Fernando Rua

Pervasive and Intelligent Decision Support in Intensive Medicine – The Complete Picture

In the Intensive Care Units (ICU) it is notorious the high number of data sources available. This situation brings more complexity to the way of how a professional makes a decision based on information provided by those data sources. Normally, the decisions are based on empirical knowledge and common sense. Often, they don’t make use of the information provided by the ICU data sources, due to the difficulty in understanding them. To overcome these constraints an integrated and pervasive system called INTCare has been deployed. This paper is focused in presenting the system architecture and the knowledge obtained by each one of the decision modules: Patient Vital Signs, Critical Events, ICU Medical Scores and Ensemble Data Mining. This system is able to make hourly predictions in terms of organ failure and outcome. High values of sensitivity where reached, e.g. 97.95% for the cardiovascular system, 99.77% for the outcome. In addition, the system is prepared for tracking patients’ critical events and for evaluating medical scores automatically and in real-time.

Pervasive Intelligent Decision Support System – Technology Acceptance in Intensive Care Units

Intensive Care Units are considered a critical environment where the decision needs to be carefully taken. The real-time recognition of the condition of the patient is important to drive the decision process efficiently. In order to help the decision process, a Pervasive Intelligent Decision Support System (PIDSS) was developed. To provide a better comprehension of the acceptance of the PIDSS it is very important to assess how the users accept the system at level of usability and their importance in the Decision Making Process. This assessment was made using the four constructs proposed by the Technology Acceptance Methodology and a questionnaire-based approach guided by the Delphi Methodology. The results obtained so far show that although the users are satisfied with the offered information recognizing its importance, they demand for a faster system.

Intelligent data acquisition and scoring system for intensive medicine

In a critical area as is Intensive Medicine, the existence of systems to support the clinical decision is mandatory. These systems should ensure a set of data to evaluate medical scores like is SAPS, SOFA and GLASGOW. The value of these scores gives the doctors the ability to understand the real condition of the patient and provides a mean to improve their decisions in order to choose the best therapy for the patient. Unfortunately, almost all of the required data to obtain these scores are recorded on paper and rarely are stored electronically. Doctors recognize this as an important limitation in the Intensive Care Units. This paper presents an intelligent system to obtain the data, calculate the scores and disseminate the results in an online, automatic, continuous and pervasive way. The major features of the system are detailed and discussed. A preliminary assessment of the system is also provided.

Preventing patient Cardiac Arrhythmias by using data mining techniques

Cardiac Arrhythmia (CA) is very dangerous and can significantly undermine patient condition. New tools are fundamental to forecast and to prevent possible critical situations. In order to help clinicians acting proactively, predictive data mining real-time models were induced using online-learning. As input variables were considered those acquired at the patient admission and complementary variables (vital signs, laboratory results, therapeutics) hourly collected. The results are very motivating; sensitivity near to 95% was obtained when using Support Vector Machines. The approach explored in this work reveals to be an interesting contribution to the healthcare in terms of predicting CA and a good direction to be further explored.

Real-Time Decision Support Using Data Mining to Predict Blood Pressure Critical Events in Intensive Medicine Patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95 %.

Predicting Plateau Pressure in Intensive Medicine for Ventilated Patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm H 2 O) in a real environment and using real data. The present study explored and assessed the possibility of predicting the Plateau pressure class with high accuracies. The dataset used only contained data provided by the ventilators. The best models are able to predict the Plateau Pressure with an accuracy ranging from 95.52% to 98.71%.

Implementing a Pervasive Real-Time Intelligent System for Tracking Critical Events with Intensive Care Patients

Nowadays, it is increasingly important to utilize intelligent systems to support the decision making process DMP in challenging areas such as Intensive Medicine. In Intensive Care Units ICU, some of the biggest challenges relate both to the number and the different types of available data sources. Even though in such a setting the values for some variables are easy to collect, data collection is still performed manually in particular instances. In order to improve the DMP in ICU, a Pervasive Intelligent Decision Support System, called INTCare was deployed in the ICU of Centro Hospitalar do Porto in Portugal. This system altered the way information is collected and presented. Moreover, the tracking system deployed as a specific module of INTCare-Electronic Nursing Record ENR is made accessible anywhere and anytime. The system allows for the calculation of the critical events regarding five variables that are typically monitored in an ICU. Specifically, the INTCare tracking system characterizes a grid that shows the events by type and duration, empowers a warning system to alert the doctors and promotes intuitive graphics that allow care providers to follow the patient care journey. User acceptance was measured through a questionnaire designed in accordance with the Technology Acceptance Model TAM and results of implementing the INTCare tracking system, and its interface are reported.

Categorize Readmitted Patients in Intensive Medicine by Means of Clustering Data Mining

This work has been supported by FCT – Fundacao para a Ciencia e Tecnologia in the scope of the project: UID/CEC/00319/2013. The authors would like to thank FCT for the financial support through the contract PTDC/EEI-SII/1302/2012 (INTCare II).

Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine

Optimal treatments for patients with microbiological problems depend significantly on the ability of the attending physicians to predict sepsis level. A set of Data Mining (DM) models has been developed using forecasting techniques and classification models to aid decision making by physicians about the appropriate, and most effective, therapeutic plan to adopt in specific situations. A combination of Decision Trees, Support Vector Machines and Naive Bayes classifier were being used to generate the DM models. Confusion Matrix, including associated metrics, and Cross-validation were used to evaluate the models. Associated metrics used to identify the most relevant measures to predict sepsis level and treatment procedures include the analysis of the total error rate, sensitivity, specificity, and accuracy measures. The data used in DM models were collected at the Intensive Care Unit of the Centro Hospitalar do Porto, in Oporto, Portugal. Encapsulated within a supervised learning context, classification models were applied to predict sepsis level and direct the therapeutic plan for patients with sepsis. This work concludes that it was possible to predict sepsis level (2nd and 3rd) with great accuracy (accuracy: 100%), but not for the therapeutic plan (best accuracy level: 62.8%).

Pervasive Patient Timeline for Intensive Care Units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.