606 research outputs found
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Counterfactuals operationalised through algorithmic recourse have become a
powerful tool to make artificial intelligence systems explainable.
Conceptually, given an individual classified as y -- the factual -- we seek
actions such that their prediction becomes the desired class y' -- the
counterfactual. This process offers algorithmic recourse that is (1) easy to
customise and interpret, and (2) directly aligned with the goals of each
individual. However, the properties of a "good" counterfactual are still
largely debated; it remains an open challenge to effectively locate a
counterfactual along with its corresponding recourse. Some strategies use
gradient-driven methods, but these offer no guarantees on the feasibility of
the recourse and are open to adversarial attacks on carefully created
manifolds. This can lead to unfairness and lack of robustness. Other methods
are data-driven, which mostly addresses the feasibility problem at the expense
of privacy, security and secrecy as they require access to the entire training
data set. Here, we introduce LocalFACE, a model-agnostic technique that
composes feasible and actionable counterfactual explanations using
locally-acquired information at each step of the algorithmic recourse. Our
explainer preserves the privacy of users by only leveraging data that it
specifically requires to construct actionable algorithmic recourse, and
protects the model by offering transparency solely in the regions deemed
necessary for the intervention.Comment: 7 pages, 5 figures, 3 appendix page
Efficient Decision Support Systems
This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers
Literature-Augmented Clinical Outcome Prediction
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach
for clinical outcome prediction that retrieves patient-specific medical
literature and incorporates it into predictive models. Based on each individual
patient's clinical notes, we train language models (LMs) to find relevant
papers and fuse them with information from notes to predict outcomes such as
in-hospital mortality. We develop methods to retrieve literature based on
noisy, information-dense patient notes, and to augment existing outcome
prediction models with retrieved papers in a manner that maximizes predictive
accuracy. Our approach boosts predictive performance on three important
clinical tasks in comparison to strong recent LM baselines, increasing F1 by up
to 5 points and precision@Top-K by a large margin of over 25%.Comment: To appear in Findings of NAACL 2022. Code available at:
https://github.com/allenai/BEE
Analysis and classification of the breathing pattern in patients on weaning trial process
La estimación del momento óptimo de retirar la ventilación asistida de un paciente en cuidado intensivo sigue
siendo fundamental en la práctica clínica. En este trabajo se estudia el patrón respiratorio a partir de la señal de flujo respiratorio de
pacientes en proceso de extubación teniendo en cuenta las siguientes etapas: caracterización de la señal a partir de la identificación
de los ciclos respiratorios, análisis del patrón respiratorio a partir del modelado matemático de las series, y clasificación del mismo
con el objetivo de identificar patrones de pacientes con posible éxito en el proceso. Se analizaron 153 pacientes clasificados en
los grupos éxito, fracaso y reintubados, de acuerdo con el resultado de la prueba de extubación de tubo en T. Se seleccionaron las
series temporales de tiempo de espiración, tiempo de inspiración, duración del ciclo respiratorio e índice de respiración superficial
dado que presentaron diferencias significativas en los parámetros de valor medio, orden del modelo, primer coeficiente y error
final de predicción. Con ellas se obtuvo una exactitud de clasificación del 86% (sensibilidad 0,86 – especificidad 0,84) utilizando
un clasificador tipo discrimante lineal. Se analizaron otros clasificadores como regresión logística y máquinas de soporte vectorial.Estimating the optimal time to remove the ventilatory support from a patient in intensive care remains essential in
clinical practice. In this work we study the breathing pattern from the respiratory flow signal in the process of weaning considering
the following stages: characterization of the signal from the identification of respiratory cycles, respiratory pattern analysis from
mathematical modeling of the resulting series, and classification in order to identify patterns of patients with possible success
in the process. We analyzed 153 patients classified into three groups: success, failure and reintubated, according to results of
T-tube test. The time series for breathing duration, inspiratory time, expiratory time, and shallow breathing index that resulted in
significant differences in the mean, model order, first coefficient and final error of prediction were selected. With them we obtained
a classification accuracy of 86% (sensitivity 0.84 - specificity 0.86) using a linear classifier discriminate type. Other classifications
were analyzed, such as logistic regression and support vector machines
Advanced analyses of physiological signals and their role in Neonatal Intensive Care
Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity
A long short-temory relation network for real-time prediction of patient-specific ventilator parameters
Accurate prediction of patient-specific ventilator parameters is crucial for optimizing patient-ventilator interaction. Current approaches encounter difficulties in concurrently observing long-term, time-series dependencies and capturing complex, significant features that influence the ventilator treatment process, thereby hindering the achievement of accurate prediction of ventilator parameters. To address these challenges, we propose a novel approach called the long short-term memory relation network (LSTMRnet). Our approach uses a long, short-term memory bank to store rich information and an important feature selection step to extract relevant features related to respiratory parameters. This information is obtained from the prior knowledge of the follow up model. We also concatenate the embeddings of both information types to maintain the joint learning of spatio-temporal features. Our LSTMRnet effectively preserves both time-series and complex spatial-critical feature information, enabling an accurate prediction of ventilator parameters. We extensively validate our approach using the publicly available medical information mart for intensive care (MIMIC-III) dataset and achieve superior results, which can be potentially utilized for ventilator treatment (i.e., sleep apnea-hypopnea syndrome ventilator treatment and intensive care units ventilator treatment
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