00:00: This video is a quick walkthrough of object, analytics and causal Discovery using 00:04: an example. It is intended as an introduction and familiarization 00:08: with the terms and Concepts. It should also serve as motivation, as 00:12: you will see what unique things can be done. 00:16: You can't identify causal factors that aren't present in your data. 00:19: So comprehensive data is essential for causal discovery. 00:23: Take Rich patient data as an example. 00:25: The root object is the patient. 00:27: And in typical projects that are up to 50 related objects and recursive 00:32: subjects that provide a full 360-degree view of the patient. 00:37: In this demo, we will use a simplified model. 00:39: The root is the patient or person. 00:44: Let's look at the gender attribute, you'll see that there are about 70,000 00:48: male and female patients. The 00:53: age distribution shows that there are 1200 patients over 100 years 00:57: old. If you select these patients you see 01:01: that most of them are female. 01:10: But that's just a warm-up. Let's take a look at the sub objects. 01:14: Direct sub objects or Master data diagnoses prescriptions 01:19: and Hospital cases. A hospital case is itself. 01:22: A complex object with subjects such as procedures charge 01:26: items and hospital diagnosis. 01:29: We want to understand what causes breast cancer. 01:31: So we dive into the diagnosis sub-object. 01:38: Breast cancer can be found in the ICD catalog in the neoplasm section. 01:50: We select those patients. The 02:06: flag at the root shows us. How many patients we have with the target disease? In 02:13: total, there are 3300 patients with breast cancer. 02:18: Among women. The percentage is 4.5%. 02:31: Can painkillers cause breast cancer. Let's find out. 02:35: We open the sub-object prescription. 02:37: The painkillers can be found in the ATC catalog in the nervous system 02:41: section. When we select those, we see that among patients who 02:45: use painkillers, we find an increased percentage of 7.6% 02:50: who also had breast cancer. Let's get a little more detail 02:54: than count the number of painkillers for each patient. 02:57: We can initiate such an aggregation along the hierarchies of the object Tree 03:01: by dragging, the prescriptions up and dropping them on the patient object. 03:06: As a result, we find a new dimension that characterizes each patient. 03:10: The amount of painkillers used. 03:12: We open this on the canvas, 57,000 of the female 03:16: patients. Never use painkillers. But there are a few, which had even more than 100 03:21: painkiller prescriptions to see how the prevalence of breast cancer changes. 03:25: With the number of painkillers we drag and drop the attribute into the window. 03:30: Switch to graph View and convert the data to percentages. 03:33: Now, we can see that the more painkillers are used the higher, the proportion 03:37: of patients with breast cancer. 03:40: Does this mean that painkillers cause breast cancer? Absolutely not. 03:44: There are several reasons why we can't infer causation from this Association. 03:49: First, we didn't control for Time. 03:51: Painkillers may have been prescribed after the breast cancer diagnosis. 03:56: Second. We're comparing apples to oranges patients. 03:59: With lots of painkillers on the right side of the graph. 04:02: Tend to be older while those with few or none are younger and breast cancer. 04:06: Also becomes more common with age H is an important confounding Factor. 04:11: We can see this by selecting a specific age group, This 04:15: ensures that all patients in the chart are of a comparable age. 04:19: When we look at the graph, it becomes flat showing that within a given 04:23: age group, there is no correlation between painkillers and breast cancer. 04:31: So what causes breast cancer? There could be millions of potential 04:35: factors according to all the different disease codes available drugs, 04:39: existing procedures, Etc. All need to be evaluated. 04:43: Also as potential, confounding factors, let's start the search. 04:47: We again select our Target breast cancer. 04:50: Now, click on the light bulb icon, to start the causal Discovery algorithms 05:01: During the process you'll see that the algorithm is evaluating 2.3 05:06: million different factors and potential confounders simultaneously. 05:09: Not just painkillers and age. 05:12: No algorithm can prove cause and effect from observational data alone, 05:16: but the deeper and broader the search, the more relevant the hypotheses 05:20: generated. That's where our object analytic space deep search 05:24: algorithms Excel. 05:26: Here are the results. At the top is malignant neoplasms 05:30: of the ovary, which is plausible. Further factors 05:34: are benign neoplasms of the breast. 05:37: And personal history of malignant neoplasms. 05:40: Also expected, more surprising, is the factor, g03 05:44: F, progestogens and estrogens in combination related to hormones, 05:49: used for menopausal symptoms, These have recently been linked to carcinogenic 05:54: effects. The initial list contains only direct factors where 05:58: also interested in indirect factors essentially the entire cause and 06:02: effect graph. This advanced search may take longer depending on your configuration 06:07: in search steps. 06:09: Here are some sample results. The 06:14: Redbubble on the right represents the target breast cancer. 06:19: The time axis runs from left to right with factors plotted according 06:23: to their duration of influence Arrows indicate cause and 06:27: effect moving from left to right. For example, hypercholesterolemia 06:32: is on the far left indicating that it contributes indirectly to breast cancer 06:36: over a long period of time through other factors. 06:41: This is only a demonstration on a small data set. 06:44: These results need to be validated on larger data sets. 06:47: Ideally, we would like to apply these algorithms to a set of 10 06:51: million patients, covering all diagnoses prescriptions and 06:55: procedures over 10 years for a total of about 10 billion events. 07:01: By now, it should be clear that causal, Discovery requires, Rich information that 07:05: cannot be represented in a flat table. 07:08: We use holistic objects and are algorithms x-ray, these objects 07:12: from any perspective to find likely causal factors for a Target. 07:16: Causal Discovery is the Supreme part but much simpler things are also 07:20: becoming feasible based on object analytics which are hard to achieve with 07:24: other Technologies in healthcare. 07:26: The term patient Journey. Describes the need of understanding the flow of 07:30: events, across the diverse event streams Associated to a patient. 07:35: For example, we want to find out whether in which blood thinners are used after 07:39: the initial diagnosis of atrial fibrillation A common type 07:43: of heart disease and how patients switch between different products as the disease, 07:47: progresses, with just a few clicks, you can build this analysis. 07:52: Usually therapy starts at the time of diagnosis. 07:55: Most often with vitamin K antagonists, and there is rarely a 07:59: switch. In 08:04: some cases, it also starts with Clopidogrel. 08:12: And from there, we often see a switch to another product. 08:20: We don't want to go into medical details, but give you an idea of what is possible 08:24: based on object analytics, analyzing a complex object, 08:28: across subjects, in the above case therapies, relative to diagnosis 08:33: and previous Therapies. Try to do this with a relational database 08:37: and SQL. If you get this done at all, it will likely be slow. 08:42: The object Explorer implements a usability concept to do that kind 08:46: of analysis in an interactive way and the object analytics database 08:50: provides the speed for that sort of analyzes. 08:53: And there is a python interface where you can do all of this on a programmatic level. 08:58: You will learn all of this in the next steps.