Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach.

BACKGROUND: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who suffer from mild forms of depression may recover without any assistance or be effectively managed by primary care or family practitioners. However, other forms of depression are far more severe and require advanced care by certified mental health providers. However, identifying cases of depression that require advanced care may be challenging to primary care providers and health care team members whose skill sets run broad rather than deep. OBJECTIVE: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana. METHODS: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups. RESULTS: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%. CONCLUSIONS: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.

Alignment of American Association of Colleges of Nursing Graduate-Level Nursing Informatics Competencies With American Medical Informatics Association Health Informatics Core Competencies.

This study yielded a map of the alignment of American Association of Colleges of Nursing Graduate-Level Nursing Informatics Competencies with American Medical Informatics Association Health Informatics Core Competencies in an effort to understand graduate-level accreditation and certification opportunities in nursing informatics. Nursing Informatics Program Directors from the American Medical Informatics Association and a health informatics expert independently mapped the American Association of Colleges of Nursing competencies to the American Medical Informatics Association Health Informatics knowledge, skills, and attitudes. The Nursing Informatics Program Directors' map connected an average of 4.0 American Medical Informatics Association Core Competencies per American Association of Colleges of Nursing competency, whereas the health informatics expert's map connected an average of 5.0 American Medical Informatics Association Core Competencies per American Association of Colleges of Nursing competency. Agreement across the two maps ranged from 14% to 60% per American Association of Colleges of Nursing competency, revealing alignment between the two groups' competencies according to knowledge, skills, and attitudes. These findings suggest that graduates of master's degree programs in nursing, especially those specializing in nursing informatics, will likely be prepared to sit for the proposed Advanced Health Informatics Certification in addition to the American Nurses Credentialing Center bachelor's-level Informatics Nursing Certification. This preliminary map sets the stage for further in-depth mapping of nursing informatics curricula with American Medical Informatics Association Core Competencies and will enable interprofessional conversations around nursing informatics specialty program accreditation, nursing workforce preparation, and nursing informatics advanced certification. Nursing informaticists should examine their need for credentials as key contributors who will address critical health informatics needs.

Dietary intake of isoflavones and coumestrol and the risk of prostate cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Experimental studies have revealed that phytoestrogens may modulate the risk of certain sites of cancer due to their structural similarity to 17ß-estradiol. The present study investigates whether intake of these compounds may influence prostate cancer risk in human populations. During a median follow up of 11.5 years, 2,598 cases of prostate cancer (including 287 advanced cases) have been identified among 27,004 men in the intervention arm of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Dietary intake of phytoestrogens (excluding lignans) was assessed with a food frequency questionnaire. Cox proportional hazards regression analysis was performed to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for dietary isoflavones and coumestrol in relation to prostate cancer risk. After adjustment for confounders, an increased risk of advanced prostate cancer [HR (95% CI) for quintile (Q) 5 vs. Q1] was found for the dietary intake of total isoflavones [1.91 (1.25-2.92)], genistein [1.51 (1.02-2.22), daidzein [1.80 (1.18-2.75) and glycitein [1.67 (1.15-2.43)] (p-trend for all associations ≤0.05). For example, HR (95% CI) for comparing the Q2, Q3, Q4 and Q5 with Q1 of daidzein intake was 1.45 (0.93-2.25), 1.65 (1.07-2.54), 1.73 (1.13-2.66) and 1.80 (1.18-2.75), respectively (p-trend: 0.013). No statistically significant associations were observed between the intake of total isoflavones and individual phytoestrogens and non-advanced and total prostate cancer after adjustment for confounders. This study revealed that dietary intake of isoflavones was associated with an elevated risk of advanced prostate cancer.

Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models.

