Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes

Verma, Deepika; Jansen, Duncan; Bach, Kerstin; Poel, Mannes; Mork, Paul Jarle; d'Hollosy, Wendy Oude Nijeweme

doi:10.1186/s12911-022-01973-9

Cited by 5 publications

(3 citation statements)

References 52 publications

(65 reference statements)

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“…The R 2 value is a goodness-of-fit measure that measures the ratio of variance explained by the independent variable(s) for the dependent variable in a regression model with values in the range [0,1], indicating how well the model predicts unseen data samples. Where 0 indicates that there is no observed variance and 1 indicates that the variance in the dependent variable is 100% due to the movement of the independent variable(s) (Verma et al, 2022).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

YILMAZ,

KUVAT

2023

UUJFE

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Overall equipment effectiveness (OEE) describes production efficiency by combining availability, performance, and quality and is used to evaluate production equipment’s performance. This research’s aim is to investigate the potential of the feature selection techniques and the multiple linear regression method, which is one of the machine learning techniques, in successfully predicting the OEE of the corrugated department of a box factory. In the study, six different planned downtimes and information on seventeen different previously known concepts related to activities to be performed are used as input features. Moreover, backward elimination, forward selection, stepwise selection, correlation-based feature selection (CFS), genetic algorithm, random forest, extra trees, ridge regression, lasso regression, and elastic net feature selection methods are proposed to find the most distinctive feature subset in the dataset. As a result of the analyses performed on the data set consisting of 23 features, 1 output and 1204 working days of information, the elastic net - multiple linear regression model, which selects 19 attributes, gave the best average R2 value compared to other models developed. Occam's razor principle is taken into account since there is not a great difference between the average R2 values obtained. Among the models developed according to the principle, the stepwise selection - multiple linear regression model yielded the best R2 value among those that selected the fewest features.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

YILMAZ,

KUVAT

2023

UUJFE

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“… 25 With the widespread implementation of the EMR and increased computing power, machine learning techniques are increasingly being used to predict outcomes as well. These techniques have been used to predict patient-reported outcomes after procedures, including total joint arthroplasty 26 and the treatment of various musculoskeletal conditions, including lower back pain 27 and end-stage ankle arthritis. 28 …”

Section: Using Big Data To Gain Insights About/from Promsmentioning

confidence: 99%

Population-Based Applications and Analytics Using Patient-Reported Outcome Measures

MacLean

Antao

Chin

et al. 2023

J Am Acad Orthop Surg

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The intersection of big data and artificial intelligence (AI) has resulted in advances in numerous areas, including machine learning, computer vision, and natural language processing. Although there are many potentially transformative applications of AI in health care, including precision medicine, this industry has been slow to adopt these technologies. At the same time, the operations of health care have historically been system-directed and physician-directed rather than patient-centered. The application of AI to patient-reported outcome measures (PROMs), which provide insight into patient-centered health outcomes, could steer research and healthcare delivery toward decisions that optimize outcomes important to patients. Historically, PROMs have only been collected within research registries. However, the increasing availability of PROMs within electronic health records has led to their inclusion in big data ecosystems, where they can inform or be informed by other data elements. The use of big data to analyze PROMs can help establish norms, evaluate data distribution, and determine proportions of patients achieving change or threshold standards. This information can be used for benchmarking, risk adjustment, predictive modeling, and ultimately improving the health of individuals and populations.T he terms "big data," "machine learning," and "artificial intelligence (AI)" have become catchphrases, at times promoting the concept that if we simply combine large enough databases with the right AI, we can solve any problem and gain hither-to-now unknown, profound insights that will solve the world's most challenging problems. This oversimplification belies the necessary contribution of human experts in both data science and the disciplines to which these advanced analytics are applied. Such partnerships have resulted in numerous advances that affect our daily lives with applications, such as facial recognition (eg, iPhone and US Customs and Border Protection), recommender systems (eg, Amazon and Spotify), autonomous driving (eg, Tesla), and natural language processing (eg, Google

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“…Moreover, incorporating patient-reported outcome measures (PROMs) into AI models can provide even more personalized information that considers a patient's unique circumstances and experiences. This can help guide patients toward the best treatment options for them and improve their outcomes (19).…”

Section: Introductionmentioning

confidence: 99%

Patients' Attitudes Towards the Use of AI-Based Decision Aids for Breast Cancer Treatment Decision-Making: A Qualitative Study

Hasannejadasl,

Offermann,

Essink

et al. 2023

Preprint

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Objectives: While AI has the potential to transform cancer care, there has been limited progress in incorporating AI tools into clinical practice. As healthcare providers work towards enhancing patient satisfaction and care quality, understanding patients' attitudes towards AI is crucial to facilitate the adoption of these tools in clinical settings. Despite this, few studies have explored patients' views on AI-based decision aids. The aim of this research is to explore the perceptions of cancer patients towards the use of AI-powered decision aids in medical decision-making. Methods: To explore the patient perspective on AI-based decision aids, the study conducted 12 semi-structured interviews with former breast cancer patients recruited through the Dutch Breast Cancer Association (BVN). The interviews covered a range of topics such as treatment recommendations, side effect prediction, survival, and recurrence. After transcription, the interviews were analyzed using thematic analysis to identify recurring themes and relevant quotes associated with each theme. The study analyzed the patients' responses in three primary domains: their familiarity with AI, the use of AI in various scenarios related to outcomes, and a comparison of AI and MD. Results: Patients' familiarity with AI was found to vary depending on their demographics, with younger and highly educated patients demonstrating a better understanding of AI. Generally, patients had a positive attitude towards AI when used for less critical scenarios such as side effects and treatment recommendations. However, when it came to more severe cases like the prediction of survival and recurrence after treatment, patients were hesitant to trust AI. The participants identified trust as a crucial factor affecting their willingness to use AI, with most of them being positive towards using AI only if they had the chance to consult with an MD. Despite the recognition of the human nature of MDs and their potential to make errors, patients still trusted them more than AI. Participants’ reluctance to accept AI was also partly attributed to the belief that AI cannot consider individuals' unique circumstances, making it more suitable for the average population. Moreover, lack of health literacy and digital skills, as well as ambiguity about accountability in case of errors, were identified as barriers to the adoption of AI in healthcare. Conclusion: This qualitative study sheds light on the perceptions of former breast cancer patients in the Netherlands regarding the use of AI in medical decision-making. The findings suggest that patients are generally open to the idea of utilizing AI-based programs to aid in decision-making, but have reservations about using them in high-stakes situations like survival and recurrence predictions. To address these concerns, the study highlights the significance of increasing awareness and understanding of AI's potential in personalized medicine, and creating educational resources for various health areas. Collaboration between healthcare providers, systems, and AI developers is essential, as well as well-defined protocols for accountability and liability in cases of patient harm. Future research should aim to diversify the patient population and provide an accurate representation of the AI program's capabilities to prevent misinterpretation.

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Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes

Cited by 5 publications

References 52 publications

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Population-Based Applications and Analytics Using Patient-Reported Outcome Measures

Patients' Attitudes Towards the Use of AI-Based Decision Aids for Breast Cancer Treatment Decision-Making: A Qualitative Study

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