scholarly journals Reviewing a Decade of Research Into Suicide and Related Behaviour Using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) System

2020 ◽  
Vol 11 ◽  
Author(s):  
André Bittar ◽  
Sumithra Velupillai ◽  
Johnny Downs ◽  
Rosemary Sedgwick ◽  
Rina Dutta
Keyword(s):  
Machine Learning ◽  
Clinical Research ◽  
Suicide Risk ◽  
Structured Data ◽  
Public Health Issue ◽  
The South ◽  
Interactive Search ◽  
Clinical Notes ◽  
Self Harm ◽  
Clinical Records

Suicide is a serious public health issue worldwide, yet current clinical methods for assessing a person's risk of taking their own life remain unreliable and new methods for assessing suicide risk are being explored. The widespread adoption of electronic health records (EHRs) has opened up new possibilities for epidemiological studies of suicide and related behaviour amongst those receiving healthcare. These types of records capture valuable information entered by healthcare practitioners at the point of care. However, much recent work has relied heavily on the structured data of EHRs, whilst much of the important information about a patient's care pathway is recorded in the unstructured text of clinical notes. Accessing and structuring text data for use in clinical research, and particularly for suicide and self-harm research, is a significant challenge that is increasingly being addressed using methods from the fields of natural language processing (NLP) and machine learning (ML). In this review, we provide an overview of the range of suicide-related studies that have been carried out using the Clinical Records Interactive Search (CRIS): a database for epidemiological and clinical research that contains de-identified EHRs from the South London and Maudsley NHS Foundation Trust. We highlight the variety of clinical research questions, cohorts and techniques that have been explored for suicide and related behaviour research using CRIS, including the development of NLP and ML approaches. We demonstrate how EHR data provides comprehensive material to study prevalence of suicide and self-harm in clinical populations. Structured data alone is insufficient and NLP methods are needed to more accurately identify relevant information from EHR data. We also show how the text in clinical notes provide signals for ML approaches to suicide risk assessment. We envision increased progress in the decades to come, particularly in externally validating findings across multiple sites and countries, both in terms of clinical evidence and in terms of NLP and machine learning method transferability.

scholarly journals How accurate are suicide risk prediction models? Asking the right questions for clinical practice

2019 ◽  
Vol 22 (3) ◽  
pp. 125-128 ◽  
Author(s):  
Daniel Whiting ◽  
Seena Fazel
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Suicide Risk ◽  
Potential Role ◽  
Prediction Models ◽  
Learning Approaches ◽  
Risk Prediction Models ◽  
Self Harm ◽  
The Right

Prediction models assist in stratifying and quantifying an individual’s risk of developing a particular adverse outcome, and are widely used in cardiovascular and cancer medicine. Whether these approaches are accurate in predicting self-harm and suicide has been questioned. We searched for systematic reviews in the suicide risk assessment field, and identified three recent reviews that have examined current tools and models derived using machine learning approaches. In this clinical review, we present a critical appraisal of these reviews, and highlight three major limitations that are shared between them. First, structured tools are not compared with unstructured assessments routine in clinical practice. Second, they do not sufficiently consider a range of performance measures, including negative predictive value and calibration. Third, the potential role of these models as clinical adjuncts is not taken into consideration. We conclude by presenting the view that the current role of prediction models for self-harm and suicide is currently not known, and discuss some methodological issues and implications of some machine learning and other analytic techniques for clinical utility.

Toward Automatic Risk Assessment to Support Suicide Prevention

Crisis ◽  
2019 ◽  
Vol 40 (4) ◽  
pp. 249-256 ◽  
Author(s):  
Marios Adamou ◽  
Grigoris Antoniou ◽  
Elissavet Greasidou ◽  
Vincenzo Lagani ◽  
Paulos Charonyktakis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Service ◽  
Suicide Risk ◽  
Causes Of Death ◽  
State Of The Art ◽  
Public Health Issue ◽  
Limiting Factor ◽  
Free Text ◽  
Medical Specialists ◽  
The Past

Abstract. Background: Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide risk has proven extremely difficult to assess for medical specialists, and traditional methodologies deployed have been ineffective. Advances in machine learning make it possible to attempt to predict suicide with the analysis of relevant data aiming to inform clinical practice. Aims: We aimed to (a) test our artificial intelligence based, referral-centric methodology in the context of the National Health Service (NHS), (b) determine whether statistically relevant results can be derived from data related to previous suicides, and (c) develop ideas for various exploitation strategies. Method: The analysis used data of patients who died by suicide in the period 2013–2016 including both structured data and free-text medical notes, necessitating the deployment of state-of-the-art machine learning and text mining methods. Limitations: Sample size is a limiting factor for this study, along with the absence of non-suicide cases. Specific analytical solutions were adopted for addressing both issues. Results and Conclusion: The results of this pilot study indicate that machine learning shows promise for predicting within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

scholarly journals Machine learning for suicide risk prediction in children and adolescents with electronic health records