Objective: Several opioid risk assessment tools are available to prescribers to evaluate opioid analgesic abuse among chronic patients. The objectives of this study are to 1) identify variables available in the literature to predict opioid abuse; 2) explore and compare methods (population, database, and analysis) used to develop statistical models that predict opioid abuse; and 3) understand how outcomes were defined in each statistical model predicting opioid abuse. Design: The OVID database was searched for this study. The search was limited to articles written in English and published from January 1990 to April 2016. This search generated 1,409 articles. Only seven studies and nine models met our inclusion-exclusion criteria. Results: We found nine models and identified 75 distinct variables. Three studies used administrative claims data, and four studies used electronic health record data. The majority, four out of seven articles (six out of nine models), were primarily dependent on the presence or absence of opioid abuse or dependence (ICD-9 diagnosis code) to define opioid abuse. However, two articles used a predefined list of opioid-related aberrant behaviors. Conclusions: We identified variables used to predict opioid abuse from electronic health records and administrative data. Medication variables are the recurrent variables in the articles reviewed (33 variables). Age and gender are the most consistent demographic variables in predicting opioid abuse. Overall, there is similarity in the sampling method and inclusion/exclusion criteria (age, number of prescriptions, follow-up period, and data analysis methods). Intuitive research to utilize unstructured data may increase opioid abuse models' accuracy.

Association between Urinary Phytoestrogens and C-reactive Protein in the Continuous National Health and Nutrition Examination Survey.

OBJECTIVE: A reduced risk of some cancers and cardiovascular disease associated with phytoestrogen intake may be mediated through its effect on serum C-reactive protein (CRP; an inflammation biomarker). Therefore, this study examined the associations between urinary phytoestrogens and serum CRP. METHODS: Urinary phytoestrogen and serum CRP data obtained from 6009 participants aged ≥ 40 years in the continuous National Health and Nutrition Examination Survey during 1999-2010 were analyzed. RESULTS: After adjustment for confounders, urinary concentrations of total and all individual phytoestrogens were inversely associated with serum concentrations of CRP (all p < 0.004). The largest reductions in serum CRP (mg/L) per interquartile range increase in urinary phytoestrogens (ng/mL) were observed for total phytoestrogens (ß = -0.18; 95% confidence interval [CI], -0.22, -0.15), total lignan (ß = -0.15; 95% CI, -0.18, -0.12), and enterolactone (ß = -0.15; 95% CI, -0.19, -0.12). A decreased risk of having high CRP concentrations (≥3.0 mg/L) for quartile 4 vs quartile 1 was also found for total phytoestrogens (OR = 0.63; 95% CI, 0.53, 0.73), total lignan (OR = 0.64; 95% CI, 0.54, 0.75), and enterolactone (OR = 0.59; 95% CI, 0.51, 0.69). CONCLUSION: Urinary total and individual phytoestrogens were significantly inversely associated with serum CRP in a nationally representative sample of the U.S.

Reconciling disparate information in continuity of care documents: Piloting a system to consolidate structured clinical documents.

BACKGROUND: Due to the nature of information generation in health care, clinical documents contain duplicate and sometimes conflicting information. Recent implementation of Health Information Exchange (HIE) mechanisms in which clinical summary documents are exchanged among disparate health care organizations can proliferate duplicate and conflicting information. MATERIALS AND METHODS: To reduce information overload, a system to automatically consolidate information across multiple clinical summary documents was developed for an HIE network. The system receives any number of Continuity of Care Documents (CCDs) and outputs a single, consolidated record. To test the system, a randomly sampled corpus of 522 CCDs representing 50 unique patients was extracted from a large HIE network. The automated methods were compared to manual consolidation of information for three key sections of the CCD: problems, allergies, and medications. RESULTS: Manual consolidation of 11,631 entries was completed in approximately 150h. The same data were automatically consolidated in 3.3min. The system successfully consolidated 99.1% of problems, 87.0% of allergies, and 91.7% of medications. Almost all of the inaccuracies were caused by issues involving the use of standardized terminologies within the documents to represent individual information entries. CONCLUSION: This study represents a novel, tested tool for de-duplication and consolidation of CDA documents, which is a major step toward improving information access and the interoperability among information systems. While more work is necessary, automated systems like the one evaluated in this study will be necessary to meet the informatics needs of providers and health systems in the future.

