Automatic diagnosis of primary headaches by machine learning methods

Open Medicine ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. 157-165 ◽  
Author(s):  
Bartosz Krawczyk ◽  
Dragan Simić ◽  
Svetlana Simić ◽  
Michał Woźniak
Keyword(s):  
Machine Learning ◽  
Negative Impact ◽  
Life Quality ◽  
Primary Headache ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Common Disease ◽  
Primary Headaches ◽  
Starting Point ◽  
Headache Diagnosis

AbstractPrimary headaches are common disease of the modern society and it has high negative impact on the productivity and the life quality of the affected person. Unfortunately, the precise diagnosis of the headache type is hard and usually imprecise, thus methods of headache diagnosis are still the focus of intense research. The paper introduces the problem of the primary headache diagnosis and presents its current taxonomy. The considered problem is simplified into the three class classification task which is solved using advanced machine learning techniques. Experiments, carried out on the large dataset collected by authors, confirmed that computer decision support systems can achieve high recognition accuracy and therefore be a useful tool in an everyday physician practice. This is the starting point for the future research on automation of the primary headache diagnosis.

scholarly journals Combining Virtual Reality and Organizational Neuroscience for Leadership Assessment

2021 ◽  
Vol 11 (13) ◽  
pp. 5956
Author(s):  
Elena Parra ◽  
Irene Alice Chicchi Giglioli ◽  
Jestine Philip ◽  
Lucia Amalia Carrasco-Ribelles ◽  
Javier Marín-Morales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Ecological Validity ◽  
Objective Assessment ◽  
Leadership Skills ◽  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Promising Tool ◽  
Future Research Agenda

In this article, we introduce three-dimensional Serious Games (3DSGs) under an evidence-centered design (ECD) framework and use an organizational neuroscience-based eye-tracking measure to capture implicit behavioral signals associated with leadership skills. While ECD is a well-established framework used in the design and development of assessments, it has rarely been utilized in organizational research. The study proposes a novel 3DSG combined with organizational neuroscience methods as a promising tool to assess and recognize leadership-related behavioral patterns that manifest during complex and realistic social situations. We offer a research protocol for assessing task- and relationship-oriented leadership skills that uses ECD, eye-tracking measures, and machine learning. Seamlessly embedding biological measures into 3DSGs enables objective assessment methods that are based on machine learning techniques to achieve high ecological validity. We conclude by describing a future research agenda for the combined use of 3DSGs and organizational neuroscience methods for leadership and human resources.

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

scholarly journals Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach

2021 ◽  
Author(s):  
Kasper van Mens ◽  
Sascha Kwakernaak ◽  
Richard Janssen ◽  
Wiepke Cahn ◽  
Joran Lokkerbol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Service Use ◽  
Visual Analysis ◽  
Mental Healthcare ◽  
Machine Learning Techniques ◽  
Individual Treatment ◽  
Learning Approach ◽  
Group Level ◽  
Starting Point ◽  
Machine Learning Approach

AbstractA mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a machine learning approach to forecast expected service use, as a starting point for agreements between financiers and suppliers of mental healthcare. This study used administrative data from a large mental healthcare organization in the Netherlands. A training set was selected using records from 2017 (N = 10,911), and a test set was selected using records from 2018 (N = 10,201). A baseline model and three random forest models were created from different types of input data to predict (the remainder of) numeric individual treatment hours. A visual analysis was performed on the individual predictions. Patients consumed 62 h of mental healthcare on average in 2018. The model that best predicted service use had a mean error of 21 min at the insurance group level and an average absolute error of 28 h at the patient level. There was a systematic under prediction of service use for high service use patients. The application of machine learning techniques on mental healthcare data is useful for predicting expected service on group level. The results indicate that these models could support financiers and suppliers of healthcare in the planning and allocation of resources. Nevertheless, uncertainty in the prediction of high-cost patients remains a challenge.

