Envisioning Insight-Driven Learning Based on Thick Data Analytics With Focus on Healthcare
Bibliographic record
Abstract
Detecting and analyzing patient insights from social media enables healthcare givers to better understand what patients want and also to identify their pain points. Healthcare institutions cannot neglect the need to monitor and analyze popular social media outlets such as Twitter and Facebook. To have a study success, a healthcare giver needs to be able to engage with their patients and adapt to their preferences effectively. However, data-driven decision-making is no longer enough, as the best-in-class organizations struggle to realize tangible benefits from their data-driven analytics investments. Relying on simplistic textual analytics that use big data technologies to learn consumer/patient insights is no longer sufficient as most of these analytics utilize sort of bag-of-words counting algorithms. The majority of projects utilizing big data analytics have failed due to the obsession with metrics at the expense of capturing the customer's perspective data, as well as the failure in turning consumer insights into actions. Most of the consumer insights can be captured with qualitative research methods that work with small, even statistically insignificant, sample sizes. Employing qualitative analytics provide some kind of actionable intelligence which acquires understanding to broad questions about the consumer needs in tandem with analytical power. Generating insight, on one hand, requires sound techniques to measure consumers' engagement more precisely and offers depth analytics to the consumer data story. On the other hand, turning relevant insights into actions requires incorporating actionable intelligence across the business by verify hypotheses based on qualitative findings by using web analytics to see if these axioms apply to a large number of customers. The first component of our visionary approach is dedicated to identifying the relationships between constituents of the healthcare pain points as echoed by the social media conversation in terms of sociographic network where the elements composing these conversations are described as nodes and their interactions as links. In this part, conversation groups of nodes that are heavily connected will be identified representing what we call conversation communities. By identifying these conversation communities several consumer hidden insights can be inferred from using techniques such as visualizing conversation graphs relevant to given pain point, conversation learning from question answering, conversations summaries, conversation timelines, conversation anomalies and other conversation pattern learning techniques. These techniques will identify and learn the patient insights without forgetting from the context of conversation communities, are tagged as "thick data analytics". Additionally machine learning methods can be used as assistive techniques to learn from the identified thick data and build models around identified thick data. With the use of transfer learning we also can fine tune these models with the arrival of new conversations. The author is currently experimenting with these seven insights driven learning methods described in this paper with massive geo-located Twitter data to infer the quality of care related to the current COVID-19 outbreak.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".