Urine Creatinine Concentrations in Drug Monitoring Participants and Hospitalized Patients

Love, Sara A.; Seegmiller, Jesse C.; Kloss, Julie; Apple, Fred S.

doi:10.1093/jat/bkw092

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…From this result, we discover a parameter hierarchy, with the creatinine concentration (serum and phosphokinase) as the relevant parameter. This result makes sense regarding the fact that the creatinine concentration is a metabolite that indicates how good the patient's adherence to therapy is [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks

Ochoa¹,

Csiszár

Schimper³

2021

BMC Med Inform Decis Mak

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Background Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for each patient accurately and individually. However, physicians can reduce their workload by using recommender systems, still having every decision under control. In this way, they also get an insight into how other physicians make treatment decisions in each situation. In this work, we report the development of a novel recommender system that uses predicted outcomes based on continuous-valued logic and multi-criteria decision operators. The advantage of this methodology is that it is transparent, since the model outcomes emulate logical decision processes based on the hierarchy of relevant physiological parameters, and second, it is safer against adversarial attacks than conventional deep learning methods since it drastically reduces the number of trainable parameters. Methods We test our methodology in a patient population with diabetes and heart insufficiency that becomes a therapy (beta-blockers, ACE or Aspirin). The original database (Pakistan database) is publicly available and accessible via the internet. However, to explore methods to protect the patient's identity and guarantee data privacy we implemented a methodology on a variable-by-variable basis by fitting a sequence of regression models and drawing synthetic values from the corresponding predictive distributions using linear regressions and norm rank. Furthermore, we implemented a deep-learning model based on logical gates modeled by perceptrons with fixed weights and biases. While a first trainable layer automatically recognizes a meaningful parameter hierarchy, the implemented Logic-Operator Neuronal Network (LONN) simulates cognitive processes like a rational, logical thinking process, considering that this logic is joined by fuzziness, i.e., logical operations are not exact but essentially fuzzy due to the implemented continuous-valued operators. The predicted outcomes of the model (kind of therapy-ACE, Aspirin or beta-blocker- and expected therapy time of the patient) are then implemented in a recommender system that compares two different models: model 1 trained on a population excluding negative outcomes (patient group 1, with no patient dead and long therapy times) and a model 2 trained on the whole patient population (patient group 2). In this way, we provide a recommendation of the best possible therapy based on the outcome of the model and the confidence of this recommendation when the outcome of model 1 is compared with the outcome of model 2. Results With the applied method for data synthetization, we obtained an error of about 1% for all the relevant parameters. Furthermore, we demonstrate that the LONN models reach an accuracy of about 75%. After comparing the LONN models against conventional deep-learning models we observe that our implemented models are less accurate (accuracy loss of about 8%). However, the loss of accuracy is compensated by the fact that LONN models are transparent and safe because the freezing of training parameters makes them less prone to adversarial attacks. Finally, we predict the best therapy as well as the expected therapy time. We were able to predict individualized therapies, which were classified as optimal (binary value) when the prediction fully matched predictions made with models 1 and 2. The results provided by the recommender system are displayed using a graphical interface. The current is a proof of concept to improve the quality of the disease management, while the methods are continuously visualized to preserve transparency for the customers. Conclusions This work contributes to simplify administrative functions and boost the quality of management of patients improving the quality of healthcare with models that are both transparent and safe. Our methodology can be extended to different clinical scenarios where recommender systems can be applied. The acceptance and further development of the app is one of the next important steps and still requires further development depending on specific requirements of the health management, the physicians or health professionals, and the patent population.

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Section: Resultsmentioning

confidence: 99%

Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks

Ochoa¹,

Csiszár

Schimper³

2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…40 The World Alzheimer's Report has estimated there are more than 46 million AD patients worldwide, with an expected increase to 130 million by 2050. [40][41][42] Given that brain pathology precedes clinical symptoms, Kim et al performed MS-based metabolomics to detect whether metabolites in plasma were pathologically related, like cerebrospinal uid (CFS). 43 Healthy individuals (n ¼ 242), patients with mild cognitive impairment (n ¼ 236), and patients with AD-type dementia (n ¼ 115) were included, and logistic regressions were employed to study the relationships between plasma metabolites, CSF markers, magnetic resonance imaging, and cognitive and clinical diagnosis.…”

Section: Metabolomic Analysis For Clinical Biomarker Discoverymentioning

confidence: 99%

Mass spectrometry-based metabolomics in health and medical science: a systematic review

Zhang

et al. 2020

RSC Adv.

