Disease spectrum classification

1. A system, comprising:

(a) a processor; and

(b) a non-transitory computer readable medium tangibly encoded with software comprising a plurality of machine learning algorithms together with instructions configured to cause the processor to:

i) receive, via transmission over a network from a server, data related to a specimen taken from an individual, the data indicating the specimen according to a time period, the data received over the network being encrypted during the transmission;

ii) consolidate the plurality of machine learning algorithms into an ensemble of machine learning algorithms;

iii) provide the data as input to the ensemble of machine learning algorithms;

iv) generate, via execution of the ensemble of machine learning algorithms, a classification of the individual relative to a plurality of related classifications by:

a. analyzing, via execution of the ensemble of machine learning algorithms, the data provided as input, the analysis comprising identifying at least one trait of the individual identified from the data via the ensemble of machine learning algorithms performing feature selection;

b. determining, based on the analysis via the ensemble of machine learning algorithms, at least one biomarker associated with the individual, the at least one biomarker corresponding to the at least one trait;

c. analyzing, via further execution of the ensemble of machine learning algorithms, the at least one biomarker, the analysis of the at least one biomarker corresponding to an automated feature selection process based on a set of gene ontology tags;

d. determining, based on the analysis of the at least one biomarker via the ensemble of machine learning algorithms, a likelihood of a disease over the time period; and

e. generating the classification in accordance with information related to the likelihood determined via an output of the ensemble of machine learning algorithms;

v) analyze, via the ensemble of machine learning algorithms, the generated classification;

vi) generate, based on the analysis of the generated classification, a displayable evaluation report, the evaluation report comprising functionality for an intuitive visualization of the classification according to the time period, the evaluation report further comprising information related to a treatment for at least one condition associated with the classification; and

vii) cause display, on a display of a device, of the evaluation report thereby providing the functionality for the intuitive visualization of the classification, the intuitive visualization providing functionality for tracking and monitoring the individual as the treatment progresses and updating the display of the evaluation report to visibly display an impact of the treatment on the at least one condition, wherein the impact is determined based on further analysis of the generated classification of the identified biomarkers via the ensemble of machine learning algorithms.

2. The system of claim 1 , wherein the classification comprises multiple sclerosis, amyotrophic lateral sclerosis, systemic lupus erythematosus, fibromyalgia, gastrointestinal reflux disease, or any combination thereof.

3. The system of claim 1 , wherein the ensemble comprises at least three machine learning algorithms.

4. The system of claim 1 , wherein the ensemble of machine learning algorithms comprises a Generalized Linear algorithm, a Random Forests algorithm, a Partial Least Squares algorithm, and Extreme Gradient Boosting algorithm, a Support Vector Machines with Linear Basis Function Kernel algorithm, a Support Vector Machines with Radial Basis Function Kernel, and a Neural Networks algorithm.

5. The system of claim 1 , wherein each machine learning algorithm of the ensemble of machine learning algorithms produces an output that is averaged by the software.

6. The system of claim 1 , wherein each machine learning algorithm of the ensemble of machine learning algorithms produces an output and wherein at least one output is an input for at least one of the machine learning algorithms.

7. The system of claim 1 , wherein at least one machine learning algorithm is trained using data relating to specimens from other individuals.

8. The system of claim 1 , wherein the specimen comprises a biological sample.

9. The system of claim 1 , wherein the specimen comprises at least one of a sputum sample, a urine sample, a blood sample, a cerebrospinal fluid sample, a stool sample, a hair sample, and a biopsy.

10. The system of claim 1 , wherein the data relates to a metabolite, a protein, a nucleic acid, or any combination thereof.

11. The system of claim 10 , wherein the metabolite comprises at least one of oleamide, creatine, and 4-methyl-2-oxopentanoate.

12. The system of claim 1 , wherein the instructions are further configured to cause the processor to receive a parameter related to the individual and wherein the ensemble machine learning algorithms use the parameter together with the data to generate the classification of the individual relative to the plurality of related classifications.

13. The system of claim 12 , wherein the parameter comprises at least one of an age, a gender, a race, a weight, a body mass index (BMI), a height, a waist size, a blood pressure, a heart rate, and a temperature.

14. The system of claim 1 , wherein the plurality of related classifications comprise a spectrum of severity of a single disease, a spectrum of prognoses of a single disease, or a spectrum of related diseases.

