Review Paper on Answers Selection and Recommendation in Community Question Answers System

Nowadays, question answering system is more convenient for the users, users ask question online and then they will get the answer of that question, but as browsing is primary need for each an individual, the number of users ask question and system will provide answer but the computation time increased as well as waiting time increased and same type of questions are asked by different users, system need to give same answers repeatedly to different users. To avoid this we propose PLANE technique which may quantitatively rank answer candidates from the relevant question pool. If users ask any question, then system provide answers in ranking form, then system recommend highest rank answer to the user. We proposing expert recommendation system, an expert will provide answer of the question which is asked by the user and we also implement sentence level clustering technique in which a single question have multiple answers, system provide most suitable answer to the question which is asked by the user

Prediction of stroke using deep learning model

© Springer International Publishing AG 2017. Many 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 reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. The outcomes of this research are more accurate than medical scoring systems currently in use for warning heart patients if they are likely to develop stroke

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

Interpretable Machine Learning Model for Clinical Decision Making

Despite machine learning models being increasingly used in medical decision-making and meeting classification predictive accuracy standards, they remain untrusted black-boxes due to decision-makers\u27 lack of insight into their complex logic. Therefore, it is necessary to develop interpretable machine learning models that will engender trust in the knowledge they generate and contribute to clinical decision-makers intention to adopt them in the field. The goal of this dissertation was to systematically investigate the applicability of interpretable model-agnostic methods to explain predictions of black-box machine learning models for medical decision-making. As proof of concept, this study addressed the problem of predicting the risk of emergency readmissions within 30 days of being discharged for heart failure patients. Using a benchmark data set, supervised classification models of differing complexity were trained to perform the prediction task. More specifically, Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Gradient Boosting Machines (GBM) models were constructed using the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD). The precision, recall, area under the ROC curve for each model were used to measure predictive accuracy. Local Interpretable Model-Agnostic Explanations (LIME) was used to generate explanations from the underlying trained models. LIME explanations were empirically evaluated using explanation stability and local fit (R2). The results demonstrated that local explanations generated by LIME created better estimates for Decision Trees (DT) classifiers

