Forecasting migraine with machine learning based on mobile phone diary and wearable data

INTRODUCTION: Triggers, premonitory symptoms and physiological changes occur in the preictal migraine phase and may be used in models for forecasting attacks. Machine learning is a promising option for such predictive analytics. The objective of this study was to explore the utility of machine learning to forecast migraine attacks based on preictal headache diary entries and simple physiological measurements. METHODS: In a prospective development and usability study 18 patients with migraine completed 388 headache diary entries and self-administered app-based biofeedback sessions wirelessly measuring heart rate, peripheral skin temperature and muscle tension. Several standard machine learning architectures were constructed to forecast headache the subsequent day. Models were scored with area under the receiver operating characteristics curve. RESULTS: Two-hundred-and-ninety-five days were included in the predictive modelling. The top performing model, based on random forest classification, achieved an area under the receiver operating characteristics curve of 0.62 in a hold-out partition of the dataset. DISCUSSION: In this study we demonstrate the utility of using mobile health apps and wearables combined with machine learning to forecast headache. We argue that high-dimensional modelling may greatly improve forecasting and discuss important considerations for future design of forecasting models using machine learning and mobile health data

Data Fusion and Systems Engineering Approaches for Quality and Performance Improvement of Health Care Systems: From Diagnosis to Care to System-level Decision-making

abstract: Technology advancements in diagnostic imaging, smart sensing, and health information systems have resulted in a data-rich environment in health care, which offers a great opportunity for Precision Medicine. The objective of my research is to develop data fusion and system informatics approaches for quality and performance improvement of health care. In my dissertation, I focus on three emerging problems in health care and develop novel statistical models and machine learning algorithms to tackle these problems from diagnosis to care to system-level decision-making. The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical definitions of subtypes use somewhat arbitrary boundaries primarily based on patient self-reported symptoms, which are subjective and error-prone. My research develops a novel Multimodality Factor Mixture Model that discovers subtypes of migraine from multimodality imaging MRI data, which provides complementary accurate measurements of the disease. Patients in the different subtypes show significantly different clinical characteristics of the disease. Treatment tailored and optimized for patients of the same subtype paves the road toward Precision Medicine. The second topic focuses on coordinated patient care. Care coordination between nurses and with other health care team members is important for providing high-quality and efficient care to patients. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables large-scale quantitative data to be collected. My research develops a novel Multi-response Multi-level Model (M3) that enables transfer learning in NCCI data fusion. M3 identifies key factors that contribute to improving care coordination, and facilitates the design and optimization of nurses’ training, workload assignment, and practice environment, which leads to improved patient outcomes. The last topic is about system-level decision-making for Alzheimer’s disease early detection at the early stage of Mild Cognitive Impairment (MCI), by predicting each MCI patient’s risk of converting to AD using imaging and proteomic biomarkers. My research proposes a systems engineering approach that integrates the multi-perspectives, including prediction accuracy, biomarker cost/availability, patient heterogeneity and diagnostic efficiency, and allows for system-wide optimized decision regarding the biomarker testing process for prediction of MCI conversion.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Demographic and Driving Performance Factors in Simulator Adaptation Syndrome

Simulation is an important option for testing at-risk drivers with medical impairments. Simulator Adaptation Syndrome (SAS), characterized by autonomic symptoms, presents a drawback to testing. This study investigated new issues regarding susceptibility of neurologically impaired drivers to SAS, scenario situations most likely to cause SAS, and effects of SAS on driver performance. Subjects were 164 drivers enrolled in larger ongoing studies of at-risk older drivers. Eighteen had Alzheimer’s disease (AD), 44 stroke, and 102 were neurologically normal controls. Experimental drives were conducted using a fixed-base high-fidelity simulator with a 150º forward field of view. Each driver completed a questionnaire immediately after driving in the simulator, rating any feelings of discomfort along nine dimensions; an overall discomfort score was calculated. Of the 164 drivers, 130 completed the full drive and 34 ended the drive early. Drivers with higher overall discomfort scores were more likely to drop out before completing a drive. Specific symptoms strongly predicted dropping out, namely dizziness, nervousness, light-headedness, body temperature increase, and nausea. Simulator dropout rates and reported discomfort scores were significantly greater in women than men, but did not differ between drivers with AD or stroke and neurologically normal drivers. Comparisons between 32 Dropouts and 32 Non-Dropouts (matched by age, gender, neurological impairment, and scenario driven) showed no evidence that higher levels of discomfort cause a driver to perform atypically before the point of dropout. We could relate dropout to specific segments and events in the drive that required abrupt braking

