Evolving classification of intensive care patients from event data

Objective: This work aims at predicting the patient discharge outcome on each hospitalization day by introducing a new paradigm—evolving classification of event data streams. Most classification algorithms implicitly assume the values of all predictive features to be available at the time of making the prediction. This assumption does not necessarily hold in the evolving classification setting (such as intensive care patient monitoring), where we may be interested in classifying the monitored entities as early as possible, based on the attributes initially available to the classifier, and then keep refining our classification model at each time step (e.g., on daily basis) with the arrival of additional attributes. / Materials and methods: An oblivious read-once decision-tree algorithm, called information network (IN), is extended to deal with evolving classification. The new algorithm, named incremental information network (IIN), restricts the order of selected features by the temporal order of feature arrival. The IIN algorithm is compared to six other evolving classification approaches on an 8-year dataset of adult patients admitted to two Intensive Care Units (ICUs) in the United Kingdom. / Results: Retrospective study of 3452 episodes of adult patients (≥ 16 years of age) admitted to the ICUs of Guy’s and St. Thomas’ hospitals in London between 2002 and 2009. Random partition (66:34) into a development (training) set n = 2287 and validation set n = 1165. Episode-related time steps: Day 0—time of ICU admission, Day x—end of the x-th day at ICU. The most accurate decision-tree models, based on the area under curve (AUC): Day 0: IN (AUC = 0.652), Day 1: IIN (AUC = 0.660), Day 2: J48 decision-tree algorithm (AUC = 0.678), Days 3–7: regenerative IN (AUC = 0.717–0.772). Logistic regression AUC: 0.582 (Day 0)—0.827 (Day 7). / Conclusions: Our experimental results have not identified a single optimal approach for evolving classification of ICU episodes. On Days 0 and 1, the IIN algorithm has produced the simplest and the most accurate models, which incorporate the temporal order of feature arrival. However, starting with Day 2, regenerative approaches have reached better performance in terms of predictive accuracy

Ανάπτυξη μοντέλων πρόβλεψης με Μηχανική Μάθηση στην αποκατάσταση ασθενών της ΜΕΘ

Η Μονάδα Εντατικής Θεραπείας είναι ένα τμήμα όπου η πρόβλεψη είναι ζωτικής σημασίας για την πορεία και την αποκατάσταση των ασθενών. Στην παρούσα εργασία αναπτύχθηκαν μοντέλα πρόβλεψης με Μηχανική Μάθηση. Τα δεδομένα της μελέτης συλλέχθησαν κατά τη διάρκεια της πολυκεντρικής μελέτης «Παράγοντες και Πρακτικές Πρόληψης και Αποκατάστασης σε Ασθενείς της ΜΕΘ». Το δείγμα της μελέτης αποτελείται από 150 ασθενείς που εισήχθησαν στη μονάδα και παρέμειναν σε μηχανικό αερισμό για τουλάχιστον τρεις ημέρες. Τα δεδομένα επεξεργάστηκαν μέσω επιβλεπόμενης μάθησης και συγκεκριμένα την τεχνική της κατηγοριοποίησης. Αναπτύχθηκαν μοντέλα για την πρόβλεψη της τραχειοστομίας, της έκβασης από την ΜΕΘ και για την πρόβλεψη της ικανότητας βάδισης κατά την έξοδο από το νοσοκομείο. Έγινε εφαρμογή ενός αλγορίθμου πρόσθετων επεξηγήσεων SHAP, ώστε να λάβουμε εξηγήσεις για τα χαρακτηριστικά που οδηγούν έναν συγκεκριμένο ασθενή σε συγκεκριμένη πρόβλεψη. Τα παραγόμενα μοντέλα είχαν ισχυρό ποσοστό ακρίβειας που φτάνει στο 85,9%, 75,8% και 93,3% αντίστοιχα αλλά με χαμηλή προγνωστική αξία, καθώς η πρόβλεψη δεν γίνεται έγκαιρα. Το μοντέλο της πρόβλεψης της ικανότητας βάδισης αποτελεί ένα πρωτότυπο εύρημα που μπορεί να καθορίσει τους στόχους της αποκατάστασης και θα αποτελέσει σημείο αναφοράς για περεταίρω έρευνα.The Intensive Care Unit is a department where prediction is vital for patients' progress and rehabilitation. In this paper, prediction models were developed with Machine Learning. The data of the study was collected during the multicenter study "Factors and Practices of Prevention and Rehabilitation in ICU Patients". The study sample consists of 150 patients admitted to the unit and received mechanical ventilation for at least three days. The data was modelled with supervised learning and specifically the classification technique. Different models have been developed to predict tracheostomy, ICU outcome, and walking capacity upon discharge from hospital. An additional SHAP explanatory algorithm was implemented to further explore the characteristics that led a particular patient to a specific prediction. The developed models had a strong accuracy of prediction rate of 85.9%, 75.8% and 93.3% respectively but with a low prognostic value, as the prediction is not made in time. The gait prediction model is an original finding that can set the goals of rehabilitation and would be a reference point for further research

Evolving classification of intensive care patients from event data

Objective: This work aims at predicting the patient discharge outcome on each hospitalization day by introducing a new paradigm—evolving classification of event data streams. Most classification algorithms implicitly assume the values of all predictive features to be available at the time of making the prediction. This assumption does not necessarily hold in the evolving classification setting (such as intensive care patient monitoring), where we may be interested in classifying the monitored entities as early as possible, based on the attributes initially available to the classifier, and then keep refining our classification model at each time step (e.g., on daily basis) with the arrival of additional attributes. Materials and methods: An oblivious read-once decision-tree algorithm, called information network (IN), is extended to deal with evolving classification. The new algorithm, named incremental information network (IIN), restricts the order of selected features by the temporal order of feature arrival. The IIN algorithm is compared to six other evolving classification approaches on an 8-year dataset of adult patients admitted to two Intensive Care Units (ICUs) in the United Kingdom. Results: Retrospective study of 3452 episodes of adult patients (≥ 16 years of age) admitted to the ICUs of Guy’s and St. Thomas’ hospitals in London between 2002 and 2009. Random partition (66:34) into a development (training) set n = 2287 and validation set n = 1165. Episode-related time steps: Day 0—time of ICU admission, Day x—end of the x-th day at ICU. The most accurate decision-tree models, based on the area under curve (AUC): Day 0: IN (AUC = 0.652), Day 1: IIN (AUC = 0.660), Day 2: J48 decision-tree algorithm (AUC = 0.678), Days 3–7: regenerative IN (AUC = 0.717–0.772). Logistic regression AUC: 0.582 (Day 0)—0.827 (Day 7). Conclusions: Our experimental results have not identified a single optimal approach for evolving classification of ICU episodes. On Days 0 and 1, the IIN algorithm has produced the simplest and the most accurate models, which incorporate the temporal order of feature arrival. However, starting with Day 2, regenerative approaches have reached better performance in terms of predictive accuracy