Utilizing Temporal Information in The EHR for Developing a Novel Continuous Prediction Model

Type 2 diabetes mellitus (T2DM) is a nation-wide prevalent chronic condition, which includes direct and indirect healthcare costs. T2DM, however, is a preventable chronic condition based on previous clinical research. Many prediction models were based on the risk factors identified by clinical trials. One of the major tasks of the T2DM prediction models is to estimate the risks for further testing by HbA1c or fasting plasma glucose to determine whether the patient has or does not have T2DM because nation-wide screening is not cost-effective. Those models had substantial limitations on data quality, such as missing values. In this dissertation, I tested the conventional models which were based on the most widely used risk factors to predict the possibility of developing T2DM. The AUC was an average of 0.5, which implies the conventional model cannot be used to screen for T2DM risks. Based on this result, I further implemented three types of temporal representations, including non-temporal representation, interval-temporal representation, and continuous-temporal representation for building the T2DM prediction model. According to the results, continuous-temporal representation had the best performance. Continuous-temporal representation was based on deep learning methods. The result implied that the deep learning method could overcome the data quality issue and could achieve better performance. This dissertation also contributes to a continuous risk output model based on the seq2seq model. This model can generate a monotonic increasing function for a given patient to predict the future probability of developing T2DM. The model is workable but still has many limitations to overcome. Finally, this dissertation demonstrates some risks factors which are underestimated and are worthy for further research to revise the current T2DM screening guideline. The results were still preliminary. I need to collaborate with an epidemiologist and other fields to verify the findings. In the future, the methods for building a T2DM prediction model can also be used for other prediction models of chronic conditions

A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes

Data mining techniques are applied in many applications as a standard procedure for analyzing the large volume of available data, extracting useful information and knowledge to support the major decision-making processes. Diabetes mellitus is a continuing, general, deadly syndrome occurring all around the world. It is characterized by hyperglycemia occurring due to abnormalities in insulin secretion which would in turn result in irregular rise of glucose level. In recent years, the impact of Diabetes mellitus has increased to a great extent especially in developing countries like India. This is mainly due to the irregularities in the food habits and life style. Thus, early diagnosis and classification of this deadly disease has become an active area of research in the last decade. Numerous clustering and classifications techniques are available in the literature to visualize temporal data to identify trends for controlling diabetes mellitus. This work presents an experimental study of several algorithms which classifies Diabetes Mellitus data effectively. The existing algorithms are analyzed thoroughly to identify their advantages and limitations. The performance assessment of the existing algorithms is carried out to determine the best approach

Data visualization and predictive modeling for identifying comorbidities in diabetic patients

Diabetes is one of the most common chronic diseases in the world. Diabetic patients are also more susceptible to develop additional comorbidities over time even causing death. This makes it essential to identify the risk of developing comorbidities as early as possible for effective diabetes management and to reduce the burden on healthcare system. Large volumes of clinical data which has been collected over the years has potential to be translated into meaningful information to enable healthcare professionals gain insights into diabetic patient comorbidities. This research has two key contributions. First, an interactive diabetes dashboard is developed in which the data is integrated and shown in the form of visually appealing charts, graphs and tables. The dashboard displays aggregated results with drilldown capabilities to allow navigation at finer granularities of various metrics. Second, predictive models are built to forecast the likelihood of one of the three common comorbidities for diabetic patients – Benign Hypertension, Congestive Heart Failure, and Acute Renal Failure. The models use advanced data mining algorithms such as Logistic Regression, Neural Network, CHAID, Bayesian Network, Random Forest and Ensemble. Results from these models are also incorporated into an interactive assessment tool that has the ability to take user input and predict the likelihood of one of these comorbidities. Northern Health (NH) dataset consisting exclusively of diabetic patients is used for this research

Workshop on R&D projects: proceedings

P.PORTO Research Workshops are thematic meetings, to present and discuss R&D activities and outcomes – be it in the form of new knowledge, applied technology, industrial or intellectual property – providing a space for debate, networking and creation of synergies. This volume provides the contributions of the Research Workshop of April 2019, dedicated to the set of R&D projects led by P.PORTO researchers, in collaboration with companies and end-users, in the scope of the national projects call 02/SAICT/2016.info:eu-repo/semantics/publishedVersio

Clinical Decision Support Systems for Diabetes Care: Evidence and Development Between 2017 and Present

The clinical decision support systems (CDSs) for diabetes have improved significantly over the years. Multiple factors serve as driving forces for the uptake of CDSs. Newer technologies, initiatives, government mandates, and a competitive environment collectively facilitate advancement in diabetes care. This book chapter summarizes global CDSs development in recent years. Our review of the past few years’ publications on CDSs for diabetes shows that the United States is leading the world in technology development and clinical evidence generation. Developing countries worldwide are catching up in CDSs development and standards of patient care. Though most CDSs and published studies are on diabetes diagnosis, treatment, and management, a small portion of the research is devoted to prediabetes and type I diabetes. Increased efforts worldwide have been devoted to artificial intelligence and machine learning in diabetes care

Non-communicable Diseases, Big Data and Artificial Intelligence

This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine

Development and Evaluation of an Interdisciplinary Periodontal Risk Prediction Tool Using a Machine Learning Approach

