Can machine learning be useful as a screening tool for depression in primary care?

2021 ◽  
Vol 132 ◽  
pp. 1-6
Author(s):  
Erito Marques de Souza Filho ◽  
Helena Cramer Veiga Rey ◽  
Rose Mary Frajtag ◽  
Daniela Matos Arrowsmith Cook ◽  
Lucas Nunes Dalbonio de Carvalho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Screening Tool

scholarly journals The Rapid Mood Screener: A Novel and Pragmatic Screener Tool for Bipolar I Disorder

CNS Spectrums ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 167-168
Author(s):  
C. Brendan Montano ◽  
Mehul Patel ◽  
Rakesh Jain ◽  
Prakash S. Masand ◽  
Amanda Harrington ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Health Outcomes ◽  
Screening Tool ◽  
Clinical Interview ◽  
Clinical Practices ◽  
Predictive Values ◽  
Bipolar I Disorder ◽  
Manic Symptoms ◽  
Depressive Episodes

AbstractIntroductionApproximately 70% of patients with bipolar disorder (BPD) are initially misdiagnosed, resulting in significantly delayed diagnosis of 7–10 years on average. Misdiagnosis and diagnostic delay adversely affect health outcomes and lead to the use of inappropriate treatments. As depressive episodes and symptoms are the predominant symptom presentation in BPD, misdiagnosis as major depressive disorder (MDD) is common. Self-rated screening instruments for BPD exist but their length and reliance on past manic symptoms are barriers to implementation, especially in primary care settings where many of these patients initially present. We developed a brief, pragmatic bipolar I disorder (BPD-I) screening tool that not only screens for manic symptoms but also includes risk factors for BPD-I (eg, age of depression onset) to help clinicians reduce the misdiagnosis of BPD-I as MDD.MethodsExisting questionnaires and risk factors were identified through a targeted literature search; a multidisciplinary panel of experts participated in 2 modified Delphi panels to select concepts thought to differentiate BPD-I from MDD. Individuals with self-reported BPD-I or MDD participated in cognitive debriefing interviews (N=12) to test and refine item wording. A multisite, cross-sectional, observational study was conducted to evaluate the screening tool’s predictive validity. Participants with clinical interview-confirmed diagnoses of BPD-I or MDD completed a draft 10-item screening tool and additional questionnaires/questions. Different combinations of item sets with various item permutations (eg, number of depressive episodes, age of onset) were simultaneously tested. The final combination of items and thresholds was selected based on multiple considerations including clinical validity, optimization of sensitivity and specificity, and pragmatism.ResultsA total of 160 clinical interviews were conducted; 139 patients had clinical interview-confirmed BPD-I (n=67) or MDD (n=72). The screening tool was reduced from 10 to 6 items based on item-level analysis. When 4 items or more were endorsed (yes) in this analysis sample, the sensitivity of this tool for identifying patients with BPD-I was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. These properties represent an improvement over the Mood Disorder Questionnaire, while using >50% fewer items.ConclusionThis new 6-item BPD-I screening tool serves to differentiate BPD-I from MDD in patients with depressive symptoms. Use of this tool can provide real-world guidance to primary care practitioners on whether more comprehensive assessment for BPD-I is warranted. Use of a brief and valid tool provides an opportunity to reduce misdiagnosis, improve treatment selection, and enhance health outcomes in busy clinical practices.FundingAbbVie Inc.

38. Development of a Screening Tool to Identify PrEP-Eligible Youth in Primary Care

2021 ◽  
Vol 68 (2) ◽  
pp. S21-S22
Author(s):  
Natalie J. Labossier ◽  
Angela R. Bazzi ◽  
Kimberly M. Nelson ◽  
Eugene S.G. Massey ◽  
Julie Potter ◽  
...  
Keyword(s):  
Primary Care ◽  
Screening Tool

scholarly journals Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: the Evaluation of Text Classification Algorithms Using Machine Learning

2019 ◽  
Author(s):  
Francesc López Seguí ◽  
Ricardo Ander Egg Aguilar ◽  
Gabriel de Maeztu ◽  
Anna García-Altés ◽  
Francesc García Cuyàs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Text Classification ◽  
Learning Strategy ◽  
Care Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Face To Face ◽  
Classification Tool ◽  
The Impact

Background: the primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: the study was intended to examine the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: 20 machine learning algorithms (based on 5 types of algorithms and 4 text representation techniques)were trained using a sample of 3,559 messages (169,102 words) corresponding to 2,268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: the best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query"(precision = 0.48). Conclusion: to the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

scholarly journals Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study

BJGP Open ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. bjgpopen18X101589 ◽  
Author(s):  
Emmanuel A Jammeh ◽  
Camille, B Carroll ◽  
Stephen, W Pearson ◽  
Javier Escudero ◽  
Athanasios Anastasiou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Case Control ◽  
Classification Model ◽  
Dementia Diagnosis ◽  
Machine Learning Classification ◽  
Primary Care Data ◽  
Dementia Risk ◽  
Diagnosis Of Dementia ◽  
Read Codes

BackgroundUp to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice.AimThe aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed.Design & settingThe study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.MethodRoutinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia.ResultsThe model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.ConclusionThe results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.

scholarly journals The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System (Preprint)

2018 ◽  
Author(s):  
Matthew Willis ◽  
Paul Duckworth ◽  
Angela Coulter ◽  
Eric T Meyer ◽  
Michael Osborne
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Health Care ◽  
Primary Health Care ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Care Staff ◽  
Primary Health ◽  
The Future

