An Examination of How Robots, Artificial Intelligence, and Machinery Learning are Being Applied in the Medical and Healthcare Industries

Machine learning techniques are associated with diagnostics systems to apply methods that enable computers to link patient data to earlier data and give instructions to correct the disease.In recent years, researchers have promoted two or three data mining based techniques for disease diagnosis. Each function in machine learning and data mining techniques is built through characteristics and features.As a part of prognosis, information must be separated from patient data and information retrieved in stored databases and comparative records. For any disease, early diagnosis or diagnosis will determine the chances of a correct recovery. Disease prediction therefore becomes a more important task to support physicians in delivering efficient treatment to people.In health care, data is being created and disposed of at an extraordinary rate compared to the health care sectors. Data for medical profiling is often found in a variety of sources such as electronic health records, lab and imaging systems, doctor notes and accounts. The medical records database will then contain irrelevant data sourced from multiple sources. Preprocessing data and eliminating irrelevant data then immediately opening it up for predictive analysis is one of the significant difficulties of the health care industry

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

Clinical Text Mining: Secondary Use of Electronic Patient Records

This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields