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<h2 class="hd hd-2 unit-title">Introduction</h2>
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<p>This section will focus on common types of medical data and the importance of collaborations between caregivers and data analysts. You will learn common terminology associated with relational databases used for these collaborations and gain a deeper understanding of the importance of reproducibility in this kind of research. Finally, you'll find in this section several exercises that will give you some experience querying a medical database.</p>
<p>We are all aware of how important data is for research. As a result, it is increasingly important to learn and exercise robust data management techniques so that studies can be carried out efficiently and reliably. Investment into best practices at the start of the study, especially with respect to the software used to process and analyze data, is likely to result in significant savings further down the line in term of time and effort.</p>
<p>While the benefits of certain techniques such as version control, testing frameworks and reproducible workflows are widely known, they are not ubiquitously adopted in the academic community. In this chapter we will discuss the key issues that should be considered when working with medical data as well as some approaches that can be used to make studies collaborative and reproducible.</p>
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<h3>Learning Objectives</h3>
<p>The topics tackled in this section are applicable to all data science but are particularly pertinent when tackling medical data. Topics include:</p>
<ul>
<li>What kind of Medical Data can be usually found in databases and how should it be interpreted and handled?</li>
<li>What are sources of bias in Medical Data and how can we account for them?</li>
<li>What should we take note of when trying to collaborate with professionals of other fields of science?</li>
<li>How can we ensure that our study is reproducible? Why is reproducibility important?</li>
<li>Whats is a Relational Database?</li>
<li>How can I query medical databases?</li>
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<h3>Credits</h3>
<p>Andre D. Silva, Tom Pollard, Franck Dernoncourt, Samuel Finlayson and Adrian Velasquez.</p>
<p>Videos: The first video in this unit is presented by <span style="color: #313131; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;">Kiri Wagstaff, subsequent videos are presented by Alistair Johnson.</span></p>
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<h2 class="hd hd-2 unit-title">Data Types in Medical Datasets</h2>
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<p>When analyzing a medical database you will encounter several different types of data. Different types will require different processing approaches and have different limitations. The most common data types are explored in the table below.</p>
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<th>Data Type</th>
<th>Description</th>
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<td>Billing Data</td>
<td>Biling data usually consists of the codes that hospitals and caregivers use to file claims with insurance providers. The two most common coding systems are the International Classification of Disease (ICD), which is maintained by the World Health Organization, and the Current Procedural Terminology (CPT) maintained by the American Medical Association. These codes were created in order to become a standardized way for medical classification and reporting.</td>
<td>Both the ICD and CPT codes are a flag system that identifies the procedures that the patient received. The codes are used mainly for billing and so may not accurately reflect the care the patient received in the issue. The CPT code key is licensed by the American Medical Association and must be bought prior to use in a study.</td>
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<td>Charted Physiological Data</td>
<td>Charted physiological data includes all measurements done at the bedside, such as heart rate, blood pressure and respiratory rate. This type of data often has a high sampling rate and so is automatically introduced into the database, though in some cases it is input manually by a healthcare professional.</td>
<td> When the data is input manually it may contain mistakes and missing values. The sampling rate is variable depending on the type of event being measured and the state of the patient, sicker patients usually have a higher frequency of data collection. Data is often archived at a lower sampling rate than it is collected using averaging algorithms that are proprietary and undisclosed. The way this data is stored and collected can vary widely between medical centers. </td>
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<td>Notes and Reports</td>
<td>Notes are usually written up by nurses or medical doctors to record patient progress, summarize a patient stay upon discharge, or provide findings from imaging studies. It may also contain images such as X-rays, computerized axial tomography (CAT/CT) scans, echo cardiograms, magnetic resonance imaging and others. These notes may contain a wealth of extra data that can enrich any study due to the more free form nature of the data type.</td>
<td>Written notes are difficult to work with when compared to numerical measurements, usually Natural Language Processing (NLP) techniques are used. As these notes were written by a person, they are prone to human error (typos, missing data, bias, etc). Different medical centers often have different note structures.</td>
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<td>Laboratory Data</td>
<td>Laboratory data shares many similarities with charted physiological data. It contains the results of laboratory tests requested by the physician and is usually quantitative.</td>
<td>Laboratory tests are conducted by humans and so they suffer from the same problems as charted physiological data (missing values, errors, different methodologies, etc). Furthermore since these analysis for the most part must be requested, not all patients will have data on all laboratory tests. The timestamp associated with the result may indicate the time the test was requested or the time the test was reported.</td>
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<td>Medication Data</td>
<td>Medication data contains information on what medication the doctor ordered for each patient. This will include a dosage and, if the medication is infused, a delivery rate. This data type contains information related to a variety of drugs including IV drugs or tablets and so different units may be used.</td>
<td>Although these records mean the medication was ordered, they do not guarantee that the patient took the medication. This data is usually input by hospital staff, so errors and missing data are possible. The timestamp associated with each entry may refer to the time the doctor ordered the medication or to the time when the drug was administered.</td>
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<p>Though the above data types are distinct, they sometimes appear together in the same dataset. As a result, it is important to learn how to both identify and handle them correctly. As you proceed through your analysis, remember the limitations of these types of data and take appropriate measures to account for them in your final results.</p>
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<h2 class="hd hd-2 unit-title">Collaborations and Data Source Bias</h2>
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<h3>Collaborations</h3>
<p>One of the greatest challenges of working with medical data is understanding the context in which the data is collected. Collaboration between hospital staff and research analysts is crucial to success. However, it can be easy for members of either group to misunderstand one another or to have different expectations of how the dataset can be used. </p>
<p>It is critical that both groups learn how to communicate with each other and manage expectations of the work ahead. One of the best ways to learn both of these important skills is to develop a mutual understanding of the field in which the work will be developed. Never underestimate the importance of being able to "speak the same language" as the medical professionals involved or learn about the limitations of data analysis. These skills can help tremendously in any collaboration and ensure a smooth conclusion to the work being developed.</p>
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<h3 class="hd hd-2">Collaborations (Recap of 1.01)</h3>
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<h3>Data Biases</h3>
<p>As mentioned earlier, it is critical to understand the limitations of data, especially with respect to potential bias. In a hospital setting, these biases may be tied to factors such as the patient populations served, the local practices of caregivers, or to the type of services provided. For example:</p>
<ul>
<li>Academic centers often see more complicated patients, and some hospitals may tend to serve patients of a specific ethnic background or socioeconomic status</li>
<li>Follow-up visits may be less common at referral centers, so they may be less likely to detect long-term complications</li>
<li>Research centers may be more likely to place patients on experimental drugs not generally used in practice</li>
</ul>
<p style="font-size: 16px;">Although there is very little that can be done to completely eliminate biases from your data, it is important to be aware of the possible sources of bias to mitigate them and to correctly interpret the final results of your study. </p>
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<h2 class="hd hd-2 unit-title">Reproducibility</h2>
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<p>There is currently a reproducibility crisis in the scientific community: some researchers are unable to replicate peer-reviewed papers. Several factors can be identified as the origin for this issue including the emphasis on the results of research over the methodology utilized or the 'publish or perish' culture that researchers are subject to. It is the responsibility of the researcher to make sure their studies are as reproducible as possible.</p>
<p>How can we make reproducibility an integral part of our studies?</p>
<p>First, the data source and code should be accessible to anyone trying to reproduce our work, ideally as part of a publication. The data science community can then more easily detect and correct the inevitable errors in all studies.</p>
<p>Second, you should never modify source data. Editing the raw data destroys the chain of reproducibility. Instead all changes should be made through code, this way all steps that the study took to process the data can be reproduced. All code and data should be well documented and the terms of reuse should be clear. Usually researchers provide a plain text "README" file that provides an introduction to the analysis package as well as a "LICENSE" file with the terms of reuse. Another way this can be accomplished that is quickly gaining popularity in the research community are tools like Jupyter Notebook, Sweave and Knitr that allow the interweaving of code and text and so produce clearly documented and reproducible studies.</p>
<p>Version control systems, like Git, also help to create more reproducible studies. Git can track the changes made to the code used in the study over time by contributor, this way the development process becomes more transparent and the logs can be used to more easily find and fix bugs in the code.</p>
<p>Git also facilitates collaboration by promoting simultaneous work on a code by allowing code distribution between several contributors. Integrating your work with Git hosting services such as Github provides a simple mechanism for backing up content, helping to reduce the risk of data loss, and also provides tools for tracking issues and tasks in a collaboration.</p>
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<h2 class="hd hd-2 unit-title">Exercise 1</h2>
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<input type="radio" name="input_8cf7e3366be041589cf357bacdc9c5a4_2_1" id="input_8cf7e3366be041589cf357bacdc9c5a4_2_1_choice_1" class="field-input input-radio" value="choice_1"/><label id="8cf7e3366be041589cf357bacdc9c5a4_2_1-choice_1-label" for="input_8cf7e3366be041589cf357bacdc9c5a4_2_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_8cf7e3366be041589cf357bacdc9c5a4_2_1"> Separate the scientists by their areas of expertise and restrict communication to the absolute necessary to allow the work to continue.
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<input type="radio" name="input_8cf7e3366be041589cf357bacdc9c5a4_2_1" id="input_8cf7e3366be041589cf357bacdc9c5a4_2_1_choice_2" class="field-input input-radio" value="choice_2"/><label id="8cf7e3366be041589cf357bacdc9c5a4_2_1-choice_2-label" for="input_8cf7e3366be041589cf357bacdc9c5a4_2_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_8cf7e3366be041589cf357bacdc9c5a4_2_1"> Become an expert on the field you wish to work on and conduct the entire project yourself.
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Question 2
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<legend id="e1a0cfedfc8f4623b6ff34498b2be714_2_1-legend" class="response-fieldset-legend field-group-hd">As discussed in the previous section of this chapter, reproducibility should be an integral part of any data science project. Which of these contribute to a reproducible project?</legend>
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<h2 class="hd hd-2 unit-title">Relational Databases</h2>
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<h3>Relational Databases</h3>
<p>The medical data you will find and use in your studies will most likely be organized in databases. There are several styles of databases in use today, but probably the most widely implemented is the “relational database”. Relational databases can be thought of as a collection of tables which are linked together by shared keys. Organizing data across tables can help to utilize storage more efficiently, maintain data integrity and enable faster analysis.</p>
<p>The model that defines the structure and relationships of the tables is known as a “database schema”. Giving a simple example of a hospital database with four tables, it might comprise of:</p>
<ul>
<li>Table 1, a list of all patients</li>
<li>Table 2, a log of hospital admissions</li>
<li>Table 3, a list of vital sign measurements</li>
<li>Table 4, a dictionary of vital sign codes and associated label</li>
</ul>
<p>These tables will be linked through primary and foreign keys. A primary key is a unique identifier within a table. For example, in the patients table, where all patients will be listed once alongside their personal info, the value subject_id is used to identify each of them, therefore subject_id will be the primary key in the patients table. A foreign key, on the other hand, is a table variable that in turn points to a primary key in another table. For example, subject_id in the admissions table is a foreign key, because it references the primary key in the patients table, this value is in the admissions table as a way to identify the patient in the patients table to whom that admission refers to.</p>
<p>Extracting data from a database is known as “querying” the database. The programming language commonly used to create a query is known as “Structured Query Language” or SQL. While the syntax of SQL is straightforward, queries are at times challenging to construct as a result of the conceptual reasoning required to join data across multiple tables. Later in this section of the course we will explore some of the SQL syntax used to query databases.</p>
<p>There are many different relational database systems in regular use. Some of these systems such as Oracle Database and Microsoft SQL Server are proprietary and may have licensing costs. Other systems such as PostgreSQL and MySQL are open source and free to install. The general principle behind the databases is the same, but it is helpful to be aware that programming syntax varies slightly between systems.</p>
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<h2 class="hd hd-2 unit-title">MIMIC-III Structure</h2>
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<h3>MIMIC-III Structure</h3>
<p>We will now take all that we have learned so far in this chapter of our course and learn from a practical example, the MIMIC-III database.</p>
<p>The MIMIC-III database is a medical database generated from the deidentified medical records of over forty thousand patients that stayed in intensive care units of the Beth Israel Deaconess Medical Center from 2001 to 2012. The MIMIC-III dataset contains information on patient demographics, vital signs measurements, laboratory test results, procedures, medications, caregiver notes, imaging reports and mortality.</p>
<p>It is freely available to researchers worldwide and contains information on a large and diverse patient population.</p>
<p>In the table below we will explore the structure of the MIMIC-III database, including the tables included in it, the data in each table and the foreign and primary keys if present.</p>
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<table style="width:100%">
<tr>
<th>Table Name</th>
<th>Description</th>
<th>Primary Key</th>
<th>Foreign Key</th>
</tr>
<tr>
<td>ADMISSIONS</td>
<td>The ADMISSIONS table gives information regarding a patient’s admission to the hospital. Since each unique hospital visit for a patient is assigned a unique HADM_ID, the ADMISSIONS table can be considered as a definition table for HADM_ID. Information available includes timing information for admission and discharge, demographic information, the source of the admission, and so on.</td>
<td>HADM_ID</td>
<td>SUBJECT_ID</td>
</tr>
<tr>
<td>CALLOUT</td>
<td>The CALLOUT table provides information about ICU discharge planning. When a patient is deemed ready to leave the ICU, they are “called out”. This process involves: (i) a care provider registering that the patient is ready to leave the ICU and detailing any specialized precautions required, (ii) a coordinator acknowledging the patient requires a bed outside the ward, (iii) a variable period of time in order to coordinate the transfer, and finally (iv) an outcome: either the patient is called out (discharged) or the call out event is canceled. This table provides information for all of the above.</td>
<td></td>
<td>SUBJECT_ID, HADM_ID</td>
</tr>
<tr>
<td>CAREGIVERS</td>
<td>This table provides information regarding care givers. For example, it would define if a care giver is a research nurse (RN), medical doctor (MD), and so on.</td>
<td>CGID</td>
<td></td>
</tr>
<tr>
<td>CHARTEVENTS</td>
<td>CHARTEVENTS contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The electronic chart displays patients’ routine vital signs and any additional information relevant to their care: ventilator settings, laboratory values, code status, mental status, and so on. As a result, the bulk of information about a patient’s stay is contained in CHARTEVENTS. Furthermore, even though laboratory values are captured elsewhere (LABEVENTS), they are frequently repeated within CHARTEVENTS. This occurs because it is desirable to display the laboratory values on the patient’s electronic chart, and so the values are copied from the database storing laboratory values to the database storing the CHARTEVENTS.</td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICUSTAY_ID, ITEMID, CGID</td>
</tr>
<tr>
<td>CPTEVENTS</td>
<td>The CPTEVENTS table contains a list of which current procedural terminology codes were billed for which patients. This can be useful for determining if certain procedures have been performed (e.g. ventilation).</td>
<td></td>
<td>SUBJECT_ID, HADM_ID, CPT_CD, CPT_NUMBER, CPT_SUFFIX</td>
</tr>
<tr>
<td>D_CPT</td>
<td>This table gives some high level information regarding current procedural terminology (CPT) codes. Unfortunately, detailed information for individual codes is unavailable. Unlike all other definition tables, D_CPT does not have a one to one mapping with the corresponding CPT_CD in CPTEVENTS, rather each row of D_CPT maps to a range of CPT_CD.</td>
<td></td>
<td>SECTIONRANGE, SUBSECTIONRANGE</td>
</tr>
<tr>
<td>D_ICD_DIAGNOSES</td>
<td>This table defines International Classification of Diseases Version 9 (ICD-9) codes for diagnoses. These codes are assigned at the end of the patient’s stay and are used by the hospital to bill for care provided.</td>
<td>ICD9_CODE</td>
<td></td>
</tr>
<tr>
<td>D_ICD_PROCEDURES</td>
<td>This table defines International Classification of Diseases Version 9 (ICD-9) codes for procedures. These codes are assigned at the end of the patient’s stay and are used by the hospital to bill for care provided. They can further be used to identify if certain procedures have been performed (e.g. surgery).</td>
<td>ICD9_CODE</td>
<td></td>
</tr>
<tr>
<td>D_ITEMS</td>
<td>The D_ITEMS table defines ITEMID, which represents measurements in the database. Measurements of the same type (e.g. heart rate) will have the same ITEMID (e.g. 211). The ITEMID column is an alternate primary key to this table: it is unique to each row. Note that the D_ITEMS table is sourced from two ICU databases: Metavision and CareVue. Each system had its own set of ITEMID to identify concepts. As a result, there are multiple ITEMID which correspond to the same concept.</td>
<td>ITEMID</td>
<td></td>
</tr>
<tr>
<td>D_LABITEMS</td>
<td>D_LABITEMS contains definitions for all ITEMID associated with lab measurements in the MIMIC database. All data in LABEVENTS link to the D_LABITEMS table. Each unique LABEL in the hospital database was assigned an ITEMID in this table, and the use of this ITEMID facilitates efficient storage and querying of the data. Note that lab items are kept separate while most definitions are contained in the D_ITEMS table.</td>
<td>ITEMID</td>
<td></td>
</tr>
<tr>
<td>DATETIMEEVENTS</td>
<td>DATETIMEEVENTS contains all date measurements about a patient in the ICU. For example, the date of last dialysis would be in the DATETIMEEVENTS table, but the systolic blood pressure would not be in this table.</td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICUSTAY_ID, ITEMID, CGID</td>
</tr>
<tr>
<td>DIAGNOSES_ICD</td>
<td>This table contains the ICD9 codes that were attributed to each patient during their stay in the ICU for billing purposes.</td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICD9_CODE</td>
</tr>
<tr>
<td>DRGCODES</td>
<td>This table contains diagnosis related groups (DRG) codes for patients. HCFA-DRG and MS-DRG codes have multiple descriptions as they have changed over time. Sometimes these descriptions are similar, but sometimes they are completely different diagnoses. Users will need to select rows using both the code and the description.</td>
<td>DRG_CODE</td>
<td>SUBJECT_ID, HADM_ID</td>
</tr>
<tr>
<td>ICUSTAYS</td>
<td>This table defines each ICUSTAY_ID in the database, i.e. defines a single ICU stay. It is derived from the TRANSFERS table. Specifically, it groups the TRANSFERS table based on ICUSTAY_ID, and excludes rows where no ICUSTAY_ID is present.</td>
<td>ICUSTAY_ID</td>
<td>SUBJECT_ID, HADM_ID</td>
</tr>
<tr>
<td>INPUTEVENTS_CV</td>
<td>This table has input data for patients under the careview system.</td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICUSTAY_ID, ITEMID, CGID</td>
</tr>
<tr>
<td>INPUTEVENTS_MV</td>
<td>This table has input data for patients under the metavision system.</td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICUSTAY_ID, ITEMID, CGID</td>
</tr>
<tr>
<td>LABEVENTS</td>
<td>The LABEVENTS data contains information regarding laboratory based measurements. The process for acquiring a lab measurement is as follows: first, a member of the clinical staff acquires a fluid from a site in the patient’s body (e.g. blood from an arterial line, urine from a catheter, etc). Next, the fluid is bar coded to associate it with the patient and timestamped to record the time of the fluid acquisition. The lab analyses the data and returns a result within 4-12 hours.</td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ITEMID</td>
</tr>
<tr>
<td>MICROBIOLOGYEVENTS</td>
<td>This table contains microbiology information, including tests performed and sensitivities.</td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ITEMID</td>
</tr>
<tr>
<td>NOTEEVENTS</td>
<td>This table contains all notes for patients. </td>
<td></td>
<td>SUBJECT_ID, HADM_ID, CGID</td>
</tr>
<tr>
<td>OUTPUTEVENTS</td>
<td>This table contains output data for patients. </td>
<td></td>
<td>SUBJECT_ID, HADM_ID, CGID, ICUSTAY_ID, ITEMID</td>
</tr>
<tr>
<td>PATIENTS</td>
<td>This table contains all charted data for all patients. </td>
<td>SUBJECT_ID</td>
<td></td>
</tr>
<tr>
<td>PRESCRIPTIONS</td>
<td>This table contains medication related order entries, i.e. prescriptions. </td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICUSTAY_ID</td>
</tr>
<tr>
<td>PROCEDUREEVENTS_MV</td>
<td>This table contains information on procedures performed on patients under the metavision system. </td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICUSTAY_ID, ITEMID</td>
</tr>
<tr>
<td>PROCEDURES_ICD</td>
<td>This table contains the ICD9 codes for the procedures performed on the patients for billing purposes. </td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICD9_CODE</td>
</tr>
<tr>
<td>SERVICES</td>
<td>The services table describes the service that a patient was admitted under. While a patient can be physicially located at a given ICU type (say MICU), they are not necessarily being cared for by the team which staffs the MICU. This can happen due to a number of reasons, including bed shortage. The SERVICES table should be used if interested in identifying the type of service a patient is receiving in the hospital. For example, if interested in identifying surgical patients, the recommended method is searching for patients admitted under a surgical service. </td>
<td></td>
<td>SUBJECT_ID, HADM_ID</td>
</tr>
<tr>
<td>TRANFERS</td>
<td>This table keeps track of the patient's physical location throughout their hospital stay. </td>
<td></td>
<td>SUBJECT_ID, HADM_ID, ICUSTAY_ID</td>
</tr>
</table>
</div>
</div>
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<h3>MIMIC-III and eICU</h3>
