Machine-Learning-Based Prediction of a Missed Scheduled Clinical Appointment by Patients With Diabetes

Kurasawa, Hisashi; Hayashi, Katsuyoshi; Fujino, Akinori; Takasugi, Kashiya; Haga, Tsuneyuki; Waki, Kayo; Noguchi, Takashi

doi:10.1177/1932296815614866

Cited by 50 publications

(52 citation statements)

References 11 publications

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“…A fuzzy approach was also presented by Eghbali-Zarch et al to address the problem of medication selection in T2D patients [ 148 ]. Finally, Kurasawa et al proposed a machine learning algorithm to predict missed clinical appointments and help patients continue regular doctor visits [ 149 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

383

207

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BackgroundArtificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.ObjectiveThe objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.MethodsA review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.ResultsWe propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.ConclusionsWe obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

383

207

View full text Add to dashboard Cite

show abstract

“…Longer gaps between appointments was a predictor for non‐attendance but there was no seasonal variation in attendance rates . A machine‐learning algorithm helped predict missed diabetes appointments at a Japanese hospital and found that ‘how and when’ an appointment was booked contributed more to attendance than the individual's clinical condition . Factors with the strongest predictive accuracy of a missed appointment included making appointments on a Sunday, scheduling appointments for a Friday, a history of diabetic ketoacidosis in those with type 2 diabetes and a recent prescription of Rilmazafone (a water‐soluble benzodiazepine).…”

Section: Resultsmentioning

confidence: 99%

Non‐attendance at diabetes outpatient appointments: a systematic review

et al. 2020

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Background Non-attendance at diabetes outpatient appointments is a sizeable problem worldwide and has been associated with suboptimal health outcomes. We aimed to describe the characteristics, health outcomes and reasons given for non-attendance at doctor-or nurse-led diabetes appointments, and interventions to improve attendance. Methods PubMed, EMBASE, CINAHL and PsychInfo were searched from database inception to February 2019. Included articles were peer-reviewed, published in English, related to adults or young people with type 1 or type 2 diabetes, and addressed one of the above aspects of non-attendance. Studies were excluded if reporting on other types of diabetes or reviewing attendance at structured education, retinal screening, paediatric, antenatal, podiatry or dietetic clinics. Results Thirty-four studies of varied designs were identified (15 observational, 1 randomized control trial, 9 qualitative, 5 surveys, 4 service improvements). The definition of non-attendance varied. Younger adults, smokers and those with financial pressures were less likely to attend. Non-attendance was associated with higher HbA 1c ; other outcomes were varied but typically worse in non-attenders. Reasons for non-attendance in qualitative studies fell into three categories: balancing the costs and benefits of attendance, coping strategies, and the relationships between the person with diabetes and healthcare professionals. Interventions included appointment management strategies, service improvements, patient navigators and WebCam appointments. Conclusions Non-attendance is only partially explained by logistical issues. Qualitative studies suggest complex psychosocial factors are involved. Interventions have progressed from simple appointment reminders in an attempt to address some of the psycho-social determinants, but more work is needed to improve attendance.

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“…They reported that the AUC increased slightly as more information became available, obtaining a maximum AUC of 0.706 in the 19th model. An article published that year by Kurasawa et al [ 30 ] deserves special attention. In this work, an LR with feature selection is conducted by means of an L2 regularization.…”

Section: Resultsmentioning

confidence: 99%

Patient No-Show Prediction: A Systematic Literature Review

Carreras-García

Delgado-Gómez

Llorente

et al. 2020

Entropy

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Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.

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Machine-Learning-Based Prediction of a Missed Scheduled Clinical Appointment by Patients With Diabetes

Abstract: Our findings may provide information to help clinicians make timely interventions to avoid MAs.

Cited by 50 publications

References 11 publications

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Non‐attendance at diabetes outpatient appointments: a systematic review

Patient No-Show Prediction: A Systematic Literature Review

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