Periodontitis (PD) is a major public health concern which profoundly affects oral health and concomitantly, general health of the population worldwide. Evidence-based research continues to support association between PD and systemic diseases such as diabetes and hypertension, among others. Notably PD also represents a modifiable risk factor that may reduce the onset and progression of some systemic diseases, including diabetes. Due to lack of oral screening in medical settings, this population does not get flagged with the risk of developing PD. This study sought to develop a PD risk assessment model applicable at clinical point-of-care (POC) by comparing performance of five supervised machine learning (ML) algorithms: Naïve Bayes, Logistic Regression, Support Vector Machine, Artificial Neural Network and Decision Tree, for modeling risk by retrospectively interrogating clinical data collected across seven different models of care (MOC) within the interdisciplinary settings. Risk assessment modeling was accomplished using Waikato Environment for Knowledge Analysis (WEKA) open-sourced tool, which supported comparative assessment of the relative performance of the five ML algorithms when applied to risk prediction. To align with current conventions for clinical classification of disease severity, predicting PD risk was treated as a ‘classification problem’, where patients were sorted into two categories based on disease severity and ‘low risk PD’ was defined as no or mild gum disease (‘controls’) or ‘high risk PD’ defined as moderate to severe disease (‘cases’). To assess the predictive performance of models, the study compared performance of ML algorithms applying analysis of recall, specificity, area under the curve, precision, F-measure and Matthew’s correlation coefficient (MCC) and receiver operating characteristic (ROC) curve. A tenfold-cross validation was performed. External validation of the resultant models was achieved by creating validation data subsets applying random selection of approximately 10% of each class of data proportionately. Findings from this study have prognostic implications for assessing PD risk. Models evolved in the present study have translational value in that they can be incorporated into the Electronic Health Record (EHR) to support POC screening. Additionally, the study has defined relative performance of PD risk prediction models across various MOC environments. Moreover, these findings have established the power ML application can serve to create a decision support tool for dental providers in assessing PD status, severity and inform treatment decisions. Further, such risk scores could also inform medical providers regarding the need for patient referrals and management of comorbid conditions impacted by presence of oral disease such as PD. Finally, this study illustrates the benefit of the integrated medical and dental care delivery environment for detecting risk of periodontitis at a stage when implementation of proven interventions could delay and even prevent disease progression. Keywords: Periodontitis, Risk Assessment, Interprofessional Relations, Machine learning, Electronic Health Records, Decision Support System

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Psychiatric Case Record

Bipolar Disorder-Mania: Patient was apparently normal one-month back, Then all of a sudden he developed sleep disturbances –mainly difficult in initiation of sleep. He also started abusing his family members for unwanted things. Subsequently, he started talking excessively and irritable. Sometimes he sings film songs and dances. He used to say that God Supreme exists in himself and so he has all the powers of Almighty. With that superior power he says that he can solve all the problems in this world. He also says that he has invented herbs to keep people young. For the past one week, he talks excessively without having an hour of sleep & wanders here and there & found excessively smoking. He becomes excessively spiritual and goes to near by villages for offering prayers to God. He takes only a little food everyday and he is very much keen in personal cleanliness. Paranoid Schizophrenia: She was apparently normal 8 months back, then she developed sleep disturbances in the form of difficult in falling asleep. She was found talking & smiling to herself at night & day with mirror gazing. She started saying that her neighbour & relatives are planning to kill herself by poisoning. In this context she had frequent quarrels with them and she refused to take food prepared by her mother in law. She left the home at night without informing any one and started wandering in the road side near her home. She was complaining that she hears voices as if her neighbour & relatives were talking about her among themselves She was not doing house hold activities for past 6 months and she was not taking care of her child. Her personal hygiene was very much deteriorated slowly as she used to take bath & brush, only if she was asked to do so. She started abusing & assaulting the strangers and family members. Generalised Anxiety Disorder: Six months back he was apparently normal. He is working as a system analyst in a private bank . He had once, made a mistake in his bank work for which he was given charges by his employer, followed this event he becomes very tense and afraid whenever his boss called him. He is very cautious that he should not commit any mistakes. Even though he is not doing so, he fears that he may commit some mistake in his work. At that moment he develops palpitation, giddiness, breathlessness, excessive sweating over palms and soles. Slowly these symptoms present through out the day even when he was not in his office, and he could not control his fearfulness. For the past 6 months he didn’t sleep well. His sleep is disturbed by bad dreams. Recurrent Depressive Disorder: Patient was apparently alright 2 months back. Then she developed sleep disturbances particularly early morning awakening, she use to wake up by 3.00 am and use to brood about herself and started crying. She was not doing her domestic work as before, as she felt excess tiredness and use to take frequent rests. She developed poor communication. She had lost her interest in pleasurable activities and was not interested in watching TV, and attending family gatherings. She stayed aloof most of the time & calm, quiet and withdrawn. She was expressing her helplessness and hopelessness about the future. She started to have decline in maintaining self care. 15 days back, she frequently expressed suicidal ideas and she had attempted suicide by hanging herself and was rescued by neighbours. 5 days back, she started talking in an irrelevant manner. She was smiling to self. She was assaulting her family members. She was suspicious that her neighbour had done black magic on her and also saying that people are talking about her. She reported hearing the voice of her neighbour scolding and threatening her. Organic Brain Syndrome – Dementia: Ten months back he was apparently alright. Then his relatives noticed himself frequently misplaces things inside his home. Then he started behaving aggressively. He was beating his wife without reason. He was roaming here and there, running out of home and wandering aimlessly. He was not able to come back home when he goes out. He was brought back to home by his relatives. Slowly he developed fearfulness and tremulousness while he was staying alone. He also started saying that his family members & neighbours were talking about himself, in this context he would make frequent quarrels with them. He also started hearing voices of known male voices abusing himself in third person. He sleeps for few hour only. He is passing urine and motion inside the house. He is asking about his brother and mother-in-law who were expired long back. He behaves abnormally such as pouring water in the plate while eating. And his relatives found the symptoms were worsened by evening. All these symptoms started insidiously, increased in severity through time and attained the present state. No history of loss of appetite / crying spells / suicidal tendencies / convulsions / fever / head injury