A Predictive Analysis of Electronic Healthcare Records for Stroke Symptoms

University of Technology Sydney. Faculty of Engineering and Information Technology.Cerebrovascular symptoms, commonly known as stroke, can affect different parts of the human body depending on the area of brain affected. The patients who survive usually have a poor quality of life because of serious illness, long-term disability and become a burden to their families and the health care system. There is a strong demand for the management focused on prevention and early treatment of disease by analysing different factors. However, a high volume of medical data, heterogeneity, and complexity have become the biggest challenges in stroke symptoms prediction. Algorithms with very high level of accuracy are, therefore, vital for medical diagnosis. The development of such algorithms nevertheless still remains obscure despite its importance and necessity for healthcare. Electronic Healthcare Records (EHRs) describe the details about patient’s physical and mental health, diagnosis, lab results, treatments or patient care plan and so forth. The huge amount of information in these records provides insights about the diagnosis and prediction of various diseases. Currently, the International Classification of Diseases, 10th Revision or ICD-10th codes is used for representing each patient record. The huge amount of information in these records provides insights about the diagnosis and prediction of various diseases. Various machine learning techniques are used for the analysis of data derived from these patient records. The predictive techniques have been widely applied in clinical decision making such as predicting occurrence of a disease or diagnosis, evaluating prognosis or outcome of diseases and assisting clinicians to recommend treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the underlying knowledge because it is incapable of simulating the complexity on feature representation of the medical problem domains. This research used aggregated files of Electronic Healthcare Records (EHRs) from Department of Medical Services, The Ministry of Public Health of Thailand between 2015 and 2016. The empirical research is intended to evaluate the ability of machine learning and deep learning to recognize patterns in multi-label classification of stroke. This research aims at the investigation of five techniques: Support Vector Machine (SVM); k-Nearest Neighbours (k-NN); Backpropagation; Recurrent Neural Network (RNN); and Long Short-Term Memory - Recurrent Neural Network (LSTM-RNN). These are powerful and widely used techniques in machine learning and bioinformatics. First, we decoded ICD-10th codes into the health records, as well as other potential risk factors within EHRs into the pattern and model for prediction. Second, we purposed a conceptual Case Based Reasoning (CBR) framework for stroke disease prediction that uses previous case-based knowledge. A conceptual case-based reasoning framework to predict from patients’ health risk factors and to recognize a particular case that probably develop stroke and prepare or warn patients to handle disease burden outcome. It describes the design, implementation and evaluation of a novel system to facilitate stroke prediction, which relies on data collected from EHRs. Finally, the effectiveness of Backpropagation; RNN; and LSTM-RNN for prediction of stroke based on healthcare records is modelled. The results show several strong baselines that include accuracy, recall, and F1 measure score. Consequently, deep learning allows the disclosure of some unknown or unexpressed knowledge during prediction procedure, which is beneficial for decision-making in medical practice and provide useful suggestions and warnings to patient about unpredictable stroke

A Comparison of Patient History- and EKG-based Cardiac Risk Scores.

Patient-specific risk scores are used to identify individuals at elevated risk for cardiovascular disease. Typically, risk scores are based on patient habits and medical history - age, sex, race, smoking behavior, and prior vital signs and diagnoses. We explore an alternative source of information, a patient's raw electrocardiogram recording, and develop a score of patient risk for various outcomes. We compare models that predict adverse cardiac outcomes following an emergency department visit, and show that a learned representation (e.g. deep neural network) of raw EKG waveforms can improve prediction over traditional risk factors. Further, we show that a simple model based on segmented heart beats performs as well or better than a complex convolutional network recently shown to reliably automate arrhythmia detection in EKGs. We analyze a large cohort of emergency department patients and show evidence that EKG-derived scores can be more robust to patient heterogeneity

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose {\sf Dipole}, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation

Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)