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chang Su ◽  
Robert Aseltine ◽  
Riddhi Doshi ◽  
Kun Chen ◽  
Steven C. Rogers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Children And Adolescents ◽  
Suicidal Behavior ◽  
Predictive Models ◽  
Suicide Risk ◽  
Health Records ◽  
Short And Long Term ◽  
Clinical Records

AbstractAccurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children’s Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10–18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53–62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents.

scholarly journals Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption

2020 ◽  
Vol 36 (6) ◽  
pp. 443-449
Author(s):  
Julian Varghese
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Clinical Research ◽  
Structured Data ◽  
Regulatory Aspects ◽  
Clinical Benefits ◽  
The Subject ◽  
Common Barriers ◽  
Clinical Adoption

<b><i>Background:</i></b> Artificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches. <b><i>Summary:</i></b> Key challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications. <b><i>Key Messages:</i></b> There is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.

Information extraction from clinical notes: A systematic review for Chronic Diseases (Preprint)

2018 ◽  
Author(s):  
Seyedmostafa Sheikhalishahi ◽  
Riccardo Miotto ◽  
Joel T. Dudley ◽  
Alberto Lavelli ◽  
Fabio Rinaldi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Diseases ◽  
Language Processing ◽  
Metabolic Diseases ◽  
Circulatory System ◽  
Structured Data ◽  
Machine Learning Algorithms ◽  
Free Text ◽  
Rule Based ◽  
Clinical Notes

BACKGROUND Worldwide, the burden of chronic diseases is growing, necessitating novel approaches that complement and go beyond evidence-based medicine. In this respect a promising avenue is the secondary use of Electronic Health Records (EHR) data, where clinical data are analysed to conduct basic and clinical and translational research. Methods based on machine learning algorithms to process EHR are resulting in improved understanding of patients’ clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, wealth of patients’ clinical history remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on development of Natural Language Processing (NLP) methods to automatically transform clinical text into structured clinical data that can be directly processed using machine learning algorithms. OBJECTIVE To provide a comprehensive overview of the development and uptake on NLP methods applied to free-text clinical notes related to chronic diseases, including investigation of challenges faced by NLP methodologies in understanding clinical narratives. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes”, “natural language processing” and “chronic disease” as keywords as well as their variations to maximise coverage of the articles. RESULTS Of the 2646 articles considered, 100 met the inclusion criteria. Review of the included papers resulted in identification of 42 chronic diseases, which were then further classified into 10 diseases categories using ICD-10. Majority of the studies focused on diseases of circulatory system (N=38) while endocrine and metabolic diseases were fewest (N=12). This was due to the structure of clinical records related to metabolic diseases that typically contain much more structured data than medical records for diseases of circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches, however deep learning methods remain emergent (N=3). Consequently, majority of works focus on classification of disease phenotype, while only a handful of papers concern the extraction of comorbidities from the free-text or the integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. CONCLUSIONS Efforts are needed to improve (1) progression of clinical NLP methods from extraction towards understanding; (2) recognition of relations among entities, rather than entities in isolation; (3) temporal extraction to understand past, current and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.

A machine learning approach to identification of self-harm and suicidal ideation among military and police Veterans

2021 ◽  
Author(s):  
Sinisa Colic ◽  
Jiang Chen He ◽  
J. Don Richardson ◽  
Kate St. Cyr ◽  
James P. Reilly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Suicidal Ideation ◽  
Suicide Risk ◽  
Predictive Accuracy ◽  
Large Data ◽  
Suicide Risk Assessment ◽  
Data Set ◽  
Psychological Phenomena ◽  
Machine Learning Approach ◽  
Self Harm

LAY SUMMARY Combat Veterans are vulnerable to suicidal thoughts and behaviour. Many who die by suicide deny having suicidal ideation (SI). Typically, researchers try to find variables indicating the presence of SI using traditional statistical approaches. These approaches do not possess the capacity to detect highly complex multivariable interactions. In contrast, machine learning (ML) is designed to detect such patterns and can consequently yield much higher predictive accuracy. In this study, the authors trained ML algorithms using 192 variables extracted from questionnaires administered to 738 Veterans and serving personnel to detect the presence of self-harm and SI (SHSI). Using the 10 most predictive non-suicide-related items, the ML algorithms could detect SHSI with 75.3% accuracy. Most of these items reflect psychological phenomena that can change quickly over time, allowing repeated risk re-assessment from day to day. The study’s findings suggest that ML methods may play an important role in the discovery, within a large data set, of predictive patterns that might be useful in suicide risk assessment.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

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