scholarly journals Predictive Value of Verbatim Parkinson’s Disease Patient-Reported Symptoms of Postural Instability and Falling

2021 ◽  
pp. 1-8
Author(s):  
Monica Javidnia ◽  
Lakshmi Arbatti ◽  
Abhishek Hosamath ◽  
Shirley W. Eberly ◽  
David Oakes ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Natural Language Processing ◽  
Natural Language ◽  
Parkinson Disease ◽  
Language Processing ◽  
Rating Scale ◽  
Disease Patient ◽  
Postural Instability ◽  
Patient Reported

Background: Postural instability is an intractable sign of Parkinson’s disease, associated with poor disease prognosis, fall risk, and decreased quality of life. Objective: 1) Characterize verbatim reports of postural instability and associated symptoms (gait disorder, balance, falling, freezing, and posture), 2) compare reports with responses to three pre-specified questions from Part II of the Movement Disorder Society Unified Parkinson Disease Rating Scale (MDS-UPDRS), and 3) examine postural instability symptoms and MDS-UPDRS responses as predictors of future falls. Methods: Fox Insight research participants reported their problems attributed to PD in their own words using the Parkinson Disease Patient Reports of Problems (PD-PROP). Natural language processing, clinical curation, and data mining techniques were applied to classify text into problem domains and clinically-curated symptoms. Baseline postural instability symptoms were mapped to MDS-UPDRS questions 2.11–2.13. T-tests and chi-square tests were used to compare postural instability reporters and non-reporters, and Cochran-Armitage trend tests were used to evaluate associations between PD-PROP and MDS-UPDRS responses; survival methods were utilized to evaluate the predictive utility of PD-PROP and MDS-UPDRS responses in time-to-fall analyses. Results: Of participants within 10 years of PD diagnosis, 9,692 (56.0%) reported postural instability symptoms referable to gait unsteadiness, balance, falling, freezing, or posture at baseline. Postural instability symptoms were significantly associated with patient-reported measures from the MDS-UPDRS questions. Balance problems reported on PD-PROP and MDS-UPDRS 2.11–2.13 measures were predictive of future falls. Conclusion: Verbatim-reported problems captured by the PD-PROP and categorized by natural language processing and clinical curation and MDS-UPDRS responses predicted falls. The PD-PROP output was more granular than, and as informative as, the categorical responses.

Drug repurposing against Parkinson's disease by text mining the scientific literature

2020 ◽  
Vol 38 (4) ◽  
pp. 741-750 ◽  
Author(s):  
Yongjun Zhu ◽  
Woojin Jung ◽  
Fei Wang ◽  
Chao Che
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Scientific Literature ◽  
Drug Repurposing ◽  
Content Type

PurposeDrug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.Design/methodology/approachThe literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.FindingsThe proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.Originality/valueThe drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.

scholarly journals Glucocerebrosidase Activity is Reduced in Cryopreserved Parkinson’s Disease Patient Monocytes and Inversely Correlates with Motor Severity

2021 ◽  
pp. 1-9
Author(s):  
Laura P. Hughes ◽  
Marilia M.M. Pereira ◽  
Deborah A. Hammond ◽  
John B. Kwok ◽  
Glenda M. Halliday ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Rating Scale ◽  
Disease Patient ◽  
Motor Symptom ◽  
Blood Monocytes ◽  
Peripheral Blood Monocytes ◽  
Classical Monocytes

Background: Reduced activity of lysosomal glucocerebrosidase is found in brain tissue from Parkinson’s disease patients. Glucocerebrosidase is also highly expressed in peripheral blood monocytes where its activity is decreased in Parkinson’s disease patients, even in the absence of GBA mutation. Objective: To measure glucocerebrosidase activity in cryopreserved peripheral blood monocytes from 30 Parkinson’s disease patients and 30 matched controls and identify any clinical correlation with disease severity. Methods: Flow cytometry was used to measure lysosomal glucocerebrosidase activity in total, classical, intermediate, and non-classical monocytes. All participants underwent neurological examination and motor severity was assessed by the Movement Disorders Society Unified Parkinson’s Disease Rating Scale. Results: Glucocerebrosidase activity was significantly reduced in the total and classical monocyte populations from the Parkinson’s disease patients compared to controls. GCase activity in classical monocytes was inversely correlated to motor symptom severity. Conclusion: Significant differences in monocyte glucocerebrosidase activity can be detected in Parkinson’s disease patients using cryopreserved mononuclear cells and monocyte GCase activity correlated with motor features of disease. Being able to use cryopreserved cells will facilitate the larger multi-site trials needed to validate monocyte GCase activity as a Parkinson’s disease biomarker.