BACKGROUND Recent advances in technology have reopened an old debate on which sectors will be most affected by automation. This debate is ill served by the current lack of detailed data on the exact capabilities of new machines and how they are influencing work. Although recent debates about the future of jobs have focused on whether they are at risk of automation, our research focuses on a more fine-grained and transparent method to model task automation and specifically focus on the domain of primary health care. OBJECTIVE This protocol describes a new wave of intelligent automation, focusing on the specific pressures faced by primary care within the National Health Service (NHS) in England. These pressures include staff shortages, increased service demand, and reduced budgets. A critical part of the problem we propose to address is a formal framework for measuring automation, which is lacking in the literature. The health care domain offers a further challenge in measuring automation because of a general lack of detailed, health care–specific occupation and task observational data to provide good insights on this misunderstood topic. METHODS This project utilizes a multimethod research design comprising two phases: a qualitative observational phase and a quantitative data analysis phase; each phase addresses one of the two project aims. Our first aim is to address the lack of task data by collecting high-quality, detailed task-specific data from UK primary health care practices. This phase employs ethnography, observation, interviews, document collection, and focus groups. The second aim is to propose a formal machine learning approach for probabilistic inference of task- and occupation-level automation to gain valuable insights. Sensitivity analysis is then used to present the occupational attributes that increase/decrease automatability most, which is vital for establishing effective training and staffing policy. RESULTS Our detailed fieldwork includes observing and documenting 16 unique occupations and performing over 130 tasks across six primary care centers. Preliminary results on the current state of automation and the potential for further automation in primary care are discussed. Our initial findings are that tasks are often shared amongst staff and can include convoluted workflows that often vary between practices. The single most used technology in primary health care is the desktop computer. In addition, we have conducted a large-scale survey of over 156 machine learning and robotics experts to assess what tasks are susceptible to automation, given the state-of-the-art technology available today. Further results and detailed analysis will be published toward the end of the project in early 2019. CONCLUSIONS We believe our analysis will identify many tasks currently performed manually within primary care that can be automated using currently available technology. Given the proper implementation of such automating technologies, we expect considerable staff resources to be saved, alleviating some pressures on the NHS primary care staff. INTERNATIONAL REGISTERED REPOR DERR1-10.2196/11232

A computerized version of the Patient Health Questionnaire-4 as an ultra-brief screening tool to detect emotional disorders in primary care

2018 ◽  
Vol 234 ◽  
pp. 247-255 ◽  
Author(s):  
Antonio Cano-Vindel ◽  
Roger Muñoz-Navarro ◽  
Leonardo Adrián Medrano ◽  
Paloma Ruiz-Rodríguez ◽  
César González-Blanch ◽  
...  
Keyword(s):  
Primary Care ◽  
Emotional Disorders ◽  
Screening Tool ◽  
Patient Health Questionnaire ◽  
Health Questionnaire ◽  
Patient Health ◽  
Brief Screening

scholarly journals Prediction of vascular aging based on smartphone acquired PPG signals

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lorenzo Dall’Olio ◽  
Nico Curti ◽  
Daniel Remondini ◽  
Yosef Safi Harb ◽  
Folkert W. Asselbergs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Screening Tool ◽  
Computational Cost ◽  
Penalized Regression ◽  
Prediction Performance ◽  
Second Derivative ◽  
Vascular Aging ◽  
Non Invasive ◽  
Potential Biomarkers

AbstractPhotoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.

scholarly journals Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions

2019 ◽  
Vol 26 (12) ◽  
pp. 1493-1504 ◽  
Author(s):  
Jihyun Park ◽  
Dimitrios Kotzias ◽  
Patty Kuo ◽  
Robert L Logan IV ◽  
Kritzia Merced ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Classification Accuracy ◽  
Conditional Random Fields ◽  
Markov Models ◽  
Support Vector ◽  
Office Visits ◽  
Noisy Information ◽  
Gated Recurrent Units ◽  
Sequential Models

Abstract Objective Amid electronic health records, laboratory tests, and other technology, office-based patient and provider communication is still the heart of primary medical care. Patients typically present multiple complaints, requiring physicians to decide how to balance competing demands. How this time is allocated has implications for patient satisfaction, payments, and quality of care. We investigate the effectiveness of machine learning methods for automated annotation of medical topics in patient-provider dialog transcripts. Materials and Methods We used dialog transcripts from 279 primary care visits to predict talk-turn topic labels. Different machine learning models were trained to operate on single or multiple local talk-turns (logistic classifiers, support vector machines, gated recurrent units) as well as sequential models that integrate information across talk-turn sequences (conditional random fields, hidden Markov models, and hierarchical gated recurrent units). Results Evaluation was performed using cross-validation to measure 1) classification accuracy for talk-turns and 2) precision, recall, and F1 scores at the visit level. Experimental results showed that sequential models had higher classification accuracy at the talk-turn level and higher precision at the visit level. Independent models had higher recall scores at the visit level compared with sequential models. Conclusions Incorporating sequential information across talk-turns improves the accuracy of topic prediction in patient-provider dialog by smoothing out noisy information from talk-turns. Although the results are promising, more advanced prediction techniques and larger labeled datasets will likely be required to achieve prediction performance appropriate for real-world clinical applications.

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