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To improve the life of autistic patients by helping the
therapist understand more systematically & more
immediately what is the effect of therapy.
To predict on the fly when a patient will behave
appropriately or not.
6.
Collection of patient’s observable behavior
Classification of observed behaviors
Using the sequence of observed behavior to
determine the underlying mental state.
If we have a sequence of behavioral pattern (say
A=>B=>C=>D) what is the next most probable
activity that patient will perform. This will help in
preventing any unwanted situation beforehand.
7.
Problems we wish to address:
1. To estimate the effectiveness of the treatment from the observations on the patient.
2. Given a pattern of behavioral symptoms, how to predict the upcoming changes in the patient’s behavior.
3. How to detect anomalies in the patient’s behavior.
8.
Estimating the effectiveness of the therapy:
Three sequences:
The medication event sequence.
Patient’s feedback event sequence.
Recorded patient’s observed behavior sequence.
Medication event sequence is a controller input that is to be manipulated by patient’s feedback sequence. Patient’s feedback sequence it is not readily available (Hidden). But we have the recorded observations on the patient’s behavior which are dependent on the hidden patient feedback sequence.
9.
Visualizing the problem as a set of hidden and observed events. Hidden Markov Models (HMM) can be a solution. E.g. deducing weather from a piece of seaweed.
If it is “soggy” means wet weather
If it is “dry” means sunny weather
If it is “intermediate” – can’t say (probably sunny or
rainy)
Second useful clue can be the known condition of
weather on previous day plus present (observed)
seaweed state provide a better forecast.
10.
Three canonical problems associated with HMM:
Given the parameters of the model, how can we compute the probability of a particular output sequence?
Given the parameters of the model, how can we determine the most likely sequence of hidden states that could have generated a given output sequence?
Given an output sequence or a set of such sequences, find the most likely set of state transition and output probabilities.
Problem 2 is more related to our case. Viterbi algorithm
can be used to solve this problem.
11.
Prediction of upcoming behavioral change:
Any activity dependent on previous activity/activities.
Use HMM to predict the next hidden mental state
transition (e.g. Observed A=>B=>C what is the probability of next activity to be D, i.e. P(D/A,B,C)).
First order HMM may not be appropriate (we have to allow the next state to be dependant on more than one previous state).
Use of Conditional random field (CRF) as an
alternative to n th order HMM.
12.
Anomaly detection on behavioral patterns:
Simplest way is to manually classify the set of
normal and abnormal behaviors.
Expert classify the behaviors into set of normal and
abnormal behaviors.
Fingerprinting is a viable technique for the same.
Fingerprinting techniques involve two basic operations:
Alignment of the sequence data, where the sequences
are aligned to maximum overlap.
Computation of similarity scores.
13.
Data mining tools may not be self sufficient
Effectiveness depends heavily on how well we collect
behavioral data
Can help in revealing patterns and relationships but
not the significance of these patterns
This is a theoretical paper so the accuracy can not be
analyzed due to lack of experimental data
Validation of these concepts with experimental data