<p>After having understood the concept of relational databases, we will continue to explore the databases most related to the work of this course, MIMIC-III database and the eICU database.</p>
<p>As explained before, the MIMIC-III database was created from the deidentified health-related data of over forty thousand patients who stayed in critical care units in the Beth Israel Medical Center from 2001 to 2012. </p>
<p>The eICU database, on the other hand, is comprised of data obtained for the Philips telehealth program. This program delivers need-to-know patient data to caregivers empowering them to better care for their needs. This database covers data from several critical care units throughout the United States for patients that were admitted from 2014 to 2015.</p>
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<h2 class="hd hd-2 unit-title">SQL Basics</h2>
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<h3>SQL Basics</h3>
<p>To effectively query the databases of medical information that you will encounter to complete your projects, you will need to learn about SQL. Websites such as <a href="https://www.sqlteaching.com/">https://www.sqlteaching.com/</a> provide a good interactive environment to pick up the basics of SQL. </p>
<p>SQL is the workhorse for you to retrieve the necessary data for your analysis. After you write queries to retrieve the information you want, you can then either write the table to a .csv file, or use various packages to connect to the database directly with your analysis tool of choice (often, R or python). </p>
<p>A few of the simplest and essential commands in SQL are as follows:</p>
<ul>
<li>SELECT [what do you want returned, list columns here separated by commas. writing 'SELECT *' will return all columns available in the table]</li>
<li>FROM [what is the name of the table where you want the data to come from]</li>
<li>WHERE [are there any restrictions in the data you want returned? e.g. before a certain time or that meet certain criteria]</li>
<li>GROUP BY [would you like to aggregate the data? note that if you choose to aggregate the data you will need to make sure that you apply proper statements to variables in your select statement]</li>
<li>HAVING [this is essentially the same as where after you've used a GROUP BY statement, one use case could be looking for patients that stayed for multiple visits after you aggregate the number of hospital admissions a patient has recorded in the admissions table]</li>
<li>ORDER BY [this is to arrange your data according to a certain column, say from greatest to least or alphabetically. By default the ordering is done in ascending order, so you can add the word DESC after the variable(s) listed]. </li>
</ul>
<p>A simple SQL query could take the following form:</p>
<pre class="example" style="box-sizing: border-box; overflow: auto; font-family: Monaco, Menlo, Consolas, 'Courier New', monospace; border-radius: 4px; padding: 9.5px; margin-top: 0px; font-size: 14.4px; line-height: 1.42857; word-break: break-word; color: #333333; background-color: #f5f5f5; border: 1px solid #cccccc;">SELECT column1, column2 FROM table1, table2 WHERE column2='value';</pre>
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<p><strong>Advanced Commands: The Join Command</strong></p>
<p>The JOIN command is a way to gather data stored in multiple places. The command will select all data from both tables and aggregate it into a new table according to a certain criteria depending on the type of join used.</p>
<p><strong>INNER JOIN</strong></p>
<p>This JOIN command places in the new table only the data that both table A and B contain.</p>
<p><img height="145" width="200" src="/assets/courseware/v1/ac4e75e16439e204179aca9f3e87b091/asset-v1:MITx+HST.953x+3T2020+type@asset+block/img_innerjoin.gif" alt="Credit: w3schools.com" /></p>
<p><em>SELECT table1.column1, table2.column2</em></p>
<p><em>FROM table1</em></p>
<p><em>INNER JOIN table2 ON table1.column1 = table2.column1;</em></p>
<p></p>
<p><strong>LEFT JOIN</strong></p>
<p>The LEFT JOIN command returns all rows of the table on the left side of the join and the matching rows from the table to the right of the join.</p>
<p><img height="145" width="200" src="/assets/courseware/v1/35125df03a8eb791aa1284473190b983/asset-v1:MITx+HST.953x+3T2020+type@asset+block/img_leftjoin.gif" alt="Credit: w3schools.com" /></p>
<p style="font-size: 16px;"><em>SELECT table1.column1, table2.column2</em></p>
<p style="font-size: 16px;"><em>FROM table1</em></p>
<p style="font-size: 16px;"><em>LEFT JOIN table2 ON table1.column1 = table2.column1;</em></p>
