A comprehensive review on medical diagnosis using machine learning

The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine learning could assist the doctors in making decisions on time, and could also be used as a second opinion or supporting tool. This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases. We present the various machine learning algorithms used over the years to diagnose various diseases. The results of this study show the distribution of machine learningmethods by medical disciplines. Based on our review, we present future research directions that could be used to conduct further research

Heart rate variability in patients with Chagas’ heart disease

Se estima la prevalencia por Trypanosoma cruzi de 436.000 habitantes en Colombia, con casos anuales de 5.250, en población expuesta de 4.792.000 habitantes. La variabilidad de la frecuencia cardiaca (VFC) es la variabilidad en el tiempo, de un latido cardiaco, medido en un análisis de periodo temporal determinado. Su análisis permite determinar el equilibrio simpático-vagal de manera indirecta. Este estudio analizó las variables temporales y No lineales, en 19 pacientes Chagásicos y 19 controles, utilizándose un polígrafo de alta resolución y el software Kubios. La variable Desviación Estandar del Intervalo RR (SDRR) en la población control arrojó un promedio de 56,23± 29,6ms vs 40,62 ± 30,1ms, en los seropositivos; La Raíz Cuadrada del Promedio de la Suma de las Diferencias al Cuadrado de todos los intervalos Adyacentes (RMSSD) fue de 34,31 ± 21,01ms y 31,94 ± 37,33ms, para controles y Chagas, respectivamente. El número de los intervalos RR consecutivos, que difi eren en más de 50ms entre sí (NN50), en controles 76,47 ± 78,3 latidos vs 13,47 ± 36,8 para seropositivos, que correspondió con el porcentaje de NN50 (pNN50) 12,3 ± 13,3% y 2,64 ± 6,0%, espectivamente, para el mismo orden de los grupos. Valores de Entropía Aproximada (ApEn) fueron 1,249± 0,134, para controles y 0,959 ± 0,325, para seropositivos y para la Entropía Muestral (SampEn) fue de 1,358 ± 0,264 y 1,102 ± 0,385, para controles y chagásicos, respectivamente. Se encontró mayor irregularidad de HRV en controles, que es refl ejo de un mejor estado de salud.The prevalence by Trypanosoma cruzi of 436,000 inhabitants in Colombia is estimated, with annual cases of 5,250, in an exposed population of 4,792,000 inhabitants. Heart rate variability (HRV) is the time interval of a beat in a given time analysis. Its analysis allows to determine the sympathetic-vagal balance indirectly. This study analyzed the temporal and non-linear variables in 19 Chagasic patients and 19 controls, using a high-resolution polygraph and the Kubios software. The variable Standard Deviation of the RR Interval (SDRR) in the control population showed an average of 56.23±29.6ms vs 40.62±30.1ms in the seropositive; The Square Root of the Sum of the Square Differences of all Adjacent intervals (RMSSD) was 34.31±21.01ms and 31.94±37.33ms for controls and Chagas respectively. The number of consecutive RR intervals that differ by more than 50ms from each other (NN50) in controls 76.47±78.3 beats vs 13.47±36.8 for seropositives corresponding to the percentage of NN50 (pNN50) 12.3±13.3% and 2.64±6.0%, respectively for the same order of the groups. Approximate Entropy (ApEn) values were 1,249±0,134 for controls and 0,959±0,325 for seropositive, and for Sample Entropy (SampEn) it was 1,358±0,264 and 1,102±0,385 for controls and chagasics respectively. Greater irregularity of HRV was found in controls, which reflects a better state of health

Modeling of the spatiotemporal distribution patterns and transmission dynamics of dengue, for an early warning surveillance system