Association between White Matter Lesions and Non-Motor Symptoms in Parkinson Disease

2018 ◽  
Vol 18 (2-3) ◽  
pp. 127-132 ◽  
Author(s):  
Jeong-Yoon Lee ◽  
Ji Sun Kim ◽  
Wooyoung Jang ◽  
Jinse Park ◽  
Eungseok Oh ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Parkinson Disease ◽  
Cognitive Dysfunction ◽  
Rating Scale ◽  
White Matter Lesions ◽  
Motor Dysfunction ◽  
Motor Symptoms ◽  
Non Motor Symptoms

Background: There are only few studies exploring the relationship between white matter lesions (WMLs) and non-motor symptoms in Parkinson disease (PD). This study aimed to investigate the association between WMLs and the severity of non-motor symptoms in PD. Methods: The severity of motor dysfunction, cognitive impairment, and non-motor symptoms was assessed by various scales in 105 PD patients. We used a visual semiquantitative rating scale and divided the subjects into four groups: no, mild, moderate, and severe WMLs. We compared the means of all scores between the four groups and analyzed the association between the severity of WMLs and the specific domain of non-motor symptoms. Results: The non-motor symptoms as assessed by the Non-Motor Symptoms Scale, Parkinson’s Disease Questionnaire (PDQ-39), Parkinson’s Disease Sleep Scale, Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Neuropsychiatric Inventory (NPI), and Parkinson Fatigue Scale (PFS) were significantly worse in the patients with moderate and severe WMLs than in those without WMLs. Compared with the no WML group, the scores for motor dysfunction were significantly higher in the mild, moderate, and severe WML groups. The scores for cognitive dysfunction were significantly higher in the patients with severe WMLs than in those without WMLs. The severity of WMLs showed linear associations with PFS, BDI, BAI, NPI, and PDQ-39 scores. The severity of WMLs also correlated linearly with scores for motor and cognitive dysfunction. Conclusions: Among the non-motor symptoms, fatigue, depression, anxiety, and quality of life were significantly affected by WMLs in PD. Confirmation of the possible role of WMLs in non-motor symptoms associated with PD in a prospective manner may be crucial not only for understanding non-motor symptoms but also for the development of treatment strategies.

scholarly journals Predictors of second-sided deep brain stimulation for Parkinson’s disease

2020 ◽  
pp. 1-7
Author(s):  
Joshua L. Golubovsky ◽  
Hong Li ◽  
Arbaz Momin ◽  
Jianning Shao ◽  
Maxwell Y. Lee ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Rating Scale ◽  
Intraoperative Complications ◽  
Cohort Analysis ◽  
Initial Surgery ◽  
Patient Reported ◽  
Deep Brain

OBJECTIVEParkinson’s disease (PD) is a progressive neurological movement disorder that is commonly treated with deep brain stimulation (DBS) surgery in advanced stages. The purpose of this study was to investigate factors that affect time to placement of a second-sided DBS lead for PD when a unilateral lead is initially placed for asymmetrical presentation. The decision whether to initially perform unilateral or bilateral DBS is largely based on physician and/or patient preference.METHODSThis study was a retrospective cohort analysis of patients with PD undergoing initial unilateral DBS for asymmetrical disease between January 1999 and December 2017 at the authors’ institution. Patients treated with DBS for essential tremor or other conditions were excluded. Variables collected included demographics at surgery, time since diagnosis, Unified Parkinson’s Disease Rating Scale motor scores (UPDRS-III), patient-reported quality-of-life outcomes, side of operation, DBS target, intraoperative complications, and date of follow-up. Paired t-tests were used to assess mean changes in UPDRS-III. Cox proportional hazards analysis and the Kaplan-Meier method were used to determine factors associated with time to second lead insertion over 5 years.RESULTSThe final cohort included 105 patients who underwent initial unilateral DBS for asymmetrical PD; 59% of patients had a second-sided lead placed within 5 years with a median time of 34 months. Factors found to be significantly associated with early second-sided DBS included patient age 65 years or younger, globus pallidus internus (GPi) target, and greater off-medication reduction in UPDRS-III score following initial surgery. Older age was also found to be associated with a smaller preoperative UPDRS-III levodopa responsiveness score and with a smaller preoperative to postoperative medication-off UPDRS-III change.CONCLUSIONSYounger patients, those undergoing GPi-targeted unilateral DBS, and patients who responded better to the initial DBS were more likely to undergo early second-sided lead placement. Therefore, these patients, and patients who are more responsive to medication preoperatively (as a proxy for DBS responsiveness), may benefit from consideration of initial bilateral DBS.

