Does Bad News Spread Faster?

Bad news travels fast. Although this concept may be intuitively accepted, there has been little evidence to confirm that the propagation of bad news differs from that of good news. In this paper, we examine the effect of user perspective on his or her sharing of a controversial news story. Social media not only offers insight into human behavior but has also developed as a source of news. In this paper, we define the spreading of news by tracking selected tweets in Twitter as they are shared over time to create models of user sharing behavior. Many news events can be viewed as positive or negative. In this paper, we compare and contrast tweets about these news events among general users, while monitoring the tweet frequency for each event over time to ensure that news events are comparable with respect to user interest. In addition, we track the tweets of a controversial event between two different groups of users (i.e., those who view the event as positive and those who view it as negative). As a result, we are able to make assessments based on a single event from two different perspectives

A Novel Conceptual Architecture for Person-Centered Health Records

Personal health records available to patients today suffer from multiple limitations, such as information fragmentation, a one-size-fits-all approach and a focus on data gathered over time and by institution rather than health conditions. This makes it difficult for patients to effectively manage their health, for these data to be enriched with relevant information from external sources and for clinicians to support them in that endeavor. We propose a novel conceptual architecture for person-centered health record information systems that transcends many of these limitations and capitalizes on the emerging trend of socially-driven information systems. Our proposed personal health record system is personalized on demand to the conditions of each individual patient; organized to facilitate the tracking and review of the patient's conditions; and able to support patient-community interactions, thereby promoting community engagement in scientific studies, facilitating preventive medicine, and accelerating the translation of research findings

Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia

Background: Medication nonadherence can compound into severe medical problems for patients. Identifying patients who are likely to become nonadherent may help reduce these problems. Data-driven machine learning models can predict medication adherence by using selected indicators from patients’ past health records. Sources of data for these models traditionally fall under two main categories: (1) proprietary data from insurance claims, pharmacy prescriptions, or electronic medical records and (2) survey data collected from representative groups of patients. Models developed using these data sources often are limited because they are proprietary, subject to high cost, have limited scalability, or lack timely accessibility. These limitations suggest that social health forums might be an alternate source of data for adherence prediction. Indeed, these data are accessible, affordable, timely, and available at scale. However, they can be inaccurate. Objective: This paper proposes a medication adherence machine learning model for fibromyalgia therapies that can mitigate the inaccuracy of social health forum data. Methods: Transfer learning is a machine learning technique that allows knowledge acquired from one dataset to be transferred to another dataset. In this study, predictive adherence models for the target disease were first developed by using accurate but limited survey data. These models were then used to predict medication adherence from health social forum data. Random forest, an ensemble machine learning technique, was used to develop the predictive models. This transfer learning methodology is demonstrated in this study by examining data from the Medical Expenditure Panel Survey and the PatientsLikeMe social health forum. Results: When the models are carefully designed, less than a 5% difference in accuracy is observed between the Medical Expenditure Panel Survey and the PatientsLikeMe medication adherence predictions for fibromyalgia treatments. This design must take into consideration the mapping between the predictors and the outcomes in the two datasets. Conclusions: This study exemplifies the potential and limitations of transfer learning in medication adherence–predictive models based on survey data and social health forum data. The proposed approach can make timely medication adherence monitoring cost-effective and widely accessible. Additional investigation is needed to improve the robustness of the approach and extend its applicability to other therapies and other sources of data. [JMIR Med Inform 2019;7(2):e12561

Making the Case for a P2P Personal Health Record

Improved health care services can benefit from a more seamless exchange of medical information between patients and health care providers. This exchange is especially important considering the increasing trends in mobility, comorbidity and outbreaks. However, current Electronic Health Records (EHR) tend to be institution-centric, often leaving the medical information of the patient fragmented and more importantly inaccessible to the patient for sharing with other health providers in a timely manner. Nearly a decade ago, several client–server models for personal health records (PHR) were proposed. The aim of these previous PHRs was to address data fragmentation issues. However, these models were not widely adopted by patients. This paper discusses the need for a new PHR model that can enhance the patient experience by making medical services more accessible. The aims of the proposed model are to (1) help patients maintain a complete lifelong health record, (2) facilitate timely communication and data sharing with health care providers from multiple institutions and (3) promote integration with advanced third-party services (e.g., risk prediction for chronic diseases) that require access to the patient’s health data. The proposed model is based on a Peer-to-Peer (P2P) network as opposed to the client–server architecture of the previous PHR models. This architecture consists of a central index server that manages the network and acts as a mediator, a peer client for patients and providers that allows them to manage health records and connect to the network, and a service client that enables third-party providers to offer services to the patients. This distributed architecture is essential since it promotes ownership of the health record by the patient instead of the health care institution. Moreover, it allows the patient to subscribe to an extended range of personalized e-health services

