AD from other degenerative brain disorders.

Predicting complications of diabetes mellitus using advanced machine learning algorithms.

OBJECTIVE: We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of complications could help with more targeted measures that would prevent or slow down their development. MATERIALS AND METHODS: Experiments were conducted on the Healthcare Cost and Utilization Project State Inpatient Databases of California for the period of 2003 to 2011. Recurrent neural network (RNN) long short-term memory (LSTM) and RNN gated recurrent unit (GRU) deep learning methods were designed and compared with random forest and multilayer perceptron traditional models. Prediction accuracy of selected complications were compared on 3 settings corresponding to minimum number of hospitalizations between diabetes diagnosis and the diagnosis of complications. RESULTS: The diagnosis domain was used for experiments. The best results were achieved with RNN GRU model, followed by RNN LSTM model. The prediction accuracy achieved with RNN GRU model was between 73% (myocardial infarction) and 83% (chronic ischemic heart disease), while accuracy of traditional models was between 66% - 76%. DISCUSSION: The number of hospitalizations was an important factor for the prediction accuracy. Experiments with 4 hospitalizations achieved significantly better accuracy than with 2 hospitalizations. To achieve improved accuracy deep learning models required training on at least 1000 patients and accuracy significantly dropped if training datasets contained 500 patients. The prediction accuracy of complications decreases over time period. Considering individual complications, the best accuracy was achieved on depressive disorder and chronic ischemic heart disease. CONCLUSIONS: The RNN GRU model was the best choice for electronic medical record type of data, based on the achieved results.

Social network analysis for better understanding of influenza.

INTRODUCTION: The objective of this study is to improve the understanding of spatial spreading of complicated cases of influenza that required hospitalizations, by creating heatmaps and social networks. They will allow to identify critical hubs and routes of spreading of Influenza, in specific geographic locations, in order to contain infections and prevent complications, that require hospitalizations. MATERIAL AND METHODS: Data were downloaded from the Healthcare Cost and Utilization Project (HCUP) - SID, New York State database. Patients hospitalized with flu complications, between 2003 and 2012 were included in the research (30,380 cases). A novel approach was designed, by constructing heatmaps for specific geographic regions in New York state and power law networks, in order to analyze distribution of hospitalized flu cases. RESULTS: Heatmaps revealed that distributions of patients follow urban areas and big roads, indicating that flu spreads along routes, that people use to travel. A scale-free network, created from correlations among zip codes, discovered that, the highest populated zip codes didn't have the largest number of patients with flu complications. Among the top five most affected zip codes, four were in Bronx. Demographics of top affected zip codes were presented in results. Normalized numbers of cases per population revealed that, none of zip codes from Bronx were in the top 20. All zip codes with the highest node degrees were in New York City area. DISCUSSION: Heatmaps identified geographic distribution of hospitalized flu patients and network analysis identified hubs of the infection. Our results will enable better estimation of resources for prevention and treatment of hospitalized patients with complications of Influenza. CONCLUSION: Analyses of geographic distribution of hospitalized patients with Influenza and demographic characteristics of populations, help us to make better planning and management of resources for Influenza patients, that require hospitalization. Obtained results could potentially help to save many lives and improve the health of the population.

Endovascular repair of a ruptured descending thoracic aortic aneurysm.

Endovascular aneurysm repair has considerable potential advantages over the surgical approach as a treatment for thoracic aortic rupture, in part because open surgical repair of ruptured thoracic aortic aneurysms is associated with high mortality and morbidity rates. We describe the successful endovascular deployment of stent-grafts to repair a contained rupture of a descending thoracic aortic aneurysm in an 86-year-old man whose comorbidities prohibited surgery. Two months after the procedure, magnetic resonance angiography showed a patent stent-graft, a patent left subclavian artery, and complete exclusion of the aneurysm.

[Measuring quality of life in patients with heart failure].

INTRODUCTION: The quality of life in patients with heart failure is diminished by symptoms of disease, affected social connections, frequent hospitalisations, side effects caused by medication, and costs of treatment borne by the patient himself. OBJECTIVE: The objective of this research was to measure the quality of life of patients with heart failure and assess any potential changes during a six-month period. METHOD: This research is a follow-up study, and comprised 56 patients who were treated from September 1998 to August 1999 at the Institute for Cardiovascular Diseases of the Clinical Centre of Serbia. During the first hospitalisation, an initial baseline measurement of quality of life was conducted. The measurement was then repeated after three and six months, during check-ups. The measurement was conducted by using a special questionnaire for patients suffering from heart disorders, the "Minnesota Living with Heart Failure Questionnaire." RESULTS: The results showed a considerable improvement in the overall quality of life between the baseline and second measurements (F = 6.263; p = 0.020), as well as considerable improvement in the physical dimension of quality of life between the baseline and first measurements (F = 6.797; p = 0.016) and between the baseline and second measurements (F = 5.351; p = 0.030). CONCLUSION: Thanks to the application of the special Minnesota questionnaire, it was possible to measure small but considerable changes in the patients' quality of life.