Modified k-mean clustering method of HMM states for initialization of Baum-Welch training algorithm

International audienceHidden Markov models are widely used for recognition algorithms (speech, writing, gesture, ...). In this paper, a classical set of models is considered: state space of hid- den variable is discrete and observation probabilities are modeled as Gaussian distributions. The models parame- ters are generally estimated with training sequences and the Baum-Welch algorithm, i.e. an expectation maxi- mization algorithm. However this kind of algorithm is well known to be sensitive to its initialization point. The problem of this initialization point choice is addressed in this paper: a model with a very large number of states which describe training sequences with accuracy is first constructed. The number of states is then reduced using a k-mean algorithm on the state. This algorithm is com- pared to other methods based on a k-mean algorithm on the data with numerical simulations

Developing Clinical Decision Support Systems for Sepsis Prediction Using Temporal and Non-Temporal Machine Learning Methods

In healthcare, diagnostic errors represent the biggest challenge to synthesize accurate treatments. In the United States, patient deaths due to misdiagnoses are estimated at 40,000 to 80,000 per year. It was also found that 30% of the annual healthcare spending was consumed on unnecessary services and other inefficiencies. The diagnostic errors could be reduced, and public health can be improved by applying machine learning and artificial intelligence in healthcare problems. This dissertation is an attempt to formulate clinical decision support systems and to develop new algorithms to reduce clinical errors.This dissertation aims at developing clinical decision support systems to diagnose sepsis in the early stages. The key feature of our work is that we captured the dynamics among body organs using Bayesian networks. The richness of the proposed model is measured not only by achieving high accuracy but also by utilizing fewer lab results.To further improve the accuracy of the clinical decision support system, we utilize longitudinal data to develop a mortality progression model. This part of the dissertation proposes a hidden Markov model (HMM) framework to model the mortality progression. In comparison to existing approaches, the proposed framework leverages the longitudinal data available in the electronic health records (EHR).In addition, this dissertation proposes an initialization procedure to train the parameters of HMM efficiently. The current HMM learning algorithms are sensitive to initialization. The proposed method computes an initial set of parameters by relaxing the time dependency in sequential time series data and incorporating the multinomial logistic regression.Finally, this dissertation compares the prognostic accuracy of two popularly used early sepsis diagnostic criteria: Systemic Inflammatory Response Syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA). Using statistical and machine learning methods, we found that qSOFA is a better diagnostic criteria than SIRS. These findings will guide healthcare providers in selecting the best bedside diagnostic criteria

Spontaneous speech recognition using HMMs

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2003.Includes bibliographical references (leaf 63).This thesis describes a speech recognition system that was built to support spontaneous speech understanding. The system is composed of (1) a front end acoustic analyzer which computes Mel-frequency cepstral coefficients, (2) acoustic models of context-dependent phonemes (triphones), (3) a back-off bigram statistical language model, and (4) a beam search decoder based on the Viterbi algorithm. The contextdependent acoustic models resulted in 67.9% phoneme recognition accuracy on the standard TIMIT speech database. Spontaneous speech was collected using a "Wizard of Oz" simulation of a simple spatial manipulation game. Naive subjects were instructed to manipulate blocks on a computer screen in order to solve a series of geometric puzzles using only spoken commands. A hidden human operator performed actions in response to each spoken command. The speech from thirteen subjects formed the corpus for the speech recognition results reported here. Using a task-specific bigram statistical language model and context-dependent acoustic models, the system achieved a word recognition accuracy of 67.6%. The recognizer operated using a vocabulary of 523 words. The recognition had a word perplexity of 36.by Benjamin W. Yoder.M.Eng