129

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“…This result makes sense regarding the fact that the creatinine concentration is a metabolite that indicates how good the patient adherence to a therapy is. (Love et al, 2016). The final recommendations are then visualized in a dashboard, as is shown in figure 10.…”

Section: Model Consumption and Visualization Of Recommendationsmentioning

confidence: 99%

Medical Recommender Systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks

Ochoa¹,

Csiszár

Schimper³

2021

Preprint

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Background Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for an individual patient accurately and individually. However, physicians can reduce their workload by using Recommender Systems, still having every decision under control. In this way, they also get an insight into how other physicians make treatment decisions in each situation. In this work, we report the development of a novel recommender system that uses predicted outcomes based on continuous-valued logic and multi-criteria decision operators. The advantage of this methodology is that it is transparent, since the model outcomes emulate logical decision processes based on the hierarchy of relevant physiological parameters, and second, it is safer against adversarial attacks than conventional deep learning methods since it drastically reduces the number of trainable parameters.Methods We test our methodology in a patient population with diabetes and heart insufficiency that becomes a therapy (beta-blockers, ACE or Aspirin). The original database (Pakistan database) is publicly available and accessible via the internet. However, to explore methods to protect the patient`s identity and guarantee data privacy we implemented a methodology on a variable-by-variable basis by ﬁtting a sequence of regression models and drawing synthetic values from the corresponding predictive distributions using linear regressions and norm rank.Furthermore, we implemented a deep-learning model based on logical gates modeled by perceptrons with fixed weights and biases. While a first trainable layer automatically recognizes a meaningful parameter hierarchy, the implemented Logic-Operator Neuronal Network (LONN) simulates cognitive processes like a rational, logical thinking process, considering that this logic is joined by fuzziness, i.e., logical operations are not exact but essentially fuzzy due to the implemented continuous-valued operators.The predicted outcomes of the model (kind of therapy -ACE, Aspirin or beta-blocker- and expected therapy time of the patient) are then implemented in a recommender system that compares two different models: model 1 trained on a population excluding negative outcomes (patient group 1, with no patient dead and long therapy times) and a model 2 trained on the whole patient population (patient group 2). In this way, we provide a recommendation of the best possible therapy based on the outcome of model 1and the confidence of this recommendation when the outcome of model 1 is compared with the outcome of model 2. Results With the applied method for data synthetization, we obtained an error of about 1% for all the relevant parameters. Furthermore, we demonstrate that the LONN models reach an accuracy of about 75%. After comparing the LONN models against conventional deep-learning models we observe that our implemented models are less accurate (accuracy loss of about 8%). However, the loss of accuracy is compensated by the fact that LONN models are transparent and safe because the freezing of training parameters makes them less prone to adversarial attacks. Finally, we predict the best therapy as well as the expected therapy time. We were able to predict individualized therapies, which were classified as optimal (binary value) when the prediction fully matched predictions made with models 1 and 2. The results provided by the recommender system are displayed using a graphical interface. The current is a proof of concept to improve the quality of the disease management, while the methods are continuously visualized to preserve transparency for the customers. Conclusions This work contributes to boost administrative functions and quality of management of patients and improve the quality of healthcare with models that are both transparent and safe. Our methodology can be extended to different clinical scenarios where recommender systems can be applied. The acceptance and further development of the app is one of the next important steps and still requires further development depending on specific requirements of the health management, the physicians or health professionals, and the patent population.

show abstract

Urine Creatinine Concentrations in Drug Monitoring Participants and Hospitalized Patients

Cited by 12 publications

References 21 publications

Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks

Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks

Mass spectrometry-based metabolomics in health and medical science: a systematic review

Medical Recommender Systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks

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