Urinary phytoestrogens and cancer, cardiovascular, and all-cause mortality in the continuous National Health and Nutrition Examination Survey.

PURPOSE: Experimental studies suggest that phytoestrogen intake alters cancer and cardiovascular risk. This study investigated the associations of urinary phytoestrogens with total cancer (n = 79), cardiovascular (n = 108), and all-cause (n = 290) mortality among 5179 participants in the continuous National Health and Nutrition Examination Survey (1999-2004). METHODS: Urinary phytoestrogens were measured using high-performance liquid chromatography with tandem mass spectrometric detection. Survival analysis was performed to evaluate hazard ratios (HRs) and 95 % confidence intervals (CIs) for each of the three outcomes in relation to urinary phytoestrogens. RESULTS: After adjustment for confounders, higher urinary concentrations of total enterolignans were associated with a reduced risk of death from cardiovascular disease (HR for tertile 3 vs. tertile 1 0.48; 95 % CI 0.24, 0.97), whereas higher urinary concentrations of total isoflavones (HR for tertile 3 vs. tertile 1 2.14; 95 % CI 1.03, 4.47) and daidzein (HR for tertile 3 vs. tertile 1 2.05; 95 % CI 1.02, 4.11) were associated with an increased risk. A reduction in all-cause mortality was observed for elevated urinary concentrations of total enterolignans (HR for tertile 3 vs. tertile 1 0.65; 95 % CI 0.43, 0.96) and enterolactone (HR for tertile 3 vs. tertile 1 0.65; 95 % CI 0.44, 0.97). CONCLUSIONS: Some urinary phytoestrogens were associated with cardiovascular and all-cause mortality in a representative sample of the US population. This is one of the first studies that used urinary phytoestrogens as biomarkers of their dietary intake to evaluate the effect of these bioactive compounds on the risk of death from cancer and cardiovascular disease.

Hypothesis generation using network structures on community health center cancer-screening performance.

RESEARCH OBJECTIVES: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments.

Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers.

Our conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman's Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability.

Theory of radiologist interaction with instant messaging decision support tools: A sequential-explanatory study.

Radiology specific clinical decision support systems (CDSS) and artificial intelligence are poorly integrated into the radiologist workflow. Current research and development efforts of radiology CDSS focus on 4 main interventions, based around exam centric time points-after image acquisition, intra-report support, post-report analysis, and radiology workflow adjacent. We review the literature surrounding CDSS tools in these time points, requirements for CDSS workflow augmentation, and technologies that support clinician to computer workflow augmentation. We develop a theory of radiologist-decision tool interaction using a sequential explanatory study design. The study consists of 2 phases, the first a quantitative survey and the second a qualitative interview study. The phase 1 survey identifies differences between average users and radiologist users in software interventions using the User Acceptance of Information Technology: Toward a Unified View (UTAUT) framework. Phase 2 semi-structured interviews provide narratives on why these differences are found. To build this theory, we propose a novel solution called Radibot-a conversational agent capable of engaging clinicians with CDSS as an assistant using existing instant messaging systems supporting hospital communications. This work contributes an understanding of how radiologist-users differ from the average user and can be utilized by software developers to increase satisfaction of CDSS tools within radiology.

Using a mobile application to self-monitor diet and fluid intake among adults receiving hemodialysis.

Hemodialysis patients have difficulty self-managing a complex dietary and fluid regimen. The purpose of this feasibility study was to pilot test an electronic self-monitoring intervention based on social cognitive theory. During a 6-week intervention, 24 participants self-monitored diet and fluid intake using the Dietary Intake Monitoring Application (DIMA), and 20 participants served as controls by monitoring their activity using the Daily Activity Monitor Application (DAMA). Results from this pilot study suggest the intervention is feasible and acceptable, although few significant effects on outcomes were found in this small sample. The DIMA has potential to facilitate dietary and fluid self-monitoring but requires additional refinement and further testing.