scholarly journals Text: An R-package for Analyzing and Visualizing Human Language Using Natural Language Processing and Deep Learning

2021 ◽  
Author(s):  
Oscar Nils Erik Kjell ◽  
H. Andrew Schwartz ◽  
Salvatore Giorgi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rating Scale ◽  
State Of The Art ◽  
R Package ◽  
Language Models ◽  
Categorical Variables ◽  
Human Language

The language that individuals use for expressing themselves contains rich psychological information. Recent significant advances in Natural Language Processing (NLP) and Deep Learning (DL), namely transformers, have resulted in large performance gains in tasks related to understanding natural language such as machine translation. However, these state-of-the-art methods have not yet been made easily accessible for psychology researchers, nor designed to be optimal for human-level analyses. This tutorial introduces text (www.r-text.org), a new R-package for analyzing and visualizing human language using transformers, the latest techniques from NLP and DL. Text is both a modular solution for accessing state-of-the-art language models and an end-to-end solution catered for human-level analyses. Hence, text provides user-friendly functions tailored to test hypotheses in social sciences for both relatively small and large datasets. This tutorial describes useful methods for analyzing text, providing functions with reliable defaults that can be used off-the-shelf as well as providing a framework for the advanced users to build on for novel techniques and analysis pipelines. The reader learns about six methods: 1) textEmbed: to transform text to traditional or modern transformer-based word embeddings (i.e., numeric representations of words); 2) textTrain: to examine the relationships between text and numeric/categorical variables; 3) textSimilarity and 4) textSimilarityTest: to computing semantic similarity scores between texts and significance test the difference in meaning between two sets of texts; and 5) textProjection and 6) textProjectionPlot: to examine and visualize text within the embedding space according to latent or specified construct dimensions (e.g., low to high rating scale scores).

scholarly journals A Natural Language Processing Approach to Social License Management

2020 ◽  
Vol 12 (20) ◽  
pp. 8441
Author(s):  
Robert G. Boutilier ◽  
Kyle Bahr
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Social Acceptance ◽  
Rating Scale ◽  
Temporal Trends ◽  
Complex Projects ◽  
Social License ◽  
The Social ◽  
Project Life

Dealing with the social and political impacts of large complex projects requires monitoring and responding to concerns from an ever-evolving network of stakeholders. This paper describes the use of text analysis algorithms to identify stakeholders’ concerns across the project life cycle. The social license (SL) concept has been used to monitor the level of social acceptance of a project. That acceptance can be assessed from the texts produced by stakeholders on sources ranging from social media to personal interviews. The same texts also contain information on the substance of stakeholders’ concerns. Until recently, extracting that information necessitated manual coding by humans, which is a method that takes too long to be useful in time-sensitive projects. Using natural language processing algorithms, we designed a program that assesses the SL level and identifies stakeholders’ concerns in a few hours. To validate the program, we compared it to human coding of interview texts from a Bolivian mining project from 2009 to 2018. The program’s estimation of the annual average SL was significantly correlated with rating scale measures. The topics of concern identified by the program matched the most mentioned categories defined by human coders and identified the same temporal trends.

scholarly journals Validation of an Ambulatory Capacity Measure in Parkinson Disease: A Construct Derived from the Unified Parkinson's Disease Rating Scale

2015 ◽  
Vol 5 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Sotirios A. Parashos ◽  
Jordan Elm ◽  
James T. Boyd ◽  
Kelvin L. Chou ◽  
Lin Dai ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Parkinson Disease ◽  
Rating Scale ◽  
Capacity Measure ◽  
Disease Rating
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