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09) to have the highest NDF digestibility. Alfalfa biotype affected in vivo apparent total tract digestibility of NDF (P less then 0.001) and there was a trend for an effect on acid detergent fiber digestibility (P = 0.051). Hi-Gest and HarvXtra had similar in vivo apparent NDF digestibilities, which were greater than for LegenDairy (P less then 0.05). There was no alfalfa biotype effect on daily alfalfa dry matter intake (DMI; P = 0.51) or average daily gain (P = 0.25) by growing steers. The absence of an effect by the novel alfalfa biotypes on DMI by growing steers suggests that the compositional and digestibility differences of the novel alfalfa biotypes compared to LegenDairy were not sufficient to alleviate the limitation of physical fill (if evident) on DMI. If more disparity in cell wall composition and NDF digestibility were to exist between control and reduced-lignin biotypes, then perhaps an advantage in cattle growth performance for a reduced-lignin alfalfa biotype would be detectable.Sedentary behaviors are now prevalent as most modern jobs are done while seated. However, such sedentary behaviors have been found to increase the risk of several ailments including diabetes, cardiovascular disease, and all-cause mortality. Current interventions are mostly reactive and are triggered after the user has already been sedentary. Behavior change theory suggests that preventive sedentary interventions, which are triggered before a person becomes sedentary, are more likely to succeed. In this paper, we characterize user patterns of sedentary behaviors by analyzing smartphone-sensor data in a real-world dataset. Our work reveals location types (where), times of day/week (when), and smartphone contexts in which sedentary behaviors are most likely. Leveraging our findings, we then propose a set of context-aware probabilistic models that can predict sedentary behaviors in advance by analyzing smartphone sensor data. Our Context-Aware Predictive (CAP) models leverage smartphone-sensed contextual variables and the user's history of sedentary behaviors to predict their future sedentary behaviors. We rigorously analyze the performance of our models and discuss the implications of our work.People of rural India often suffer from acute health conditions like diarrhea, flu, lung congestion, and anemia, but they are not receiving treatment even at primary level due to scarcity of doctors and health infrastructure in remote villages. Health workers are involved in diagnosing the patients based on the symptoms and physiological signs. However, due to inadequate domain knowledge, lack of expertise, and error in measuring the health data, uncertainty creeps in the decision space, resulting many false cases in predicting the diseases. The paper proposes an uncertainty management technique using fuzzy and rough set theory to diagnose the patients with minimum false-positive and false-negative cases. We use fuzzy variables with proper semantic to represent the vagueness of input data, appearing due to measurement error. We derive initial degree of belonging of each patient in two different disease class labels (YES/NO) using the fuzzified input data. Next, we apply rough set theory to manage uncertainty in diagnosing the diseases by learning approximations of the decision boundary between the two class labels. The optimum lower and upper approximation membership functions for each disease class label have been obtained using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, using the proposed disease_similarity_factor, new patients are diagnosed precisely with 98% accuracy and minimum false cases.Cortisol is a glucocorticoid hormone that is critical to immune system functioning. Studies show that prolonged exposure to high levels of cortisol can lead to a range of physical health ailments including the progression of tumor growth. The ability to monitor cortisol levels over time can therefore be used to facilitate decision-making during cancer treatment. learn more However, collecting serum or saliva samples to monitor cortisol in situ is inconvenient, costly, and impractical. In this paper, we propose a general predictive modeling process that uses passively sensed actigraphy data to predict underlying salivary cortisol levels using graph representation learning. We compare machine learning models with handcrafted feature engineering and with graph representation learning, which includes Graph2Vec, FeatherGraph, GeoScattering and NetLSD. Our preliminary results generated from data from 10 newly diagnosed pancreatic cancer patients demonstrate that machine learning models with graph representation learning can outperform the handcrafted feature engineering to predict salivary cortisol levels.Patients can use social media to describe their healthcare experiences. Several social media platforms, such as the Care Opinion platform, host large volumes of patient stories. However, the large number of these stories and the healthcare system's workload make exploring these stories a difficult task for healthcare providers and administrators. This study uses text mining for analyzing patient stories on the Care Opinion platform and exploring healthcare experiences described in these stories. We collected 367,573 stories, which were posted between September 2005 and September 2019. Topic modeling (Latent Dirichlet Allocation) and sentiment analysis were used to analyze the stories. Sixteen topics were identified representing five aspects of the healthcare experience communication between patients and providers, quality of clinical services, quality of non-clinical services, human aspects of healthcare experiences, and patient satisfaction. There was also a clear sentiment in 99% of the stories. More than 55% of the stories that describe the patient's request for information, the patient's description of treatment, or the patient's making of an appointment had a negative sentiment, which represents patient dissatisfaction. The study provides insights into the content of patient stories and demonstrates how topic modeling and sentiment analysis can be used to analyze large volumes of patient stories and provide insights into these stories. The findings suggest that these stories are not general social media posts; instead, they describe elements of healthcare experiences that can be helpful for quality improvement.