An answer recommendation framework for an online cancer community forum.

Health community forums are a kind of online platform to discuss various matters related to management of illness. People are increasingly searching for answers online, particularly when they are diagnosed with cancer like life-threatening diseases. People seek suggestions or advice through these platforms to make decisions during their treatments. However, locating the correct information or similar people is often a great challenge for them. In this scenario, this paper proposes an answer recommendation system in an online breast cancer community forum that provide guidance and valuable references to users while making decisions. The answer is the summary of already discussed topic in the forum, so that they do not need to go through all the answer posts which spans over multiple pages or initiate a thread once again. There are three phases for the answer recommendation system, including query similarity model to retrieve the past similar query, query-answer pair generation and answer recommendation. Query similarity model is employed by a Siamese network with Bi-LSTM architecture which could achieve an F1-score of 85.5%. Also, the paper shows the efficacy of transfer learning technique to generalize the model well in our breast cancer query-query pair data set. The query-answer pairs are generated by an extractive summarization technique that is based on an optimization algorithm. The effectiveness of the generated summary is evaluated based on a manually generated summary, and the result shows a ROUGE-1 score of 49%.

Generalizability of Human Activity Recognition Machine Learning Models from non-Parkinson's to Parkinson's Disease Patients.

Recent evidence shows that high-intensity exercises reduce tremors and stiffness in Parkinson's disease (PD). However, there is insufficient evidence on the types of exercises; in effect, high-intensity may be a personalized measure. Recent progress in automated Human Activity Recognition using machine learning (ML) models shows potential for better monitoring of PD patients. However, ML models must be calibrated to ignore tremors and accurately identify activity and its intensity. We report findings from a study where we trained ML models using data from medically validated triple synchronous sensors connected to 8 non-PD subjects performing 32 exercises. We then tested the models to identify exercises performed by 8 PD patients at different stages of the disease. Our analysis shows that better data preprocessing before modeling can provide some model generalizability. However, it is extremely challenging, as the models work with high accuracy on one group (Healthy or PD patients) (F1=0.88-0.94) but not on both groups.Clinical relevance-Patients with Parkinson's and other motor-generative diseases can now accurately measure physical activity with machine learning approaches. Clinicians, caregivers, and apps can make accurate, personalized exercise recommendations to augment medications that reduce tremors and stiffness.

The Kidney Transplant Self-Management Scale: Instrument Development and Psychometric Testing.

This study reports the development and psychometric testing of the Kidney Transplant Self-Management Scale (KT-SMS). The instrument development phase included the following: (a) conceptual definition, item generation, and framework; (b) face validity assessment; and (c) content validity assessment. The psychometric testing phase included the following: (a) construct validity testing; (b) internal consistency reliability testing; (c) convergent validity testing; and (d) predictive power of the KT-SMS using a cross-sectional sample of kidney transplant recipients (N = 153). Factor analysis results supported the 16-item KT-SMS as multidimensional with five domains (medication adherence, cardiovascular risk reduction, protecting kidney, ownership, and skin cancer prevention). Internal consistency reliability for the total scale and five subscales was adequate. Convergent validity was supported as the intercorrelations of the KT-SMS total score with the five subscales were significant. The KT-SMS total score and five subscales were significantly correlated with self-efficacy for managing chronic disease, patient activation, and health-related quality of life.

Mapping the delineation of practice to the AMIA foundational domains for applied health informatics.

