Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described

Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach

Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVM), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: K-LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large datasets from the Boston Medical Center, the largest safety-net hospital system in New England

Predictive modelling of hospital readmissions in diabetic patients clusters

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceDiabetes is a global public health problem with increasing incidence over the past 10 years. This disease's social and economic impacts are widely assessed worldwide, showing a direct and gradual decrease in the individual's ability to work, a gradual loss in the scale of quality of life and a burden on personal finances. The recurrence of hospitalisation is one of the most significant indexes in measuring the quality of care and the opportunity to optimise resources. Numerous techniques identify the patient who will need to be readmitted, such as LACE and HOSPITAL. The purpose of this study was to use a dataset related to the risk of hospital readmission in patients with Diabetes first to apply a clustering of subgroups by similarity. Then structures a predictive analysis with the main algorithms to identify the methodology of best performance. Numerous approaches were performed to prepare the dataset for these two interventions. The results found in the first phase were two clusters based on the total number of hospital recurrences and others on total administrative costs, with K=3. In the second phase, the best algorithm found was Neural Network 3, with a ROC of 0.68 and a misclassification rate of 0.37. When applied the same algorithm in the clusters, there were no gains in the confidence of the indexes, suggesting that there are no substantial gains in the division of subpopulations since the disease has the same behaviour and needs throughout its development

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications

Centralized and distributed learning methods for predictive health analytics

The U.S. health care system is considered costly and highly inefficient, devoting substantial resources to the treatment of acute conditions in a hospital setting rather than focusing on prevention and keeping patients out of the hospital. The potential for cost savings is large; in the U.S. more than $30 billion are spent each year on hospitalizations deemed preventable, 31% of which is attributed to heart diseases and 20% to diabetes. Motivated by this, our work focuses on developing centralized and distributed learning methods to predict future heart- or diabetes- related hospitalizations based on patient Electronic Health Records (EHRs). We explore a variety of supervised classification methods and we present a novel likelihood ratio based method (K-LRT) that predicts hospitalizations and offers interpretability by identifying the K most significant features that lead to a positive prediction for each patient. Next, assuming that the positive class consists of multiple clusters (hospitalized patients due to different reasons), while the negative class is drawn from a single cluster (non-hospitalized patients healthy in every aspect), we present an alternating optimization approach, which jointly discovers the clusters in the positive class and optimizes the classifiers that separate each positive cluster from the negative samples. We establish the convergence of the method and characterize its VC dimension. Last, we develop a decentralized cluster Primal-Dual Splitting (cPDS) method for large-scale problems, that is computationally efficient and privacy-aware. Such a distributed learning scheme is relevant for multi-institutional collaborations or peer-to-peer applications, allowing the agents to collaborate, while keeping every participant's data private. cPDS is proved to have an improved convergence rate compared to existing centralized and decentralized methods. We test all methods on real EHR data from the Boston Medical Center and compare results in terms of prediction accuracy and interpretability

Mobile Wound Assessment and 3D Modeling from a Single Image

The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image

Electronic Health Record Summarization over Heterogeneous and Irregularly Sampled Clinical Data

The increasing adoption of electronic health records (EHRs) has led to an unprecedented amount of patient health information stored in an electronic format. The ability to comb through this information is imperative, both for patient care and computational modeling. Creating a system to minimize unnecessary EHR data, automatically distill longitudinal patient information, and highlight salient parts of a patient’s record is currently an unmet need. However, summarization of EHR data is not a trivial task, as there exist many challenges with reasoning over this data. EHR data elements are most often obtained at irregular intervals as patients are more likely to receive medical care when they are ill, than when they are healthy. The presence of narrative documentation adds another layer of complexity as the notes are riddled with over-sampled text, often caused by the frequent copy-and-pasting during the documentation process. This dissertation synthesizes a set of challenges for automated EHR summarization identified in the literature and presents an array of methods for dealing with some of these challenges. We used hybrid data-driven and knowledge-based approaches to examine abundant redundancy in clinical narrative text, a data-driven approach to identify and mitigate biases in laboratory testing patterns with implications for using clinical data for research, and a probabilistic modeling approach to automatically summarize patient records and learn computational models of disease with heterogeneous data types. The dissertation also demonstrates two applications of the developed methods to important clinical questions: the questions of laboratory test overutilization and cohort selection from EHR data