15. The system of claim 14 , wherein the spectrum of related diseases comprise a plurality of neurological diseases that share at least one common feature.

16. A computer implemented method comprising:

(a) receiving, by a device, data relating to a specimen taken from an individual, the data indicating the specimen according to a time period;

(b) consolidating, by the device, a plurality of machine learning algorithms into an ensemble of machine learning algorithms;

(c) providing, by the device, the data as input to the ensemble of machine learning algorithms;

(d) generating, by the device executing the ensemble of machine learning algorithms, a classification of the individual relative to a plurality of related classifications by:

a. analyzing, via execution of the ensemble of machine learning algorithms, the data provided as input, the analysis comprising identifying at least one trait of the individual identified from the data via the ensemble of machine learning algorithms performing feature selection;

b. determining, based on the analysis via the ensemble of machine learning algorithms, at least one biomarker associated with the individual, the at least one biomarker corresponding to the at least one trait;

c. analyzing, via further execution of the ensemble of machine learning algorithms, the at least one biomarker, the analysis of the at least one biomarker corresponding to an automated feature selection process based on a set of gene ontology tags;

d. determining, based on the analysis of the at least one biomarker via the ensemble of machine learning algorithms, a likelihood of a disease over the time period; and

e. generating the classification in accordance with information related to the likelihood determined via an output of the ensemble of machine learning algorithms;

(e) analyzing, by the device via the ensemble of machine learning algorithms, the generated classification;

(f) generating, by the device, based on the analysis of the generated classification, a displayable evaluation report, the evaluation report comprising functionality for an intuitive visualization of the classification according to the time period, the evaluation report further comprising information related to a treatment for at least one condition associated with the classification; and

(g) causing display, on a display associated with the device, of the evaluation report thereby providing the functionality for the intuitive visualization of the classification, the intuitive visualization providing functionality for tracking and monitoring the individual as the treatment progresses and updating the display of the evaluation report to visibly display an impact of the treatment on the at least one condition, wherein the impact is determined based on further analysis of the generated classification of the identified biomarkers via the ensemble of machine learning algorithms.

17. A system comprising:

(a) a processor; and

(b) a non-transitory computer readable medium tangibly encoded with software comprising a plurality of machine learning algorithms together with instructions configured to cause the processor to:

i) receive data related to a specimen taken from an individual, the data indicating the specimen according to a time period;

ii) consolidate the plurality of machine learning algorithms into an ensemble of machine learning algorithms;

iii) provide the data as input to the ensemble of machine learning algorithms;

iv) generate, via execution of the ensemble of machine learning algorithms, an assessment of one or more traits of the individual according to the time period by:

a. analyzing, via execution of the ensemble of machine learning algorithms, the data provided as input, the analysis comprising identifying the one or more traits of the individual identified from the data via the ensemble of machine learning algorithms performing feature selection;

b. determining, based on the analysis via the ensemble of machine learning algorithms, one or more biomarker associated with the individual, the at least one biomarker corresponding to the one or more trait;

c. analyzing, via further execution of the ensemble of machine learning algorithms, the at least one biomarker, the analysis of the at least one biomarker corresponding to an automated feature selection process based on a set of gene ontology tags;

d. determining, based on the analysis of the at least one biomarker via the ensemble of machine learning algorithms, a likelihood of a disease over the time period; and

e. generating the assessment in accordance with information related to the likelihood determined via an output of the ensemble of machine learning algorithms;

v) analyze, via the ensemble of machine learning algorithms, the assessment;

vi) generate, based on the analysis of the assessment, a displayable evaluation report, the evaluation report comprising functionality for an intuitive visualization of the assessment according to the time period, the evaluation report further comprising information related to a treatment for at least one condition associated with the assessment and

vii) cause display, on a display of a device, of the evaluation report thereby providing the functionality for the intuitive visualization of the assessment, the intuitive visualization providing functionality for tracking and monitoring the individual as the treatment progresses and updating the display of the evaluation report to visibly display an impact of the treatment on the at least one condition, wherein the impact is determined based on further analysis of the generated assessment of the identified biomarkers via the ensemble of machine learning algorithms.

18. The system of claim 17 , wherein the assessment comprises at least one trait selected from a category that is personal characteristics, general health, mental health, health behaviors, interventions, organ systems, environmental, and conditions.