OBJECTIVE: This article reports on the alignment between the foundational domains and the delineation of practice (DoP) for health informatics, both developed by the American Medical Informatics Association (AMIA). Whereas the foundational domains guide graduate-level curriculum development and accreditation assessment, providing an educational pathway to the minimum competencies needed as a health informatician, the DoP defines the domains, tasks, knowledge, and skills that a professional needs to competently perform in the discipline of health informatics. The purpose of this article is to determine whether the foundational domains need modification to better reflect applied practice. MATERIALS AND METHODS: Using an iterative process and through individual and collective approaches, the foundational domains and the DoP statements were analyzed for alignment and eventual harmonization. Tables and Sankey plot diagrams were used to detail and illustrate the resulting alignment. RESULTS: We were able to map all the individual DoP knowledge statements and tasks to the AMIA foundational domains, but the statements within a single DoP domain did not all map to the same foundational domain. Even though the AMIA foundational domains and DoP domains are not in perfect alignment, the DoP provides good examples of specific health informatics competencies for most of the foundational domains. There are, however, limited DoP knowledge statements and tasks mapping to foundational domain 6-Social and Behavioral Aspects of Health. DISCUSSION: Both the foundational domains and the DoP were developed independently, several years apart, and for different purposes. The mapping analyses reveal similarities and differences between the practice experience and the curricular needs of health informaticians. CONCLUSIONS: The overall alignment of both domains may be explained by the fact that both describe the current and/or future health informatics professional. One can think of the foundational domains as representing the broad foci for educational programs for health informaticians and, hence, they are appropriately the focus of organizations that accredit these programs.

Using podcasts to help students apply health informatics concepts: benefits and unintended consequences.

Despite requirements for robust health informatics education, a multitude of educators and policy analysts report that programs are not adequately preparing nurses to handle the bevy of technologies that will be a part of their practice. A series of 14 "Podcasted" exemplars were developed to help graduate online students visualize the application of health informatics concepts in real-world settings and to determine the impact of podcasting on student cognition, engagement, and satisfaction. Although no significant differences in student cognition scores or student engagement were found between course conditions, course satisfaction was significantly higher in Podcasted weeks of the course. Also, student engagement was positively correlated with aspects of course satisfaction and overall cognition scores under both course conditions. This result suggests that student engagement plays an important mediating role in improving cognition. Students' use of podcasting did produce a temporary drop in scores for one group; therefore, more research is needed to understand these unintended consequences. With distance/online education becoming mainstream, it is imperative that faculty deploy and confirm ways to improve student cognition, engagement, and satisfaction.

PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature.

BACKGROUND: Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. RESULTS: Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the "Biomedical & Procedure" dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for "The use of NLP". The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. CONCLUSIONS: The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications.

Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience.

Online health communities (OHC) provide various opportunities for patients with chronic or life-threatening illnesses, especially for cancer patients and survivors. A better understanding of the sentiment dynamics of patients in OHCs can help in the precise formulation of the needs during their treatment. The current study investigated the sentiment dynamics in patients' narratives in a Breast Cancer community group (Breastcancer.org) to identify the changes in emotions, thoughts, stress, and coping mechanisms while undergoing treatment options, particularly chemotherapy, radiation, and surgery. Sentiment dynamics of users' posts was performed using a deep learning model. A sentiment change analysis was performed to measure change in the satisfaction level of the users. The deep learning model BiLSTM with sentiment embedding features provided a better F1-score of 91.9%. Sentiment dynamics can assess the difference in satisfaction level the users acquire by interacting with other users in the forum. A comparison of the proposed model with existing models revealed the effectiveness of this methodology.

Comparison of Ease of Use and Comfort in Fitness Trackers for Participants Impaired by Parkinson's Disease: An exploratory study.

Parkinson's disease (PD) is an incurable, fatal neurodegenerative disease, and only available treatment is to minimize symptoms. Anecdotal evidence suggests whole body workout can help to reduce PD severity; however, it is challenging to quantify its effect on PD. The increased availability of fitness trackers can help in quantifying the effect of whole-body workout on PD. Before using any over the counter fitness tracker, we must study the ease of use of the fitness trackers in PD patients. We interviewed 32 PD patients with six over the counter fitness trackers and determined their perceptions and attitude towards the fitness trackers. Although none of the fitness trackers received perfect scores for ease of use or comfort due to the presence of tremors, two trackers performed significantly better than the others. Further study is warranted to understand the potential for fitness trackers to be used by PD patients.