A machine learning approach for predicting treatment response of hyponatremia
Hyponatremia leads to severe central nervous system disorders and requires immediate treatment in some cases. However, a rapid increase in serum sodium (s-Na) concentration could cause osmotic demyelination syndrome. To achieve a safety hyponatremia treatment, we develop a prediction model of s-Na c...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Japan Endocrine Society
2024-04-01
|
| Series: | Endocrine Journal |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/endocrj/71/4/71_EJ23-0561/_html/-char/en |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591672965332992 |
|---|---|
| author | Tamaki Kinoshita Shintaro Oyama Daisuke Hagiwara Yoshinori Azuma Hiroshi Arima |
| author_facet | Tamaki Kinoshita Shintaro Oyama Daisuke Hagiwara Yoshinori Azuma Hiroshi Arima |
| author_sort | Tamaki Kinoshita |
| collection | DOAJ |
| description | Hyponatremia leads to severe central nervous system disorders and requires immediate treatment in some cases. However, a rapid increase in serum sodium (s-Na) concentration could cause osmotic demyelination syndrome. To achieve a safety hyponatremia treatment, we develop a prediction model of s-Na concentration using a machine learning. Among the 341 and 47 patients admitted to two tertiary hospitals for hyponatremia treatment (s-Na <130 mEq/L), those who were admitted to the general unit with urine sodium <20 mEq/L or treated with desmopressin were excluded. Ultimately, 74 and 15 patients (342 and 146 6-hourly datasets) were included in the learning and validation data, respectively. We trained the prediction model using three regression algorithms for shallow machine learning to predict s-Na every 6 h during treatment with the data of patients with hyponatremia (median s-Na: 112.5 mEq/L; range: 110.0–116.8 mEq/L) from one hospital. The model was validated externally using the data of patients with hyponatremia (median s-Na: 117.0 mEq/L; range: 112.9–120.0 mEq/L) from another hospital. Using 5–7 predictors (water intake, sodium intake, potassium intake, urine volume, s-Na concentration, serum potassium concentration, serum chloride concentration), the support vector regression model showed the best performance overall (root mean square error = 0.05396; R2 = 0.92), followed by the linear regression and regression tree models. The predicted s-Na levels, using explainable machine learning algorithms and clinically accessible parameters, correlated well with the actual levels. Thus, our model could be applied to the treatment of hyponatremia in clinical practice. |
| format | Article |
| id | doaj-art-046263761ef441d9b57af8ddd1de1f3c |
| institution | Kabale University |
| issn | 1348-4540 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | The Japan Endocrine Society |
| record_format | Article |
| series | Endocrine Journal |
| spelling | doaj-art-046263761ef441d9b57af8ddd1de1f3c2025-01-22T06:37:03ZengThe Japan Endocrine SocietyEndocrine Journal1348-45402024-04-0171434535510.1507/endocrj.EJ23-0561endocrjA machine learning approach for predicting treatment response of hyponatremiaTamaki Kinoshita0Shintaro Oyama1Daisuke Hagiwara2Yoshinori Azuma3Hiroshi Arima4Department of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya 466-8550, JapanInnovative Research Center for Preventive Medical Engineering (PME), Institutes of Innovation for Future Society, Nagoya University, Nagoya 464-8601, JapanDepartment of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya 466-8550, JapanDepartment of Endocrinology and Diabetes, the Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, Nagoya 466-8650, JapanDepartment of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya 466-8550, JapanHyponatremia leads to severe central nervous system disorders and requires immediate treatment in some cases. However, a rapid increase in serum sodium (s-Na) concentration could cause osmotic demyelination syndrome. To achieve a safety hyponatremia treatment, we develop a prediction model of s-Na concentration using a machine learning. Among the 341 and 47 patients admitted to two tertiary hospitals for hyponatremia treatment (s-Na <130 mEq/L), those who were admitted to the general unit with urine sodium <20 mEq/L or treated with desmopressin were excluded. Ultimately, 74 and 15 patients (342 and 146 6-hourly datasets) were included in the learning and validation data, respectively. We trained the prediction model using three regression algorithms for shallow machine learning to predict s-Na every 6 h during treatment with the data of patients with hyponatremia (median s-Na: 112.5 mEq/L; range: 110.0–116.8 mEq/L) from one hospital. The model was validated externally using the data of patients with hyponatremia (median s-Na: 117.0 mEq/L; range: 112.9–120.0 mEq/L) from another hospital. Using 5–7 predictors (water intake, sodium intake, potassium intake, urine volume, s-Na concentration, serum potassium concentration, serum chloride concentration), the support vector regression model showed the best performance overall (root mean square error = 0.05396; R2 = 0.92), followed by the linear regression and regression tree models. The predicted s-Na levels, using explainable machine learning algorithms and clinically accessible parameters, correlated well with the actual levels. Thus, our model could be applied to the treatment of hyponatremia in clinical practice.https://www.jstage.jst.go.jp/article/endocrj/71/4/71_EJ23-0561/_html/-char/enhyponatremiapredictive machine learning toolrevised adrogué–madias formulaprevention of osmotic demyelination syndromemonitoring for electrolyte abnormalities |
| spellingShingle | Tamaki Kinoshita Shintaro Oyama Daisuke Hagiwara Yoshinori Azuma Hiroshi Arima A machine learning approach for predicting treatment response of hyponatremia Endocrine Journal hyponatremia predictive machine learning tool revised adrogué–madias formula prevention of osmotic demyelination syndrome monitoring for electrolyte abnormalities |
| title | A machine learning approach for predicting treatment response of hyponatremia |
| title_full | A machine learning approach for predicting treatment response of hyponatremia |
| title_fullStr | A machine learning approach for predicting treatment response of hyponatremia |
| title_full_unstemmed | A machine learning approach for predicting treatment response of hyponatremia |
| title_short | A machine learning approach for predicting treatment response of hyponatremia |
| title_sort | machine learning approach for predicting treatment response of hyponatremia |
| topic | hyponatremia predictive machine learning tool revised adrogué–madias formula prevention of osmotic demyelination syndrome monitoring for electrolyte abnormalities |
| url | https://www.jstage.jst.go.jp/article/endocrj/71/4/71_EJ23-0561/_html/-char/en |
| work_keys_str_mv | AT tamakikinoshita amachinelearningapproachforpredictingtreatmentresponseofhyponatremia AT shintarooyama amachinelearningapproachforpredictingtreatmentresponseofhyponatremia AT daisukehagiwara amachinelearningapproachforpredictingtreatmentresponseofhyponatremia AT yoshinoriazuma amachinelearningapproachforpredictingtreatmentresponseofhyponatremia AT hiroshiarima amachinelearningapproachforpredictingtreatmentresponseofhyponatremia AT tamakikinoshita machinelearningapproachforpredictingtreatmentresponseofhyponatremia AT shintarooyama machinelearningapproachforpredictingtreatmentresponseofhyponatremia AT daisukehagiwara machinelearningapproachforpredictingtreatmentresponseofhyponatremia AT yoshinoriazuma machinelearningapproachforpredictingtreatmentresponseofhyponatremia AT hiroshiarima machinelearningapproachforpredictingtreatmentresponseofhyponatremia |