As doenças emergentes transmitidas por vetores representam um desafio significativo para a saúde pública global. Nos últimos tempos, os surtos de doenças como a dengue e a febre de chikungunya, aumentaram em frequência. Tal é facilitado pela globalização, pelo aumento do comércio e das viagens, e pela dispersão para novas áreas dos seus vetores invasores. Na Europa, este facto é exemplificado pela recente introdução e estabelecimento de espécies de mosquitos do género Aedes com a subsequente ocorrência de surtos de doenças como a dengue. Com a crescente disseminação da dengue em todo o mundo, a região europeia também tem vindo a registar um aumento de casos - a maioria destes relacionados com viagens. Da mesma forma, tem havido um aumento de eventos esporádicos de transmissão autóctone de dengue em áreas onde ocorre o vetor sob condições ambientais favoráveis. Assim, atualmente, a Europa enfrenta o desafio de avaliar o risco de importação de casos virémicos de dengue e a probabilidade de ocorrência de transmissão local deste vírus. Esta tese visa contribuir para a compreensão dos fatores relacionados com a importação do vírus da dengue na Europa e a sua transmissão neste território, nomeadamente na ilha da Madeira. Para tal foi implementado uma estrutura integrada de modelos computacionais da importação e transmissão da doença. A estrutura combina três submodelos: (i) um modelo explicativo de importação da doença assente em teoria de redes (ii) um modelo preditivo de aprendizagem automática e, (iii) um modelo compartimental de transmissão vetor-hospedeiro. Os modelos de teoria de redes e de aprendizagem automática foram parametrizados com recurso a dados históricos referentes a estimativas de casos importados de dengue em 21 países na Europa e índices que caracterizam parâmetros com relevância na importação da dengue: (i) tráfego de passageiros aéreos, (ii) atividade e sazonalidade da dengue, (iii) taxa de incidência, (iv) proximidade geográfica, (v) vulnerabilidade à epidemia, e, (vi) contexto económico do país de origem. O modelo compartimental de transmissão foi calibrado com parâmetros empíricos referentes ao ciclo de vida do mosquito, à transmissão viral e à variação anual de temperatura do Funchal, na ilha da Madeira. Os resultados dos modelos de teoria de redes e aprendizagem automática demonstram um maior risco de importação de casos virémicos de países com elevado tráfego de passageiros, elevadas taxas de incidência, situação económica débil e com maior proximidade geográfica em relação ao país de destino. O modelo de aprendizagem automática alcançou elevada performance preditiva, com uma pontuação AUC de 0,94. O modelo compartimental de transmissão demonstra a existência de um potencial de transmissão da dengue no Funchal nos períodos de verão e outono, com a data de chegada da pessoa infeciosa a afetar significativamente a distribuição no tempo e tamanho do pico da epidemia. Da mesma forma, a variação sazonal da temperatura afeta dramaticamente a dinâmica da epidemia, em que temperaturas iniciais mais quentes levam a surtos de maiores proporções, com o pico de casos a ocorrer mais cedo. A estrutura de modelação descrita nesta tese tem o potencial de servir como uma ferramenta integrada de vigilância de alerta precoce para a ocorrência de surtos de dengue na Europa. Este trabalho fornece orientação prática para auxiliar as autoridades de saúde pública na prevenção de surtos de dengue e na redução do risco de transmissão local, em áreas onde ocorrem os vetores. Essa estrutura, com os devidos reajustamentos, pode ser aplicada a outras doenças transmitidas por Aedes, como chikungunya e febre amarela.Emerging vector-borne diseases pose a significant global public health challenge. In recent times, outbreaks of diseases, such as dengue and chikungunya fever, have increased in frequency. This is facilitated by globalization, increase in trade and travel, and the spread of invasive vectors into new areas. In Europe, this is exemplified by the recent introduction and establishment of Aedes mosquito species and subsequent outbreaks of diseases like dengue. With the increasing spread of dengue worldwide, the European region has also experienced increase in reported cases - majority being travel related. Likewise, there has been an increase in sporadic events of autochthonous dengue transmission, in areas with established vector presence and favourable environmental conditions. Europe is currently faced with the challenge of assessing its importation risk of viraemic cases of dengue, and the probability of local transmission. This thesis aims to study the dynamics of viraemic cases importation and virus transmission of dengue fever in Europe, namely in Madeira Island. This is achieved by establishing an importation and transmission modelling framework. The framework combines three sub-models: (i) a network connectivity importation model (ii) a machine learning predictive model and, (iii) a compartmental vector-host transmission model. The network connectivity and machine learning model were both parameterized using a historical dengue importation data for 21 countries in Europe, and indices that characterize important parameters for dengue importation: (i) the air passenger traffic, (ii) dengue activity and seasonality, (iii) incidence rate, (iv) geographical proximity, (v) epidemic vulnerability, and (vi) wealth of a source country. The transmission model was calibrated using empirical parameters for the mosquito life history traits, viral transmission, and temperature seasonality of Funchal, Madeira Island. The results of the network connectivity and machine learning models demonstrate a higher importation risk of a viraemic case from source countries with high passenger traffic, high incidence rates, lower economic status, and geographical proximity to a destination country. The machine learning model achieved high predictive accuracy with an AUC score of 0.94. The transmission model demonstrates the potential for summer and autumn season transmission of dengue in Funchal, with the arrival date of the infectious person significantly affecting the distribution of the timing and peak size of the epidemic. Likewise, seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing large epidemics with peaks occurring more rapidly. The modelling framework described in this thesis has the potential to serve as an integrated early warning surveillance tool for dengue in Europe. This work provides practical guidance to assist public health officials in preventing outbreaks of dengue and reducing the risk of local transmission in areas with vectors presence. This framework could be applied to other Aedes-borne diseases such as chikungunya and yellow fever

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

The neuro-cognitive representation of word meaning resolved in space and time.