A Blockchain System for Mobile Health Applications and Services

Com o aparecimento das tecnologias blockchain, o crescimento e adaptação de características criptográficas levaram à exploração de novos usos em novas áreas, como a computação móvel para a saúde (m-Health). Atualmente, estas tecnologias são implementadas primáriamente como mecanismos para manter os registos de saúde eletrónicos seguros. No entanto, novos estudos têm provado que estas apresentam-se como uma ferramenta poderosa para promover o controlo da informação de saúde pelos próprios pacientes e possibilita a existência de um historial médico sem alterações errôneas, para além da responsabilização dos profissionais de saúde. Nos últimos anos, verificou-se um rápido crescimento da área da m-Health, sustentada numa arquitetura orientada a serviços, levando a que a adaptação de mecanismos de blockchain em aplicações de saúde gerasse a possibilidade da existência de um serviço mais descentralizado, pessoal e disponível. A ideia de adaptar tecnologia blockchain na área da prestação de serviços de saúde apresenta inicialmente alguns pontos críticos, como por exemplo como é que é assegurada a segurança e a privacidade da informação de saúde guardada na blockchain. Normalmente, num sistema completamente descentralizado, a informação tem de estar completamente disponível a atores externos e tem de ser guardada de forma distribuída. Embora o armazenamento da informação de forma distribuída não apresente dificuldades, a particulariedade do tipo de informação que é guardada na blockchain e de que maneira esta é mantida privada e segura são questões problemáticas já conhecidas. Um breve estudo desta tecnologia é suficiente para concluir que não é adequado um registo médico de um paciente ser guardado na blockchain, uma vez que, devido ao tamanho do registo, iria gerar problemas de escalabilidade com o aumento do número de pacientes. Perante esta situação, o desempenho da blockchain iria diminuir e seria necessária uma quantidade demasiado elevada de poder computacional para a realização de tarefas básicas, gerando ainda um aumento nos requisitos de armazenamento e de transmissão em rede. Embora a blockchain não tenha capacidade para guardar a informação completa de um paciente, as suas características permitem que seja utilizada para guardar outros dados relacionados com a privacidade da informação da saúde. Deste modo, é precisamente no registo e controlo de acesso à informação de saúde que a tecnologia blockchain promete inovar. Ao registar todos os acessos à informação de saúde de um paciente, é possível criar um registo com a identificação e a autentificação de todos os utilizadores do sistema que requereram o acesso a determinada informação de saúde. Portanto, um registo de acesso consegue ser criado com uma pequena quantidade de informação, como um timestamp, com a identificação do utilizador que está a aceder aos dados e com a identificação do utilizador cujos dados estão a ser acedidos. Uma das grandes vantagens de registar a informação dos acessos numa blockchain é o facto de os registos serem distribuídos por várias localizações, sendo estas imutáveis e tolerantes a falhas e públicos. Deste modo, verifica-se que os problemas de escalabilidade, associados ao tamanho reduzido do registo, que surgem ao guardar informação na blockchain discutidos previamente conseguem ter um impacto mais reduzido. Contudo, apesar das vantagens desta tecnologia, alguns dos aspetos da sua integração desta em m-Health não são compatíveis com a natureza da informação de saúde de um paciente. Para acomodar tecnologia blockchain na área da saúde, é necessário que o sistema seja construído com várias restrições em mente. Uma destas restrições é o facto de que a informação presente na blockchain é normalmente pública, o que entra em conflito com o direito à privacidade dos pacientes e leva à necessidade de encriptar a informação. Outra restrição é o facto de como identificar um utilizador num registo de acesso, uma vez que normalmente a informação dos utilizadores é anónima. O objetivo desta dissertação é estudar como a tecnologia blockchain consegue ser conjugada com a informação de saúde recolhida ou processada por aplicações móveis. Com a finalidade de alcançar esse objetivo, foi desenvolvido um protótipo de uma solução baseada em blockchain para controlar acesso à informação de saúde. Este protótipo para além de oferecer uma segurança melhorada da informação, devido à implementação de mecanismos de criptografia, oferece um historial médico imutável ao armazenar informação de eventos de saúde numa blockchain. A esta construção foi ainda adicionado um sistema de armazenamento de dados anónimos baseado numa arquitetura de data lake. Posteriormente, este protótipo foi integrado num ambiente de teste, que consistiu em várias aplicações móveis, com o objetivo de testar detalhadamente a viabilidade e desempenho de propostas similares.With the advent of blockchain, the growth and adaptation of cryptographic features and capabilities were quickly extended to new and under-explored areas, such as healthcare. Currently, blockchain is being implemented mainly as a mechanism to secure Electronic Health Record (EHR)s. However, new studies have shown that this technology can be a powerful tool in empowering patients to control their own health data, as well as for enabling a fool-proof health data history and establishing medical responsibility. With the advent of mobile health (m-Health) sustained on service-oriented architectures, the adaptation of blockchain mechanisms into m-Health applications creates the possibility for a more decentralized and available healthcare service. The idea of adapting blockchain technology into healthcare initially presents several critical points where special consideration is required, such as how privacy and security of healthcare information can be assured if information is stored into a blockchain. Usually, for a completely decentralized system, the information has to be available to everyone and is to be stored in a distributed manner. While the storage of the information being distributed is not difficult, what kind of information should be stored into the blockchain as well as how this information can be kept private and secure present issues. A brief study of blockchain technology is enough to conclude that a full patient record is not fit to be stored into a blockchain, because the size of the record would create scalability problems as the number of patient records increases. This diminishes the performance of the blockchain to where the amount of computational power needed to perform basic tasks would rise considerably, as well as the storage and network requirements needed to permanently store the information and to replicate the information throughout the whole network, respectively. However, other uses for blockchain technology arise once the nature of the health information is analyzed thoroughly. Because of the highly personal and private aspect of health information belonging to a patient, the security of how that information is stored, transmitted and accessed becomes a main focus of health systems. It is precisely in access recording and management of healthcare information that blockchain shows promise in implementation. By recording all accesses to a the health information of a patient, it is possible to create a log of every user in a system that has had access to some information. By having a system that identifies and authenticates all users, every access to health data can be recorded as having been done by an identified user. An access record can be made with a small amount of information, such as a timestamp, an accessing user identifier and an identifier of the user whose data is being accessed. Because an access record can be accomplished with only this amount of information, the scalability issues that where discussed earlier regarding storing information into a blockchain can be mitigated. In terms of advantages, recording access information into a blockchain results in the access records being distributed across several locations, immutable, fault-tolerant and public. However, some aspects of the integration of blockchain into healthcare result in incompatibilities of the nature of health information and of blockchain. To accommodate health information and blockchain, the surrounding system must be constructed with several limitations in mind. One of which is the public nature of blockchain not being in line with the private nature of health information and therefore the information must be encrypted, or how a user can be identified in an access record if usually information in a blockchain is anonymous. This work proposes a system that successfully integrates blockchain into an m-Health testbed, outlining how both areas have evolved and their main challenges. The proposed system offers enhanced information security both in transmission, storage and access, by integrating several cryptographic mechanisms. Furthermore it is integrated with a blockchain access system and a high volume anonymous information storage mechanism based on a data lake database architecture. This system is integrated into a testbed that allows for a more detailed discussion on viability and performance of similar concepts

