The resulting corpus is the first of its kind made available for de-identification research. All annotated private health information were replaced with realistic surrogates automatically and then read over and corrected manually. The resulting annotations were used both to de-identify the data and to set the gold standard for the de-identification track of the 2014 i2b2/UTHealth shared task. The average token-based F1 measure for the annotators compared to the gold standard was 0.927. This corpus was de-identified under a broad interpretation of the HIPAA guidelines using double-annotation followed by arbitration, rounds of sanity checking, and proof reading.
For this track, we de-identified a set of 1304 longitudinal medical records describing 296 patients. If you change the paramter rfade from 0 to 1, you can include the effect of fading.īy changing the value of Eb/N0 (variable ebn0), you can obtain the graph that shows the relationship between Eb/N0 and BER and that can been seen in the figures of the book.The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on the de-identification of longitudinal medical records. And, simulation result is stored in the file (BER.dat) defined with (1)-(y). Where first number 3 is Eb/No, second number 4492 is the number of errors, third number 200000 is the number of data, and fourth number 2.246000e-002 is BER. (3) Then, you can see the following simulation result on your command window. (y) Output file name to store the simulation results (w) Flat fading or not (0-Normal, 1-Flat) (o) Do you include the Rayleigh fading or not ? (0-No, 1-Yes)
(i) Code sequence (1-M-sequence, 2-Gold sequence, 3-Orthogonal Gold sequecne) Īnd, the function goldseq(m1,m2,N) outputs N one-chip shifted Gold-sequences.įirst of all, we set simulation parameters in "dscdma.m".
The function mseq(X,Y,Z) outputs an M-sequence of the stage number X, the position of feedback taps Y, and the initial value of registers Z.Īnd, the function mseq(X,Y,Z,N) outputs N one-chip shifted M-sequences.įirst of all, you must prepare preferred pair of M-sequences.įor example, you type the following commands, and generate M-sequences m1, m2.Īs a result, you can get a Gold-sequence. > crosscorr(,)Īs a result, the following cross-correlation value is obtained. Computers in biomedicine (at the fundamental level) play the role of data collection, storage, and use. If you would like to calculate of sequences and, you can type the following command. Kaiser Report on Rising Costs of Healthcare Week 4 Biomedical data: what is/how it is communicated Shortliffe Chapter 2-2.1 What are Medical Data Gathering data and interpretation of the data is the central to the health care process. If you would like to calculate of a sequence, you can type the following commandĪs a result, the following auto-correlation value is obtained. If you copy all of files to /matlabR12/work/chapter5, you only type the following command. Next, you can go to the directory that have all of programs in this section by using change directory (cd) command. Then, you start to run MATLAB and you can see the following command prompt in the command window. If you would like to try to use the above programs by using MATLAB.įirst of all, please copy all of files to your created adequate directory. The relationship between file name and the number of program written in the book is shown in as follows. In this directory, we can find the seventeen files. A practical example of using artificial intelligence to improve computer-based detection of myocardial infarction and left ventricular hypertrophy in the 12-lead ECG. % to We try to do our best to answer your questions. % If you have any bugs and questions in our simulation programs, please e-mail