Robust and accurate modeling approaches for migraine Per-Patient prediction from ambulatory data

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives

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

Headache Disease Type Classification and Predicting System using Data Mining Techniques

In this Paper, Migraine Headache and the types of Migraine Headache were analyzed using Data mining Techniques. From the headache diary the data is being collected. Naïve Bayes is used on the collected data to determine the data corresponds to which category of Headache. It is also called as probabilistic algorithm which is used to predict and classify the category of Headache into the classes like Migraine, Cluster, Sinus and Tension Headache. Apply Decision tree,C4.5 algorithm on the Migraine Class and classify the data set into three class(Pediatric migraine, Episodic migraine, Chronic migraine) based on the on certain conditions

Continuous and automated data collection in migraine research - Extending the data collection capabilities of the Empatica E4

Migraine is a recurrent headache disorder that afflicts significant portions of the global population. There is no current cure and migraines are mainly managed through symptomatic medical treatments and manual biofeedback routines. Automated data collection and prediction of migraine attacks through machine learning could be viable approaches for helping migraineurs and for reducing the impact of migraines, both on a societal and an individual level. However, machine learning approaches require access to large amounts of high-quality real-time data for facilitating prompt and reliable prediction under everyday conditions and within useful timeframes. The Empatica E4 is an unobtrusive wearable sensor device that can satisfy these data collection needs, although not without flaws and shortcomings. Several studies have reported issues with E4 data collection, most regarding participant involvement and the logistical aspects of the collection process. On top of this, the native systems provided by Empatica for storing, retrieving, and utilizing collected data do not properly facilitate real-time data analysis or machine learning approaches. This project creates a flexible data collection solution based on the E4 for facilitating real-time prediction of migraine attacks. It incorporates features and elements for increasing user involvement and for maximizing the data collection potential of the E4. Additionally, the solution is integrated with the mSpider data storage platform, facilitating reliable and flexible data storage and retrieval options. The prototype system was tested on three potential end-users under everyday conditions over the course of 20 days. After the data collection period, each user attended a semi-structured interview. Testing and interview results show that the data collection capabilities of the prototype system are on-par with other similar systems, it offers stable data collection under everyday conditions, and it can store data in the mSpider system. However, the added features for increasing participant involvement had little discernible effect on the data collection process or the amount of collected data. This was probably caused by the low intensity of the added features or the short duration of the testing period. Additionally, the testing process found that the high technical proficiency requirements and the necessary daily maintenance of the E4 makes it unsuited for continuous migraine treatment purposes, although it is a good tool for migraine research. Future prototype iterations should increase the intensity of the participant involvement features and greatly increase the length of testing periods

Epilepsia

PurposeA subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction.MethodsSubjects 18 or older with LRE and \ue2\u2030\ua53 seizures/month maintained an e-diary, reporting AM/PM data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, \ue2\u20ac\u153How likely are you to experience a seizure [time frame]\ue2\u20ac?? Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations.Key FindingsNineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for \ue2\u20ac\u153almost certain\ue2\u20ac?. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant.SignificanceSome persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self awareness of mood and premonitory features. The 6-hour prediction window is suitable for the development of pre-emptive therapy.1R01DA031275-01A1/DA/NIDA NIH HHS/United States5P60AA003510-33/AA/NIAAA NIH HHS/United States5R01AA016599-03/AA/NIAAA NIH HHS/United States5R01AA12827-07/AA/NIAAA NIH HHS/United StatesK23AG030857/AG/NIA NIH HHS/United StatesK23NS05140901A1/NS/NINDS NIH HHS/United StatesK23NS47256/NS/NINDS NIH HHS/United StatesP01 AG003949/AG/NIA NIH HHS/United StatesP01 AG027734/AG/NIA NIH HHS/United StatesP01 AG03949/AG/NIA NIH HHS/United StatesP30 CA13330-35/CA/NCI NIH HHS/United StatesR01 AG022092/AG/NIA NIH HHS/United StatesR01 AG034087/AG/NIA NIH HHS/United StatesR01 NS053998/NS/NINDS NIH HHS/United StatesR01AG022374-06A2/AG/NIA NIH HHS/United StatesR01AG025119/AG/NIA NIH HHS/United StatesR01AG034119/AG/NIA NIH HHS/United StatesR01AG12101/AG/NIA NIH HHS/United StatesR21 AG036935/AG/NIA NIH HHS/United StatesU01-OH10411/OH/NIOSH CDC HHS/United StatesU01-OH10412/OH/NIOSH CDC HHS/United StatesUL1-RR025750-01/RR/NCRR NIH HHS/United States2014-11-01T00:00:00Z24111898PMC383327

