Prediction of Pregnancy-Induced Hypertension Levels Using Machine Learning Algorithms

Hiwale, Anuja; Talele, Pratvina; Phalnikar, Rashmi

doi:10.1007/978-981-32-9515-5_56

Cited by 1 publication

(11 citation statements)

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“…A MAP anomaly can be either hypotensive or hypertensive and is defined as an interval where the values exceed thresholds specified for at least a minute [1][2][3]. Patients with anesthesia, surgery, and intensive care [1][2][3][4][5][6] are likely to experience anomalous levels of MAP. Clinically, it has been concluded that anomalous MAP is associated with various complications such as acute kidney injury, increased postoperative myocardial infarction levels, cardiovascular risks, as well as organ failure and death [1][2][3][4][5][6].…”

Section: Clinical Motivations and Challengesmentioning

confidence: 99%

“…Episodes of anomalous mean arterial pressure (MAP) values are frequent at the bedside as they are often associated with anesthesia. These episodes are linked to cardiovascular risks, multiple organ failure, and lifethreatening complications, especially in critically ill patients [1][2][3][4][5][6]. As a result, evolving research activity has begun to develop real-time decision support tools to predict MAP to enable preventive preparations and reduce complications [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…These episodes are linked to cardiovascular risks, multiple organ failure, and lifethreatening complications, especially in critically ill patients [1][2][3][4][5][6]. As a result, evolving research activity has begun to develop real-time decision support tools to predict MAP to enable preventive preparations and reduce complications [1][2][3][4][5][6]. The use of machine learning in clinical decision support systems has become a current research hotspot [2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, evolving research activity has begun to develop real-time decision support tools to predict MAP to enable preventive preparations and reduce complications [1][2][3][4][5][6]. The use of machine learning in clinical decision support systems has become a current research hotspot [2][3][4][5][6][7][8][9][10][11]. These systems are based on offline analysis to save the challenge of processing streams in real time [3][4][5][6][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…The use of machine learning in clinical decision support systems has become a current research hotspot [2][3][4][5][6][7][8][9][10][11]. These systems are based on offline analysis to save the challenge of processing streams in real time [3][4][5][6][9][10][11]. The latter requires machine learning algorithms that are capable of adapting to high-speed streams and their fast-changing characteristics [12].…”

Section: Introductionmentioning

confidence: 99%

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A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

et al. 2020

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Mean arterial pressure (MAP) is an important clinical parameter to evaluate the health of critically ill patients in intensive care units. Thus, the real time clinical decision support systems detecting anomalies and deviations in MAP enable early interventions and prevent serious complications. The state-ofthe-art decision support systems are based on a three-phase method that applies offline training, transfer learning, and retraining at the bedside. Their applicability in critical care units is challenging with delay and inaccuracy. In this paper, we propose a real time clinical decision support system forecasting the MAP status at the bedside using a new machine learning structure. The proposed system works in real time at the bedside without requiring the offline phase for training using large datasets. It thereby enables timely interventions and improved healthcare services. The proposed machine learning structure includes two stages. Stage I applies online learning using hierarchical temporal memory (HTM) to enable real time stream processing and provides unsupervised predictions. To the best of our knowledge, this is the first time it is applied to medical signals. Stage II is a long short-term memory (LSTM) classifier that forecasts the status of the patient's MAP ahead of time based on Stage I stream predictions. We perform a thorough performance evaluation of the proposed system and compare it with the state-of-the-art systems employing logistic regression (LR). The comparison shows the proposed system outperforms LR in terms of the classification accuracy, recall, precision, and area under the receiver operation curve (AUROC). INDEX TERMS Clinical decision support, classification, hierarchical temporal memory (HTM), long shortterm memory (LSTM), machine learning, real time prediction.

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