Quantitative and temporal approach to utilising electronic medical records from general practices in mental health prediction

Półchłopek, Olga; Koning, Nynke R; Büchner, Frederike L.; Crone, Mathilde R.; Numans, Mattijs E.; Hoogendoorn, Mark

doi:10.1016/j.compbiomed.2020.103973

Cited by 9 publications

(8 citation statements)

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“…The XGBoost algorithm has been previously applied to mental health prediction. Six ML algorithms were used to predict mental health using electronic medical records, of which XGBoost obtained the highest AUC value ( 48 ). Therefore, ML, especially the XGBoost algorithm, is better for classification and prediction of the three physical frailty indices: 5-Item FRAIL, CHS, and SOF.…”

Section: Discussionmentioning

confidence: 99%

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index

Yang,

Chen,

Yang

et al. 2024

Front. Public Health

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BackgroundPhysical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteoporotic Fractures (SOF) indices, have been regularly used in clinical and research studies. However, no previous studies have investigated the predictive ability of machine learning (ML) for physical frailty assessment. The aim was to use two ML algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to predict these three physical frailty assessment models.Materials and methodsQuestionnaires regarding demographic characteristics, lifestyle habits, living environment, and physical frailty assessment were answered by 445 participants aged 60 years and above. The RF and XGBoost algorithms were used to assess their scores for the three physical frailty indices. Furthermore, feature importance and Shapley additive explanations (SHAP) were used to determine the important physical frailty factors.ResultsThe XGBoost algorithm obtained higher accuracy for predicting the three physical frailty indices; the areas under the curve obtained by the XGBoost algorithm for the 5-Item FRAIL, CHS, and SOF indices were 0.84. 0.79, and 0.69, respectively. The feature importance and SHAP of the XGBoost algorithm revealed that systolic blood pressure, diastolic blood pressure, age, and body mass index play important roles in all three physical frailty models.ConclusionThe XGBoost algorithm has a more accurate predictive rate than RF across all three physical frailty assessments. Thus, ML can be a useful tool for the early detection of physical frailty.

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Section: Discussionmentioning

confidence: 99%

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index

Yang,

Chen,

Yang

et al. 2024

Front. Public Health

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“…In 22 studies of this ICD-10 classifications addressing six health conditions [28,45,[89][90][91][92][93][94][95][96][97]119,81,120,121,[82][83][84][85][86][87][88], the involved population were from eight countries, mainly the US and the UK (n=14). These studies were published since 2013 with the highest number of studies in 2020 (44.4%).…”

Section: Health Conditionsmentioning

confidence: 99%

“…A study predicted anxiety (F41) in cancer survivors seeking care in PHC and suggested that fatigue and insomnia were the most important predictors [86]. Lastly, a study used PHC data to predict any mental disorder using different ML modes, claimed that the potentially successful prediction was the best before 180 days of real diagnosis [93].…”

Section: Health Conditionsmentioning

confidence: 99%

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Abdulazeem

Whitelaw

Schauberger

et al. 2022

Preprint

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Aim: With the rapid advances in technology and data science, machine learning (ML) is being adopted by the health care sector; but there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC). To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify the health conditions targeted by ML in PHC. Methods: We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included any primary study addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. We performed literature screening, data extraction, and risk of bias assessment. Health conditions were categorized according to international classification of diseases. Extracted date were analyzed quantitatively and qualitatively. Results: We identified 109 studies investigating 42 health conditions. These studies included 273 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 82% of the studies were retrospective. 76.6% of the studies reported diagnostic predictive ML models. 77% of all reported models aimed for models’ development without external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were Alzheimer’s disease and diabetes mellitus. Conclusions: To the best of our knowledge, this is the first review to investigate the extent of the health conditions targeted by the ML prediction models within PHC settings. Our study provides an important summary on the presently available ML models in PHC, which can be used in further research and implementation efforts.

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“…A survey of Dutch GPs showed that GPs are still in need of tools for PSS-related diagnostics 20. Studies have shown that routine care data can be responsibly used for predictive modelling 39 40. The development of prediction models based on routine primary care data may enable screening based on readily available clinical information and support GPs in their practice.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies reveal the multi-applicability of routine care data since it can be used in several different ways. Approaches range from the more classic theory-driven approaches, simple data-driven approaches41 and more complex temporal data-mining techniques 39 40…”

Section: Introductionmentioning

confidence: 99%

Early identification of persistent somatic symptoms in primary care: data-driven and theory-driven predictive modelling based on electronic medical records of Dutch general practices

et al. 2023

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ObjectiveThe present study aimed to early identify patients with persistent somatic symptoms (PSS) in primary care by exploring routine care data-based approaches.Design/settingA cohort study based on routine primary care data from 76 general practices in the Netherlands was executed for predictive modelling.ParticipantsInclusion of 94 440 adult patients was based on: at least 7-year general practice enrolment, having more than one symptom/disease registration and >10 consultations.MethodsCases were selected based on the first PSS registration in 2017–2018. Candidate predictors were selected 2–5 years prior to PSS and categorised into data-driven approaches: symptoms/diseases, medications, referrals, sequential patterns and changing lab results; and theory-driven approaches: constructed factors based on literature and terminology in free text. Of these, 12 candidate predictor categories were formed and used to develop prediction models by cross-validated least absolute shrinkage and selection operator regression on 80% of the dataset. Derived models were internally validated on the remaining 20% of the dataset.ResultsAll models had comparable predictive values (area under the receiver operating characteristic curves=0.70 to 0.72). Predictors are related to genital complaints, specific symptoms (eg, digestive, fatigue and mood), healthcare utilisation, and number of complaints. Most fruitful predictor categories are literature-based and medications. Predictors often had overlapping constructs, such as digestive symptoms (symptom/disease codes) and drugs for anti-constipation (medication codes), indicating that registration is inconsistent between general practitioners (GPs).ConclusionsThe findings indicate low to moderate diagnostic accuracy for early identification of PSS based on routine primary care data. Nonetheless, simple clinical decision rules based on structured symptom/disease or medication codes could possibly be an efficient way to support GPs in identifying patients at risk of PSS. A full data-based prediction currently appears to be hampered by inconsistent and missing registrations. Future research on predictive modelling of PSS using routine care data should focus on data enrichment or free-text mining to overcome inconsistent registrations and improve predictive accuracy.

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Quantitative and temporal approach to utilising electronic medical records from general practices in mental health prediction

Cited by 9 publications

References 50 publications

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Early identification of persistent somatic symptoms in primary care: data-driven and theory-driven predictive modelling based on electronic medical records of Dutch general practices

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