Effective and Secure Healthcare Machine Learning System with Explanations Based on High Quality Crowdsourcing Data

Affordable cloud computing technologies allow users to efficiently outsource, store, and manage their Personal Health Records (PHRs) and share with their caregivers or physicians. With this exponential growth of the stored large scale clinical data and the growing need for personalized care, researchers are keen on developing data mining methodologies to learn efficient hidden patterns in such data. While studies have shown that those progresses can significantly improve the performance of various healthcare applications for clinical decision making and personalized medicine, the collected medical datasets are highly ambiguous and noisy. Thus, it is essential to develop a better tool for disease progression and survival rate predictions, where dataset needs to be cleaned before it is used for predictions and useful feature selection techniques need to be employed before prediction models can be constructed. In addition, having predictions without explanations prevent medical personnel and patients from adopting such healthcare deep learning models. Thus, any prediction models must come with some explanations. Finally, despite the efficiency of machine learning systems and their outstanding prediction performance, it is still a risk to reuse pre-trained models since most machine learning modules that are contributed and maintained by third parties lack proper checking to ensure that they are robust to various adversarial attacks. We need to design mechanisms for detection such attacks. In this thesis, we focus on addressing all the above issues: (i) Privacy Preserving Disease Treatment & Complication Prediction System (PDTCPS): A privacy-preserving disease treatment, complication prediction scheme (PDTCPS) is proposed, which allows authorized users to conduct searches for disease diagnosis, personalized treatments, and prediction of potential complications. (ii) Incentivizing High Quality Crowdsourcing Data For Disease Prediction: A new incentive model with individual rationality and platform profitability features is developed to encourage different hospitals to share high quality data so that better prediction models can be constructed. We also explore how data cleaning and feature selection techniques affect the performance of the prediction models. (iii) Explainable Deep Learning Based Medical Diagnostic System: A deep learning based medical diagnosis system (DL-MDS) is present which integrates heterogeneous medical data sources to produce better disease diagnosis with explanations for authorized users who submit their personalized health related queries. (iv) Attacks on RNN based Healthcare Learning Systems and Their Detection & Defense Mechanisms: Potential attacks on Recurrent Neural Network (RNN) based ML systems are identified and low-cost detection & defense schemes are designed to prevent such adversarial attacks. Finally, we conduct extensive experiments using both synthetic and real-world datasets to validate the feasibility and practicality of our proposed systems