Wishes For Wearables From Patients With Migraine

Migraine is a long-term failure mode, including a risk of disease-related deficits, that leads to social exclusion. The study was conducted among members of the Finnish Migraine Association and was aimed at identifying migraine patients with pre-symptoms and whether they would be willing to use wearable sensors to detect pre-symptoms. The survey received responses from 565 persons, 90% of whom were willing to use wearable sensors to measure pre-symptoms and support treatment. Moreover, the study revealed that 87.8% of migraine patients identified migraine’s early symptoms, the most common of which are tiredness, slow thinking, difficulty finding words and visual disturbances. Most of the respondents wanted the device placed on their wrist as a watch, wristband or skin patch

Outpatient Emergency Department Utilization: Measurement and Prediction: A Dissertation

Approximately half of all emergency department (ED) visits are primary-care sensitive (PCS) – meaning that they could potentially be avoided with timely, effective primary care. Reducing undesirable types of healthcare utilization (including PCS ED use) requires the ability to define, measure, and predict such use in a population. In this retrospective, observational study, we quantified ED use in 2 privately insured populations and developed ED risk prediction models. One dataset, obtained from a Massachusetts managed-care network (MCN), included data from 2009-11. The second was the MarketScan database, with data from 2007-08. The MCN study included 64,623 individuals enrolled for at least 1 base-year month and 1 prediction-year month in Massachusetts whose primary care provider (PCP) participated in the MCN. The MarketScan study included 15,136,261 individuals enrolled for at least 1 base-year month and 1 prediction-year month in the 50 US states plus DC, Puerto Rico, and the US Virgin Islands. We used medical claims to identify principal diagnosis codes for ED visits, and scored each according to the New York University Emergency Department algorithm. We defined primary-care sensitive (PCS) ED visits as those in 3 subcategories: nonemergent, emergent but primary-care treatable, and emergent but preventable/avoidable. We then: 1) defined and described the distributions of 3 ED outcomes: any ED use; number of ED visits; and a new outcome, based on the NYU algorithm, that we call PCS ED use; 2) built and validated predictive models for these outcomes using administrative claims data; 3) compared the performance of models predicting any ED use, number of ED visits, and PCS ED use; 4) enhanced these models by adding enrollee characteristics from electronic medical records, neighborhood characteristics, and payor/provider characteristics, and explored differences in performance between the original and enhanced models. In the MarketScan sample, 10.6% of enrollees had at least 1 ED visit, with about half of utilization scored as PCS. For the top risk group (those in the 99.5th percentile), the model’s sensitivity was 3.1%, specificity was 99.7%, and positive predictive value (PPV) was 49.7%. The model predicting PCS visits yielded sensitivity of 3.8%, specificity of 99.7%, and PPV of 40.5% for the top risk group. In the MCN sample, 14.6% (±0.1%) had at least 1 ED visit during the prediction period, with an overall rate of 18.8 (±0.2) visits per 100 persons and 7.6 (±0.1) PCS ED visits per 100 persons. Measuring PCS ED use with a threshold-based approach resulted in many fewer visits counted as PCS, discarding information unnecessarily. Out of 45 practices, 5 to 11 (11-24%) had observed values that were statistically significantly different from their expected values. Models predicting ED utilization using age, sex, race, morbidity, and prior use only (claims-based models) had lower R2 (ranging from 2.9% to 3.7%) and poorer predictive ability than the enhanced models that also included payor, PCP type and quality, problem list conditions, and covariates from the EMR, Census tract, and MCN provider data (enhanced model R2 ranged from 4.17% to 5.14%). In adjusted analyses, age, claims-based morbidity score, any ED visit in the base year, asthma, congestive heart failure, depression, tobacco use, and neighborhood poverty were strongly associated with increased risk for all 3 measures (all P\u3c.001)

Arquitectura de un sistema integrado para diseño dirigido por modelos en el contexto de internet de las cosas con aplicaciones en medicina

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 14-10-20222Over the past few years, we have seen how processing and storage architectures become cheaper and more efficient, communication infrastructures become faster and more scalable, and many new ways of interacting with the world around us are being developed. Every day more devices are connected to the network, and the generation of data worldwide is growing exponentially. In this context, the Internet of Things promises to be the new technological revolution, as was the introduction of the network of networks or universal mobile accessibility in tis day...A lo largo de los últimos años hemos visto cómo las arquitecturas de procesamiento y almacenamiento se vuelven más baratas y eficientes, las infraestructuras de comunicación se hacen más rápidas y escalables, y se desarrollan multitud de nuevas formas de interactuar con el mundo que nos rodea. Cada día más dispositivos se conectan a la red, y la generación de datos a nivel mundal está creciendo exponencialmente. En este contexto, el Internet de las cosas promete ser la nueva revolución tecnológica, como en su día lo fue la introducción de la red de redes o la accesibilidad móvil universal...Fac. de InformáticaTRUEunpu