The online version contains supplementary material available at 10.1007/s41666-021-00097-5.

The online version contains supplementary material available at 10.1007/s41666-021-00097-5.Miscarriages are the most common type of pregnancy loss, mostly occurring in the first 12 weeks of pregnancy. Pregnancy risk assessment aims to quantify evidence to reduce such maternal morbidities, and personalized decision support systems are the cornerstone of high-quality, patient-centered care to improve diagnosis, treatment selection, and risk assessment. However, data sparsity and the increasing number of patient-level observations require more effective forms of representing clinical knowledge to encode known information that enables performing inference and reasoning. Whereas knowledge embedding representation has been widely explored in the open domain data, there are few efforts for its application in the clinical domain. In this study, we contrast differences among multiple embedding strategies, and we demonstrate how these methods can assist in performing risk assessment of miscarriage before and during pregnancy. Our experiments show that simple knowledge embedding approaches that utilize domain-specific metadata perform better than complex embedding strategies, although both can improve results comparatively to a population probabilistic baseline in both AUPRC, F1-score, and a proposed normalized version of these evaluation metrics that better reflects accuracy for unbalanced datasets. Finally, embedding approaches provide evidence about each individual, supporting explainability for its model predictions in such a way that humans understand.As more data is generated from medical attendances and as Artificial Neural Networks gain momentum in research and industry, computer-aided medical prognosis has become a promising technology. A common approach to perform automated prognoses relies on textual clinical notes extracted from Electronic Health Records (EHRs). Data from EHRs are fed to neural networks that produce a set with the most probable medical problems to which a patient is subject in her/his clinical future, including clinical conditions, mortality, and readmission. Following this research line, we introduce a methodology that takes advantage of the unstructured text found in clinical notes by applying preprocessing, concepts extraction, and fine-tuned neural networks to predict the most probable medical problems to follow in a patient's clinical trajectory. Different from former works that focus on word embeddings and raw sets of extracted concepts, we generate a refined set of Unified Medical Language System (UMLS) concepts by applying a similarity threshold filter and a list of acceptable concept types. In our prediction experiments, our method demonstrated AUC-ROC performance of 0.91 for diagnosis codes, 0.93 for mortality, and 0.72 for readmission, determining an efficacy that rivals state-of-the-art works. Our findings contribute to the development of automated prognosis systems in hospitals where text is the main source of clinical history.People living with dementia (PLwD) often exhibit behavioral and psychological symptoms, such as episodes of agitation and aggression. Agitated behavior in PLwD causes distress and increases the risk of injury to both patients and caregivers. In this paper, we present the use of a multi-modal wearable device that captures motion and physiological indicators to detect agitation in PLwD. We identify features extracted from sensor signals that are the most relevant for agitation detection. We hypothesize that combining multi-modal sensor data will be more effective to identify agitation in PLwD in comparison to a single sensor. The results of this unique pilot study are based on 17 participants' data collected during 600 days from PLwD admitted to a Specialized Dementia Unit. Our findings show the importance of using multi-modal sensor data and highlight the most significant features for agitation detection.There have been many efforts in the last decade in the health informatics community to develop systems that can automatically recognize and predict disclosures on social media. However, a majority of such efforts have focused on simple topic prediction or sentiment classification. However, taboo disclosures on social media that people are not comfortable to talk with their friends represent an abstract theme dependent on context and background. Recent research has demonstrated the efficacy of injecting concept into the learning model to improve prediction. We present a vectorization scheme that combines corpus- and lexicon-based approaches for predicting taboo topics from anonymous social media datasets. The proposed vectorization scheme exploits two context-rich lexicons LIWC and Urban Dictionary. Our methodology achieves cross-validation accuracies of up to 78.1% for the supervised learning task on Facebook Confessions dataset, and 70.5% for the transfer learning task on the YikYak dataset. For both the tasks, supervised algorithms trained with features generated by the proposed vectorizer perform better than vanilla t f - i d f representation.