Three Essays on Enhancing Clinical Trial Subject Recruitment Using Natural Language Processing and Text Mining

Patient recruitment and enrollment are critical factors for a successful clinical trial; however, recruitment tends to be the most common problem in most clinical trials. The success of a clinical trial depends on efficiently recruiting suitable patients to conduct the trial. Every clinical trial research has a protocol, which describes what will be done in the study and how it will be conducted. Also, the protocol ensures the safety of the trial subjects and the integrity of the data collected. The eligibility criteria section of clinical trial protocols is important because it specifies the necessary conditions that participants have to satisfy. Since clinical trial eligibility criteria are usually written in free text form, they are not computer interpretable. To automate the analysis of the eligibility criteria, it is therefore necessary to transform those criteria into a computer-interpretable format. Unstructured format of eligibility criteria additionally create search efficiency issues. Thus, searching and selecting appropriate clinical trials for a patient from relatively large number of available trials is a complex task. A few attempts have been made to automate the matching process between patients and clinical trials. However, those attempts have not fully integrated the entire matching process and have not exploited the state-of-the-art Natural Language Processing (NLP) techniques that may improve the matching performance. Given the importance of patient recruitment in clinical trial research, the objective of this research is to automate the matching process using NLP and text mining techniques and, thereby, improve the efficiency and effectiveness of the recruitment process. This dissertation research, which comprises three essays, investigates the issues of clinical trial subject recruitment using state-of-the-art NLP and text mining techniques. Essay 1: Building a Domain-Specific Lexicon for Clinical Trial Subject Eligibility Analysis Essay 2: Clustering Clinical Trials Using Semantic-Based Feature Expansion Essay 3: An Automatic Matching Process of Clinical Trial Subject Recruitment In essay1, I develop a domain-specific lexicon for n-gram Named Entity Recognition (NER) in the breast cancer domain. The domain-specific dictionary is used for selection and reduction of n-gram features in clustering in eassy2. The domain-specific dictionary was evaluated by comparing it with Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT). The results showed that it add significant number of new terms which is very useful in effective natural language processing In essay 2, I explore the clustering of similar clinical trials using the domain-specific lexicon and term expansion using synonym from the Unified Medical Language System (UMLS). I generate word n-gram features and modify the features with the domain-specific dictionary matching process. In order to resolve semantic ambiguity, a semantic-based feature expansion technique using UMLS is applied. A hierarchical agglomerative clustering algorithm is used to generate clinical trial clusters. The focus is on summarization of clinical trial information in order to enhance trial search efficiency. Finally, in essay 3, I investigate an automatic matching process of clinical trial clusters and patient medical records. The patient records collected from a prior study were used to test our approach. The patient records were pre-processed by tokenization and lemmatization. The pre-processed patient information were then further enhanced by matching with breast cancer custom dictionary described in essay 1 and semantic feature expansion using UMLS Metathesaurus. Finally, I matched the patient record with clinical trial clusters to select the best matched cluster(s) and then with trials within the clusters. The matching results were evaluated by internal expert as well as external medical expert

Risk prediction analysis for post-surgical complications in cardiothoracic surgery

Cardiothoracic surgery patients have the risk of developing surgical site infections (SSIs), which causes hospital readmissions, increases healthcare costs and may lead to mortality. The first 30 days after hospital discharge are crucial for preventing these kind of infections. As an alternative to a hospital-based diagnosis, an automatic digital monitoring system can help with the early detection of SSIs by analyzing daily images of patient’s wounds. However, analyzing a wound automatically is one of the biggest challenges in medical image analysis. The proposed system is integrated into a research project called CardioFollowAI, which developed a digital telemonitoring service to follow-up the recovery of cardiothoracic surgery patients. This present work aims to tackle the problem of SSIs by predicting the existence of worrying alterations in wound images taken by patients, with the help of machine learning and deep learning algorithms. The developed system is divided into a segmentation model which detects the wound region area and categorizes the wound type, and a classification model which predicts the occurrence of alterations in the wounds. The dataset consists of 1337 images with chest wounds (WC), drainage wounds (WD) and leg wounds (WL) from 34 cardiothoracic surgery patients. For segmenting the images, an architecture with a Mobilenet encoder and an Unet decoder was used to obtain the regions of interest (ROI) and attribute the wound class. The following model was divided into three sub-classifiers for each wound type, in order to improve the model’s performance. Color and textural features were extracted from the wound’s ROIs to feed one of the three machine learning classifiers (random Forest, support vector machine and K-nearest neighbors), that predict the final output. The segmentation model achieved a final mean IoU of 89.9%, a dice coefficient of 94.6% and a mean average precision of 90.1%, showing good results. As for the algorithms that performed classification, the WL classifier exhibited the best results with a 87.6% recall and 52.6% precision, while WC classifier achieved a 71.4% recall and 36.0% precision. The WD had the worst performance with a 68.4% recall and 33.2% precision. The obtained results demonstrate the feasibility of this solution, which can be a start for preventing SSIs through image analysis with artificial intelligence.Os pacientes submetidos a uma cirurgia cardiotorácica tem o risco de desenvolver infeções no local da ferida cirúrgica, o que pode consequentemente levar a readmissões hospitalares, ao aumento dos custos na saúde e à mortalidade. Os primeiros 30 dias após a alta hospitalar são cruciais na prevenção destas infecções. Assim, como alternativa ao diagnóstico no hospital, a utilização diária de um sistema digital e automático de monotorização em imagens de feridas cirúrgicas pode ajudar na precoce deteção destas infeções. No entanto, a análise automática de feridas é um dos grandes desafios em análise de imagens médicas. O sistema proposto integra um projeto de investigação designado CardioFollow.AI, que desenvolveu um serviço digital de telemonitorização para realizar o follow-up da recuperação dos pacientes de cirurgia cardiotorácica. Neste trabalho, o problema da infeção de feridas cirúrgicas é abordado, através da deteção de alterações preocupantes na ferida com ajuda de algoritmos de aprendizagem automática. O sistema desenvolvido divide-se num modelo de segmentação, que deteta a região da ferida e a categoriza consoante o seu tipo, e num modelo de classificação que prevê a existência de alterações na ferida. O conjunto de dados consistiu em 1337 imagens de feridas do peito (WC), feridas dos tubos de drenagem (WD) e feridas da perna (WL), provenientes de 34 pacientes de cirurgia cardiotorácica. A segmentação de imagem foi realizada através da combinação de Mobilenet como codificador e Unet como decodificador, de forma a obter-se as regiões de interesse e atribuir a classe da ferida. O modelo seguinte foi dividido em três subclassificadores para cada tipo de ferida, de forma a melhorar a performance do modelo. Caraterísticas de cor e textura foram extraídas da região da ferida para serem introduzidas num dos modelos de aprendizagem automática de forma a prever a classificação final (Random Forest, Support Vector Machine and K-Nearest Neighbors). O modelo de segmentação demonstrou bons resultados ao obter um IoU médio final de 89.9%, um dice de 94.6% e uma média de precisão de 90.1%. Relativamente aos algoritmos que realizaram a classificação, o classificador WL exibiu os melhores resultados com 87.6% de recall e 62.6% de precisão, enquanto o classificador das WC conseguiu um recall de 71.4% e 36.0% de precisão. Por fim, o classificador das WD teve a pior performance com um recall de 68.4% e 33.2% de precisão. Os resultados obtidos demonstram a viabilidade desta solução, que constitui o início da prevenção de infeções em feridas cirúrgica a partir da análise de imagem, com recurso a inteligência artificial