A Water Demand Prediction Model for Central Indiana

Due to the limited natural water resources and the increase in population, managing water consumption is becoming an increasingly important subject worldwide. In this paper, we present and compare different machine learning models that are able to predict water demand for Central Indiana. The models are developed for two different time scales: daily and monthly. The input features for the proposed model include weather conditions (temperature, rainfall, snow), social features (holiday, median income), date (day of the year, month), and operational features (number of customers, previous water demand levels). The importance of these input features as accurate predictors is investigated. The results show that daily and monthly models based on recurrent neural networks produced the best results with an average error in prediction of 1.69% and 2.29%, respectively for 2016. These models achieve a high accuracy with a limited set of input features

Predicting Childhood Obesity Using Machine Learning: Practical Considerations

Previous studies demonstrate the feasibility of predicting obesity using various machine learning techniques; however, these studies do not address the limitations of these methods in real-life settings where available data for children may vary. We investigated the medical history required for machine learning models to accurately predict body mass index (BMI) during early childhood. Within a longitudinal dataset of children ages 0–4 years, we developed predictive models based on long short-term memory (LSTM), a recurrent neural network architecture, using history EHR data from 2 to 8 clinical encounters to estimate child BMI. We developed separate, sex-stratified models using 80% of the data for training and 20% for external validation. We evaluated model performance using K-fold cross-validation, mean average error (MAE), and Pearson’s correlation coefficient (R2). Two history encounters and a 4-month prediction yielded a high prediction error and low correlation between predicted and actual BMI (MAE of 1.60 for girls and 1.49 for boys). Model performance improved with additional history encounters; improvement was not significant beyond five history encounters. The combined model outperformed the sex-stratified models, with a MAE = 0.98 (SD 0.03) and R2 = 0.72. Our models show that five history encounters are sufficient to predict BMI prior to age 4 for both boys and girls. Moreover, starting from an initial dataset with more than 269 exposure variables, we were able to identify a limited set of 24 variables that can facilitate BMI prediction in early childhood. Nine of these final variables are collected once, and the remaining 15 need to be updated during each visit

Inferring the patient’s age from implicit age clues in health forum posts

Broader patient-reported experiences in oncology are largely unknown due to the lack of available information from traditional data sources. Online health community data provide an exploratory way to uncover these experiences at a large scale. Analyzing these data can guide further studies towards understanding patients’ needs and experiences. However, analysis of online health data is inherently difficult due to the unstructured nature of these data and the variety of ways information can be expressed over text. Specifically, subscribers may not disclose critical information such as the age of the patient in their posts. In fact, the number of health forum posts that explicitly mention the age of the patient is significantly lower than the number of posts that do not include this information in the Reddit r/Cancer health forum under consideration in the present paper. Health-focused studies often need to consider or control for age as a confounder, hence the importance of having sufficient age data. This paper presents a methodology that can help classify health forum posts according to four age groups (0–17, 18–39, 40–64 and 65 + years) even when the posts do not contain explicit mention of the age of the patient. First, the subset of the posts that include explicit mention of the age of the patient is identified. Second, the explicit age clues are removed from these posts and used to train the proposed age classifier. The resulting classifier is able to infer the age of the patient using only implicit age clues with an average true positive rate (TPR) of 71%. This TPR is comparable to the average TPR of 69% obtained from human annotations for the same set of posts

A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles

This paper advocates a data summarization approach based on distance rather than the traditional time period when developing individualized machine learning models for fuel consumption. This approach is used in conjunction with seven predictors derived from vehicle speed and road grade to produce a highly predictive neural network model for average fuel consumption in heavy vehicles. The proposed model can easily be developed and deployed for each individual vehicle in a fleet in order to optimize fuel consumption over the entire fleet. The predictors of the model are aggregated over fixed window sizes of distance traveled. Different window sizes are evaluated and the results show that a 1 km window is able to predict fuel consumption with a 0.91 coefficient of determination and mean absolute peak-to-peak percent error less than 4% for routes that include both city and highway duty cycle segments.This research was supported in part by Allison Transmission, Inc

Predicting Dementia With Routine Care EMR Data

Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions. The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis