Mood instability and clinical outcomes in mental health disorders: A natural language processing (NLP) study

2016 ◽  
Vol 33 (S1) ◽  
pp. s224-s224
Author(s):  
R. Patel ◽  
T. Lloyd ◽  
R. Jackson ◽  
M. Ball ◽  
H. Shetty ◽  
...  
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Mental Disorders ◽  
Natural Language ◽  
Clinical Outcomes ◽  
Language Processing ◽  
Mental Health Disorders ◽  
Mood Instability ◽  
Health Disorders ◽  
Clinical Records

IntroductionMood instability is an important problem but has received relatively little research attention. Natural language processing (NLP) is a novel method, which can used to automatically extract clinical data from electronic health records (EHRs).AimsTo extract mood instability data from EHRs and investigate its impact on people with mental health disorders.MethodsData on mood instability were extracted using NLP from 27,704 adults receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) for affective, personality or psychotic disorders. These data were used to investigate the association of mood instability with different mental disorders and with hospitalisation and treatment outcomes.ResultsMood instability was documented in 12.1% of people included in the study. It was most frequently documented in people with bipolar disorder (22.6%), but was also common in personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (B coefficient 18.5, 95% CI 12.1–24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75–2.17), and an increased likelihood of prescription of antipsychotics (2.03, 1.75–2.35).ConclusionsUsing NLP, it was possible to identify mood instability in a large number of people, which would otherwise not have been possible by manually reading clinical records. Mood instability occurs in a wide range of mental disorders. It is generally associated with poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also highlight the utility of NLP for clinical research.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes

BMJ Open ◽  
2015 ◽  
Vol 5 (5) ◽  
pp. e007504-e007504 ◽  
Author(s):  
R. Patel ◽  
T. Lloyd ◽  
R. Jackson ◽  
M. Ball ◽  
H. Shetty ◽  
...  
Keyword(s):  
Mental Health ◽  
Clinical Outcomes ◽  
Mental Health Disorders ◽  
Mood Instability ◽  
Health Disorders

scholarly journals Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing

2021 ◽  
Vol 3 ◽  
Author(s):  
Aurelie Mascio ◽  
Robert Stewart ◽  
Riley Botelle ◽  
Marcus Williams ◽  
Luwaiza Mirza ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Clinical Outcomes ◽  
Language Processing ◽  
Large Scale ◽  
Rating Scales ◽  
Cognitive Impairments ◽  
Free Text ◽  
Socio Demographic Factors ◽  
Clinical Records

Background: Cognitive impairments are a neglected aspect of schizophrenia despite being a major factor of poor functional outcome. They are usually measured using various rating scales, however, these necessitate trained practitioners and are rarely routinely applied in clinical settings. Recent advances in natural language processing techniques allow us to extract such information from unstructured portions of text at a large scale and in a cost effective manner. We aimed to identify cognitive problems in the clinical records of a large sample of patients with schizophrenia, and assess their association with clinical outcomes.Methods: We developed a natural language processing based application identifying cognitive dysfunctions from the free text of medical records, and assessed its performance against a rating scale widely used in the United Kingdom, the cognitive component of the Health of the Nation Outcome Scales (HoNOS). Furthermore, we analyzed cognitive trajectories over the course of patient treatment, and evaluated their relationship with various socio-demographic factors and clinical outcomes.Results: We found a high prevalence of cognitive impairments in patients with schizophrenia, and a strong correlation with several socio-demographic factors (gender, education, ethnicity, marital status, and employment) as well as adverse clinical outcomes. Results obtained from the free text were broadly in line with those obtained using the HoNOS subscale, and shed light on additional associations, notably related to attention and social impairments for patients with higher education.Conclusions: Our findings demonstrate that cognitive problems are common in patients with schizophrenia, can be reliably extracted from clinical records using natural language processing, and are associated with adverse clinical outcomes. Harvesting the free text from medical records provides a larger coverage in contrast to neurocognitive batteries or rating scales, and access to additional socio-demographic and clinical variables. Text mining tools can therefore facilitate large scale patient screening and early symptoms detection, and ultimately help inform clinical decisions.

Using Natural Language Processing to Assess the Psychological Effect of COVID-19 Pandemic on Insomnia via Tweets: A Pre-Post Retrospective Pilot Study (Preprint)

2021 ◽  
Author(s):  
Arash Maghsoudi ◽  
Sara Nowakowski ◽  
Ritwick Agrawal ◽  
Amir Sharafkhaneh ◽  
Sadaf Aram ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Temporal Analysis ◽  
Health Condition ◽  
Additional Stress ◽  
Mental Health Condition ◽  
Twitter Users

BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a higher incidence of insomnia. In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. OBJECTIVE In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. METHODS We designed a pre-post retrospective study using public social media content from Twitter. We categorized tweets based on time into two intervals: prepandemic (01/01/2019 to 01/01/2020) and pandemic (01/01/2020 to 01/01/2021). We used NLP to analyze polarity (positive/negative) and intensity of emotions and also users’ tweets psychological states in terms of sadness, anxiety and anger by counting the words related to these categories in each tweet. Additionally, we performed temporal analysis to examine the effect of time on the users’ insomnia experience. RESULTS We extracted 268,803 tweets containing the word insomnia (prepandemic, 123,293 and pandemic, 145,510). The odds of negative tweets (OR, 1.31; 95% CI, 1.29-1.33), anger (OR, 1.19; 95% CI, 1.16-1.21), and anxiety (OR, 1.24; 95% CI: 1.21-1.26) were higher during the pandemic compared to prepandemic. The likelihood of negative tweets after midnight was higher than for other daily intevals, comprising approximately 60% of all negative insomnia-related tweets in 2020 and 2021 collectively. CONCLUSIONS Twitter users shared more negative tweets about insomnia during the pandemic than during the year before. Also, more anger and anxiety-related content were disseminated during the pandemic on the social media platform. Future studies using an NLP framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

scholarly journals A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

2021 ◽  
Author(s):  
Vishal Dey ◽  
Peter Krasniak ◽  
Minh Nguyen ◽  
Clara Lee ◽  
Xia Ning
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Breast Implant ◽  
Public Attention ◽  
Social Media Data ◽  
Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

Anticancer, Anxiolytic, Nootropic Effects of Convolvulus Pluricaulis

2021 ◽  
pp. 1-1
Author(s):  
Rashmi Sharma ◽  
Keyword(s):  
Mental Health ◽  
Mental Disorders ◽  
Mental Health Disorders ◽  
Low Energy ◽  
Suicidal Thinking ◽  
Health Disorders ◽  
Convolvulus Pluricaulis

Mental health disorders effect thinking, behave, mood etc. these can be schizophrenia, eating beh. Addictive beh, Depression, anxiety, feeling sad, down, fear, worry, guilt, anger, violence & suicidal thinking. Convolvulus is traditionally used to treat mental disorders insomnia, fatigue, low Energy

A Proof of Concept for Assessing Emergency Room Use with Primary Care Data and Natural Language Processing

2013 ◽  
Vol 52 (01) ◽  
pp. 33-42 ◽  
Author(s):  
M.-H. Kuo ◽  
P. Gooch ◽  
J. St-Maurice
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Emergency Room ◽  
Language Processing ◽  
Free Text ◽  
Proof Of Concept ◽  
Term Extraction ◽  
Primary Care Data

SummaryObjective: The objective of this study was to undertake a proof of concept that demonstrated the use of primary care data and natural language processing and term extraction to assess emergency room use. The study extracted biopsychosocial concepts from primary care free text and related them to inappropriate emergency room use through the use of odds ratios.Methods: De-identified free text notes were extracted from a primary care clinic in Guelph, Ontario and analyzed with a software toolkit that incorporated General Architecture for Text Engineering (GATE) and MetaMap components for natural language processing and term extraction.Results: Over 10 million concepts were extracted from 13,836 patient records. Codes found in at least 1% percent of the sample were regressed against inappropriate emergency room use. 77 codes fell within the realm of biopsychosocial, were very statistically significant (p < 0.001) and had an OR > 2.0. Thematically, these codes involved mental health and pain related concepts.Conclusions: Analyzed thematically, mental health issues and pain are important themes; we have concluded that pain and mental health problems are primary drivers for inappropriate emergency room use. Age and sex were not significant. This proof of concept demonstrates the feasibly of combining natural language processing and primary care data to analyze a system use question. As a first work it supports further research and could be applied to investigate other, more complex problems.

Poster #S170 IDENTIFYING NEGATIVE SYMPTOMS IN SCHIZOPHRENIA AND ASSOCIATION WITH CLINICAL OUTCOMES USING NATURAL LANGUAGE PROCESSING

2014 ◽  
Vol 153 ◽  
pp. S151
Author(s):  
Rashmi Patel ◽  
Nishamali Jayatilleke ◽  
Richard Jackson ◽  
Hitesh Shetty ◽  
Robert Stewart ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Clinical Outcomes ◽  
Language Processing ◽  
Negative Symptoms

scholarly journals Management of mental health disorders and central nervous system sequelae in HIV-positive children and adolescents

2014 ◽  
Vol 15 (3) ◽  
pp. 81-96 ◽  
Author(s):  
R Nassen ◽  
K Donald ◽  
K Walker ◽  
S Paruk ◽  
M Vujovic ◽  
...  
Keyword(s):  
Mental Health ◽  
Central Nervous System ◽  
Nervous System ◽  
Mental Disorders ◽  
Children And Adolescents ◽  
Mental Health Disorders ◽  
Hiv Positive ◽  
Increased Risk ◽  
Health Disorders ◽  
The Impact

HIV-positive children and adolescents are at increased risk of both central nervous system (CNS) sequelae and mental disorders owing to a number of factors, including the impact of HIV infection on the brain, social determinants of health (e.g. poverty and orphanhood) and psychosocial stressors related to living with HIV. Every effort should be made to identify perinatally HIV-infected children and initiate them on antiretroviral therapy early in life. HIV clinicians should ideally screen for mental health and neurocognitive problems, as part of the routine monitoring of children attending antiretroviral clinics. This guideline is intended as a reference tool for HIV clinicians to support the early identification, screening and management of mental health disorders and/or CNS impairment in children and adolescents. This guideline covers mental disorders (section 1) and HIV-associated neurocognitive disorders (section 2) among children and adolescents.  

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