Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Resume:

1.-Healthcare is undergoing a data-driven transformation, enabling novel software-based interventions to improve care quality and reduce costs.

2.-Policy changes in 2008-2010 incentivized healthcare systems to digitize patient data, making diverse datasets available for analysis.

3.-Parkinson's disease affects millions, costing billions annually, and treatment focuses on symptom management as the disease progresses.

4.-Measuring Parkinson's severity is challenging, relying on subjective assessments by neurologists during infrequent clinic visits.

5.-The team developed an Android app using phone sensors to collect active test data on Parkinson's symptoms from patients at home.

6.-Obtaining clinical ratings to map sensor data to severity is expensive, so they used semi-supervised learning with comparison pairs instead.

7.-An objective function was optimized to learn a severity score from sensor data that is concordant with clinician severity rankings.

8.-The mobile Parkinson's disease score (mPDS) showed high correlation with standard clinical instruments in a prospective study.

9.-mPDS enables frequent, objective measurement of Parkinson's symptoms and progression at home, which was not previously possible.

10.-This approach of augmenting clinical capability with machine learning on device data is applicable to many other diseases.

11.-Achieving clinical impact required deeply understanding the medical domain to innovate on problem formulation and data collection.

12.-Using semi-supervised learning and exploiting sensor data enabled learning clinically meaningful scores with limited supervision.

13.-Early diagnosis aims to recognize diseases earlier than current diagnostic guidelines based on visible signs and symptoms.

14.-Early recognition could enable prevention in some cases, and machine learning on patient data may make this possible.

15.-Mortality risk prediction in hospitals tries to use initial patient measurements to forecast death risk during admission.

16.-Training ML models on different hospital datasets can produce conflicting risk scores for the same patient.

17.-This problem arises because risk is affected by unknown interventions that occur between prediction time and outcome.

18.-ML averages over these unseen factors, causing model predictions to be sensitive to dataset-specific intervention patterns.

19.-Counterfactual reasoning is proposed, explicitly modeling risk under specific intervention regimes to control for these hidden factors.

20.-A counterfactual Gaussian process model is developed to estimate risk trajectories under different intervention regimes from time series.

21.-On simulated data, the counterfactual model produces stable risk estimates across intervention regimes while a standard model's estimates vary.

22.-Controlling for intervention regime when forecasting from temporal data is important for decision support applications beyond healthcare.

23.-Developing robust ML systems for healthcare requires an iterative process of model building, diagnostics, problem reformulation, and actionability assessment.

24.-This approach has been applied to early prediction of sepsis, a life-threatening condition, to enable early treatment.

25.-Further challenges include framing the right problems, robustness to unexpected scenarios, expensive gold-standard data for validation, calibrated uncertainty estimates, and human-machine collaboration.

26.-Healthcare offers many impactful application areas for machine learning as novel data sources become available.

27.-Survival analysis accounting for censoring is a more natural formulation than binary classification for the mortality forecasting problem.

28.-Adding constraints in the Parkinson's model to enforce expected progression over disease stages is possible but sensitivity is a concern.

29.-Deep learning for healthcare raises concerns about generalization to out-of-distribution patients and the adequacy of uncertainty quantification methods.

30.-In the speaker's experience, problem formulation, bias adjustment, uncertainty estimation, and weak supervision have been higher priority than complex models.

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