<p style="font-size: 16px;"></p>
<p style="font-size: 16px;"><strong>RIGHT JOIN</strong></p>
<p style="font-size: 16px;">The RIGHT JOIN command returns all rows of the table on the right side of the join and the matching rows from the table to the left of the join.</p>
<p style="font-size: 16px;"><img height="145" width="200" src="/assets/courseware/v1/02187c9d9300093234b96c129286bea4/asset-v1:MITx+HST.953x+3T2020+type@asset+block/img_rightjoin.gif" alt="Credits: w3schools.com" /></p>
<p style="font-size: 16px;"><em>SELECT table1.column1, table2.column2</em></p>
<p style="font-size: 16px;"><em>FROM table1</em></p>
<p style="font-size: 16px;"><em>RIGHT JOIN table2 ON table1.column1 = table2.column1;</em></p>
<p style="font-size: 16px;"></p>
<p style="font-size: 16px;"><strong>FULL OUTER JOIN</strong></p>
<p style="font-size: 16px;">The FULL OUTER JOIN command returns all rows in both tables including the ones that match in both.</p>
<p style="font-size: 16px;"><img height="145" width="200" src="/assets/courseware/v1/78c764bdbdafd434a395c4dac01dc57a/asset-v1:MITx+HST.953x+3T2020+type@asset+block/img_fulljoin.gif" alt="Credit: w3schools.com" /></p>
<p style="font-size: 16px;"><em>SELECT table1.column1, table2.column2</em></p>
<p style="font-size: 16px;"><em>FROM table1</em></p>
<p style="font-size: 16px;"><em>FULL OUTER JOIN table2 ON table1.column1 = table2.column1;</em></p>
<p style="font-size: 16px;">Credit: All images obtained from w3schools.com</p>
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<h2 class="hd hd-2 unit-title">Exercises</h2>
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SQL Question 1
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<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 1" role="group"><p>What is the function of the following SQL code snippet:</p>
<p>SELECT item_id FROM d_items WHERE label="Heart Rate";</p>
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<input type="radio" name="input_48cb30bbd7ed4d80a4e21cfef0ac0d0a_2_1" id="input_48cb30bbd7ed4d80a4e21cfef0ac0d0a_2_1_choice_2" class="field-input input-radio" value="choice_2"/><label id="48cb30bbd7ed4d80a4e21cfef0ac0d0a_2_1-choice_2-label" for="input_48cb30bbd7ed4d80a4e21cfef0ac0d0a_2_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_48cb30bbd7ed4d80a4e21cfef0ac0d0a_2_1"> It deletes all item_id values from the d_items table whose label is, exactly, Heart Rate and then displays all other item_id values.
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<h2 class="hd hd-2 unit-title">Conclusion</h2>
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<h3></h3>
<ul>
<li>Not all medical data found in a database has the same characteristics even if it represents the same concept. Pay close attention to the data you are extracting and it's original purpose. Remember that both the type of data and the place where it comes from introduces its own bias to your study. Take precautions accordingly and analyze your results with that knowledge.</li>
<li>Collaborating with professionals of different areas of science than our own can be daunting. The language of both sides may be difficult to understand and the needs and expectations of each may not always align. Take time to understand the nuances of the language of every area involved. You do not need to be an expert in all areas but you do need to know how to understand each other and how to manage expectations.</li>
<li>No matter what results you obtain in your study, they are as good as nothing if they cannot be reproduced by other groups. Please take steps so that your studies can always be reproduced. Never directly edit the raw data, all modifications should be made through coding. Provide both the raw data as well as all code needed to achieve your processed data status, this way other groups may arrive at the same state you did and may also analyze your path there and provide constructive criticism when necessary.</li>
<li>Remember the concepts of relational databases. Although difficult to understand and sometimes confusing to navigate, relational databases help you organize your data and quickly find exactly what you are looking for without having to wade through hundreds of lines of meaningless noise.</li>
<li>Finally, stay up to date with SQL. This language is used in most databases worldwide to explore them and extract the information required for analysis. It is very likely that you will have to use it throughout your work so being familiar with it will be an asset to your professional journey.</li>
</ul>
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