11th European Headache Federation Congress jointly with 31st Congress of the Italian Society for the Study of Headaches : Rome, Italy. 01-03 December 2017

. Aims of the study were explore the relationship between peripheral chromatic and central visual dysfunction evaluating also the presence of functional receptor impairment in patients with migraine, with and without aura examined interictally

Modelado robusto para la extracción de información en entornos biofísicos y críticos

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 12/07/2018The era of information and Big Data is an environment where multiple devices, always connected, generate huge volumes of information (paradigm of the Internet of Things). This paradigm is present in different areas: the Smart Cities, sport tracking, lifestyle, or health. The goal of this thesis is the development and implementation of a Robust predictive modeling methodology using low cost wearable devices in biophysical and critical scenarios. In this manuscript we present a multilevel architecture that covers from the on-node data processing, up to the data management in Data Centers. The methodology applies energy aware optimization techniques at each level of the network. And the decision system makes use of data from different sources leading to expert decision system...La era de la información y el Big Data, se sustenta en un entorno en el que múltiples dispositivos, siempre conectados, generan ingentes volúmenes de información (paradigma del Internet de las Cosas). Este paradigma ha llegado diversos entornos: las denominadas ciudades inteligentes, monitorización deportiva, estilo de vida, o salud. El objetivo de esta tesis es el desarrollo e implementación de una metodología de modelado predictivo robusto mediante dispositivos wearable de bajo coste en entornos biofísicos y críticos. A lo largo de este manuscrito se presenta una arquitectura multinivel que abarca desde el tratamiento de los datos en los dispositivos sensores hasta el manejo de éstos en centros de datos. La metodología cubre la optimización energética a todos los niveles con consciencia del estado de la red. Y el sistema de decisión hace uso de datos de distintas fuentes para conformar un sistema experto de decisión...Fac. de InformáticaTRUEunpu

Soil Erosion: A Review of Models and Applications

Soil erosion is a global environmental problem influenced by both natural and human factors. Modeling provides a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions, and is needed to guide the comprehensive control of soil erosion. Over the years various soil erosion models have been developed. The application of these models is dependent on the soil type and climate of the given area because models differ in complexity and input requirements. This paper reviews various soil erosion models and their applications, focusing more on the most widely applied models which are: Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) and Water Erosion Prediction Project (WEPP). The method used for this research is a review of academic articles, bulletins, conference papers, textbooks, research reports and publicly available materials on soil erosion models and their applications. The results of this study revealed that most soil erosion models have been developed for the assessment of rill and interill erosion at plot or catchment scale on agricultural lands and watersheds in terms of estimating mostly soil loss, sediment yield, erodibility (K) values, rainfall factor (R) factors, runoff rates and forecasts of likely impacts. Again, the study indicated that most previous authors on soil erosion assessment used the empirical models due to their limited data and parameter inputs. Recommendations of this study include: (1) expansion of the USLE and RUSLE models for the simulation of gully erosion and sediment processes; (2) researchers should be encouraged through grants to develop empirical models (that make use of limited data) based on rainfall (R) factor and erodibility (K) factor that provide two opposing forces in soil erosion processes; and (3) management of soil erosion based on the indigenous knowledge of the affected people and land holders

Differential Co-Abundance Network Analyses for Microbiome Data Adjusted for Clinical Covariates Using Jackknife Pseudo-Values

A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients.Comment: 23 pages, 2 figures, 4 table