Insights into fish-anthropogenic pressures relationships using machine learning techniques: the case of Castilla-La Mancha (Spain)

2021 ◽  
Author(s):  
Carlotta Valerio ◽  
Graciela Gómez Nicola ◽  
Rocío Aránzazu Baquero Noriega ◽  
Alberto Garrido ◽  
Lucia De Stefano
Keyword(s):  
Machine Learning ◽  
Fish Species ◽  
Conservation Status ◽  
Boosted Regression Trees ◽  
Machine Learning Techniques ◽  
Freshwater Species ◽  
Anthropogenic Pressures ◽  
La Mancha ◽  
Learning Techniques ◽  
Starting Point

<p>Since 1970 the number of freshwater species has suffered a decline of 83% worldwide and anthropic activities are considered to be major drivers of ecosystems degradation. Linking the ecological response to the multiple anthropogenic stressors acting in the system is essential to effectively design policy measures to restore riverine ecosystems. However, obtaining quantitative links between stressors and ecological status is still challenging, given the non-linearity of the ecosystem response and the need to consider multiple factors at play. This study applies machine learning techniques to explore the relationships between anthropogenic pressures and the composition of fish communities in the river basins of Castilla-La Mancha, a region covering nearly 79 500 km² in central Spain. During the past two decades, this region has experienced an alarming decline of the conservation status of native fish species. The starting point for the analysis is a 10x10 km grid that defines for each cell the presence or absence of several fish species before and after 2001. This database was used to characterize the evolution of several metrics of fish species richness over time, accounting for the species origin (native or alien), species features (e.g. pollution tolerance) and habitat preferences. Random Forest and Gradient Boosted Regression Trees algorithms were used to relate the resulting metrics to the stressor variables describing the anthropogenic pressures acting in the rivers, such as urban wastewater discharges, land use cover, hydro-morphological degradation and the alteration of the river flow regime. The study provides new, quantitative insights into pressures-ecosystem relationships in rivers and reveals the main factors that lead to the decline of fish richness in Castilla-La Mancha, which could help inform environmental policy initiatives.</p>

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data

2019 ◽  
Vol 21 (3) ◽  
pp. 80-92
Author(s):  
Madhuri Gupta ◽  
Bharat Gupta
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Research Work ◽  
Cancer Recurrence ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Learning Techniques ◽  
Learning Frameworks ◽  
Processing Engine

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.

Whiplash Headache is Transitory Worsening of a Pre-Existing Primary Headache

Cephalalgia ◽  
2008 ◽  
Vol 28 (1_suppl) ◽  
pp. 28-31 ◽  
Author(s):  
LJ Stovner ◽  
D Obelieniene
Keyword(s):  
Neck Pain ◽  
Primary Headache ◽  
Control Group ◽  
Primary Headaches ◽  
Headache Diagnosis ◽  
Police Records ◽  
Characteristic Features ◽  
Long Term Prognosis ◽  
Chronic Whiplash

Acute and chronic whiplash headache are new diagnostic entities in the ICHD-2 (5.3, 5.4). In a prospective cohort study, 210 rear-end collision victims were identified consecutively from police records and asked about head and neck pain in questionnaires after 2 weeks, 3 months and 1 year. The results were compared with those of matched controls who were also followed for 1 year. Of 210 accident victims, 75 developed headache within 7 days. Of these, 37 had also neck pain and complied with the criteria for acute whiplash headache. These 37 had the same headache diagnoses, headache features, accompanying symptoms and long-term prognosis as the 38 without initial neck pain who therefore did not comply with the acute whiplash headache diagnosis. Previous headache was a major risk factor for headache both in the acute and chronic stage. Compared with the non-traumatized controls, headache in the whiplash group had the same prevalence, the same diagnoses and characteristic features, and the same prognosis. Both acute and chronic whiplash headache lack specificity compared with the headache in a control group, and have the same long-term prognosis, indicating that such headaches are primary headaches, probably elicited by the stress of the situation.

scholarly journals Machine learning for energy-water nexus: challenges and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 228-267 ◽  
Author(s):  
Zaidi ◽  
Chandola ◽  
Allen ◽  
Sanyal ◽  
Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Technological Advancement ◽  
Research Directions ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
System Sustainability

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

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