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Short background:Before we moved to salesforce we had a lot of data in a description box (tags related to that contact). We used these tags to pull out lists and run reports. This data, when moving to Salesforce, now exists in a custom text box.
Current problem:When I now run a report in Salesforce and search for the tags in the text box, only a selection of all contacts with those tags started showing up. I did multiple reports and set-ups trying to figure out the problem and I believe it might be caused due to the following: a word count limit, for the report to search through the text box.
I've done a few tests and all contacts with the relevant tags listed within a 235 character count show up. All contacts with the relevant tags listed after 235 character count disappears/wont show on my reports.
Design: The authors converted medical text reports to a structured form through natural language processing. They then inductively created classifiers for medical text reports using varying degrees and types of expert knowledge and different inductive learning algorithms. The authors measured performance of the different classifiers as well as the costs to induce classifiers and acquire expert knowledge.
Results: Expert knowledge was shown to be the most significant factor affecting inductive learning performance, outweighing differences in learning algorithms. The use of expert knowledge can affect comparisons between learning algorithms. This expert knowledge may be obtained and represented separately as knowledge about the clinical task or about the data representation used. The benefit of the expert knowledge is more than that of inductive learning itself, with less cost to obtain.
A prominent example of this challenge is accessing data contained in medical text reports. Medical text reports contain substantial and essential clinical data.2,3 For example, a recent study distinguishing between planned and unplanned readmissions found that information available in structured, coded format alone was not sufficient for classifying admissions and that information in text reports significantly improved this task.4 Although narrative text reports can be stored and retrieved electronically, clinical information represented in text reports often is not available in coded form and not easily used for automated decision support, analysis of patient outcomes, or clinical research. For computer analysis of patient data to effectively include clinical information from text reports, the data must be extracted from the reports and converted to a structured, coded form.5
One approach that may be used to convert this information to structured form is classification. Medical text reports can be classified according to the clinical conditions that are described in the reports (e.g., whether the report indicates the patient has pneumonia). Classifiers can be created to detect clinical conditions indicated in narrative text and to represent these indicated conditions as standardized codes or terms.5,6,7,8,9,10,11 However, manual creation of these classifiers (often represented as expert rules) is a difficult and expensive process, requiring the coordinated effort of both medical experts and knowledge engineers.8,12 Researchers therefore have investigated the use of inductive learning algorithms to automatically generate classifiers for medical documents.11,13,14
Expert knowledge can be used in the data preparation. An important task of data preparation is to determine the subset of attributes or features that are relevant to the classification task. This is done through feature selection or feature extraction. Domain experts can select specific attributes or features that are relevant to the classification task (feature selection). Using domain knowledge for feature selection has been suggested previously as a way to enhance the performance of machine learning algorithms.16 Gaines17 showed this effect of using expert knowledge to select relevant attributes. Clark and Matwin18 showed improved performance when using domain knowledge to restrict an algorithm's search space, but they also discussed the increased cost that can arise from using this knowledge. Domain knowledge can be used also to combine multiple features together, to create a new feature or variable (feature extraction). For example, variables assigned values indicating their presence or absence in a report could be extracted to new variables indicating their presence or absence as clinical conditions for a patient. Feature extraction not only changes the representation of the data, but may also reduce the number of variables used.
The cost of classifying chest x-ray reports was determined from the original evaluation of MedLEE.5 That study reported that it took a physician about two hours to analyze 100 reports. These reports were analyzed to detect six clinical conditions, although the bulk of the time probably was spent reading the report. Therefore, we estimated the cost of manually classifying one report by one physician for one condition (represented by CASE) to be about 1 minute. The time required to write rules for seven clinical conditions has been reported as one week.12 The average cost of writing rules for one condition (RULES) is between six and 20 hours. This includes the time to specify task-specific and representation-specific knowledge as rules, and to debug/test the rules.
To determine the cost of specifying task-specific observations (TASK), we measured the time it took a physician to select relevant observations for one disease from a list. Initially, we limited the list of all possible observations to those that were more likely to be relevant to a disease using automated methods. First, a physician selected ICD-9 codes that were relevant to congestive heart failure (CHF). Using these codes, we compiled a set of 10,000 chest radiographs from New York-Presbyterian Hospital where the discharge diagnosis code of the inpatient visit associated with a report was relevant to CHF. We used a large set of reports here, rather than the 200 chest radiograph reports used above, to ensure a more comprehensive list of possible relevant observations. We processed these reports using MedLEE and compiled a list of all observations occurring in these reports. We then selected those observations occurring in at least 1% of all the reports, resulting in a list of about 200 observations. Finally, we measured the time a physician took to manually select from this list those findings that would be strongly relevant to identifying CHF in a chest x-ray report. It took between 5 and 15 minutes to determine discharge diagnoses of CHF and 5 to 15 minutes to select relevant observations from this list. Thus, we estimated that it took between 10 and 30 physician minutes to select relevant observations from NLP output.
Comparison of machine learning algorithms and feature selection methods using natural language processing (NLP) output from radiology reports. ROC = receiver operating characteristic [curve]; MC4, CN2, NB, IB, and DT are algorithms.
A characteristic of a curve that may be more interesting than the slope is where the performance of another method surpasses the best performance of the predictive method. This point indicates the real value of the domain knowledge in terms of the training set size. When one type of knowledge (task-specific or representation-specific) is used, the number of training cases needed for equivalent performance decreases by about half. When both types of knowledge are used, the number of cases is less than one tenth. 041b061a72