One of the core human abilities is that of interpreting symbols. Prompted with a perceptual stimulus devoid of any intrinsic meaning, such as a written word, our brain can access a complex multidimensional representation, called semantic representation, which corresponds to its meaning. Notwithstanding decades of neuropsychological and neuroimaging work on the cognitive and neural substrate of semantic representations, many questions are left unanswered. The research in this dissertation attempts to unravel one of them: are the neural substrates of different components of concrete word meaning dissociated? In the first part, I review the different theoretical positions and empirical findings on the cognitive and neural correlates of semantic representations. I highlight how recent methodological advances, namely the introduction of multivariate methods for the analysis of distributed patterns of brain activity, broaden the set of hypotheses that can be empirically tested. In particular, they allow the exploration of the representational geometries of different brain areas, which is instrumental to the understanding of where and when the various dimensions of the semantic space are activated in the brain. Crucially, I propose an operational distinction between motor-perceptual dimensions (i.e., those attributes of the objects referred to by the words that are perceived through the senses) and conceptual ones (i.e., the information that is built via a complex integration of multiple perceptual features). In the second part, I present the results of the studies I conducted in order to investigate the automaticity of retrieval, topographical organization, and temporal dynamics of motor-perceptual and conceptual dimensions of word meaning. First, I show how the representational spaces retrieved with different behavioral and corpora-based methods (i.e., Semantic Distance Judgment, Semantic Feature Listing, WordNet) appear to be highly correlated and overall consistent within and across subjects. Second, I present the results of four priming experiments suggesting that perceptual dimensions of word meaning (such as implied real world size and sound) are recovered in an automatic but task-dependent way during reading. Third, thanks to a functional magnetic resonance imaging experiment, I show a representational shift along the ventral visual path: from perceptual features, preferentially encoded in primary visual areas, to conceptual ones, preferentially encoded in mid and anterior temporal areas. This result indicates that complementary dimensions of the semantic space are encoded in a distributed yet partially dissociated way across the cortex. Fourth, by means of a study conducted with magnetoencephalography, I present evidence of an early (around 200 ms after stimulus onset) simultaneous access to both motor-perceptual and conceptual dimensions of the semantic space thanks to different aspects of the signal: inter-trial phase coherence appears to be key for the encoding of perceptual while spectral power changes appear to support encoding of conceptual dimensions. These observations suggest that the neural substrates of different components of symbol meaning can be dissociated in terms of localization and of the feature of the signal encoding them, while sharing a similar temporal evolution

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Neglect in policy problems: the case of 'neglected tropical diseases'

This thesis is concerned with how previously neglected issues, in this instance tropical diseases, gain prominence on policy agendas, and shows how advocacy and measurement are used to bring issues to the attention of policymakers. The term 'neglected tropical diseases' (NTDs) was coined in the early 2000s to describe lesser-known diseases that existed in the shadow of the high-profile and well-funded “big three” – HIV/AIDS, tuberculosis (TB), and malaria. The case of NTDs demonstrates how a policy problem can be understood amidst connections being drawn or not drawn between issues, and the forms of intervention taken to address neglect in policy. Thus, the central question of this thesis is: How did a re-labeled disease category within a decade result in billions of funding being directed towards a previously 'neglected' issue, with global commitments for control, elimination, and eradication? The analysis is presented in two parts and shows how NTDs have gained acknowledgement and care through the concept of neglect. The first part involves the conceptualization of common characteristics and methods of standardizing a disease grouping, which is far from a straightforward process as various lists of NTDs attest. The second part, through a sociohistorical analysis of the origins and policy development of NTDs, demonstrates how policy appeal is created through the use of both advocacy and measurement, more usually treated as distinct areas within global health policy. It draws on interviews with 55 actors from scientists, to policy officials, NGO workers, and academics, and also undertakes a documentary analysis, which includes historical sources. Using theoretical perspectives from Science and Technology Studies, Public Policy, and Political Economy, this thesis demonstrates what the concept of neglect brings to understanding policy problems. It concludes that both the perception and responses to neglect in policy can be understood in four distinct and overlapping ways, through: information, action, feeling and thought

Informatics for Health 2017 : advancing both science and practice

Conference report, The Informatics for Health congress, 24-26 April 2017, in Manchester, UK.Introduction : The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress. Discussion : The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes. Conclusions : Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.Publisher PDFPeer reviewe